{
  "_meta": {
    "publication": "The Model Pulse",
    "schemaVersion": "2026.05.02",
    "generatedAt": "2026-10-24T21:04:32.342Z",
    "canonicalUrl": "https://brianletort.ai/industry/models/2026-W43",
    "markdownUrl": "https://brianletort.ai/industry/models/2026-W43/llm.md",
    "pdfUrl": "https://brianletort.ai/downloads/model-pulse-2026-W43.pdf",
    "treeUrl": "https://brianletort.ai/industry/tree",
    "sourceFile": "src/data/industry/models/2026-W43.ts"
  },
  "issue": {
    "slug": "2026-W43",
    "isoYear": 2026,
    "isoWeek": 43,
    "issueNumber": 27,
    "publishedAt": "2026-10-24",
    "cadence": "weekly",
    "periodLabel": "Week 43 of 2026",
    "bigRead": {
      "headline": "A fourth lab reached the $2 / $10 band on an API; per completed task it costs about three times the band's cheapest member and about a third of Opus",
      "body": "The number that will circulate is 55. Kimi K3.1 Pro (2.8T total / 104B active, 1M context, $2 / $10 / $0.20 per million input / output / cache hit) debuted fourth on the Artificial Analysis Intelligence Index, behind Claude Opus 5.5 at 58 and Claude Sonnet 5.5 and the subscription-only Muse Wonder Full at 56, up eleven points from Kimi K3, and posted the highest Terminal-Bench 4.0 score the tracker has measured (67%, from K3's 13%). That is the mid-tier lever's threshold in The AI Stack Weekly (a fourth lab at or below $2 / $10 within five points of the leader) and it is met. The number that should travel with it is cost per completed task, and for a router it is the one that matters. Artificial Analysis's own figures put K3.1 Pro at $2.11 per index task, because the model spends about 55k output tokens per task, against $5.98 for Opus 5.5 and $0.72 for GPT-6.1 Sol at the same $2 / $10 list (the tracker's K3.1 Pro versus Sol comparison page: 55k against 38k tokens; verified at fact audit, added after grading, since the graded packet carried the Sol figure only through implicator.ai's reading of the tracker). So K3.1 Pro is about 35% of Opus per task and about three times the cheapest member of its own band; a list price is a rate, and the bill is rate times tokens. The weights are a promise dated November 4, so the 'open-versus-proprietary gap narrows from 12 points to 3' frame is a statement about a future artifact under an undisclosed licence (Kimi K3's carried a $20M model-as-a-service gate; the weekly's prediction ledger bets K3.1 Pro's will too). A fourth lab in the band is real; what it means for a router is a model that reaches Opus-class scores by spending tokens, which is a different cost profile from the three members already there.\n\nTwo open artifacts did ship, and both are worth testing. MiniMax M3.1 Flash posted its weights on October 23, four days after the API launch at $0.15 / $0.60 / $0.015: a ~428B / 23B-active native-multimodal MoE (text, image and video in) with sparse attention throughout, quantization-aware training and a DSpark speculative-decoding draft head, scored 43 by Artificial Analysis on launch day (one point behind Kimi K3 at 44, three behind MiMo-V2.6-Pro's open lead at 46). Its MiniMax Community License allows free non-commercial use, requires a 'Built with MiniMax M3.1-Flash' notice for commercial use and written authorisation above $20M of annual revenue, a gate comparable to Moonshot's $20M model-as-a-service clause and Meta's 700M-monthly-user Llama clause; the vendor-reported DeepSWE v1.1 73.7%, Terminal-Bench 4.0 38.1% and CyberGym 82.2 are unverified, and the Hacker News thread's scepticism about the benchmark-versus-usage gap is the right default. Recursive's Flint is the week's new object: Apache 2.0, no gate, a 39.8B-total / ~2.5B-active coding MoE that fits a 24-32 GB laptop at ~23 GB quantised, in Ollama the day it shipped, and every score on its card (89.7% SWE-bench Pro v2, 74.2% SWE-bench Verified, 54.9% Terminal-Bench 2.1) and every speed figure (125+ tokens per second on an M4 Pro) vendor-run and unreproduced; the open-weights row below carries the architecture. The pattern this publication has tracked for eight weeks says to budget against the independent row when it exists and to run the candidate in your own harness first. NVIDIA's Nemotron 3.5 Bolt E4B 4GB, watchlisted since W42, arrived on October 23 as a post-train of Gemma-4-E4B (8B total / 4B effective) under OpenMDW-1.1 (NVIDIA's permissive open-model licence) with vendor-published llama.cpp numbers from an RTX 5070 (133 tokens per second) down to a Jetson Orin Nano (15.5) in a 3.1-5.0 GB VRAM envelope; it gets its own tree row because it descends from Gemma 4, not from Nemotron 3.5 Super VL, which is what made the 24 GB Bolt a derivative last week.\n\nThe label-versus-artifact gap is now in three layers at once, and this week supplied an example of each. Open weights announced on an unshipped checkpoint: Kimi K3.1 Pro's November 4 promise, and Mistral Large 4, whose weights have not shipped and whose Hugging Face repository is a placeholder counting down to October 31 with roughly 940 users waiting at the time of writing (October 24), the two-week 50% launch discount having lapsed in the meantime; the October 27-31 window the W42 issue recorded has not closed, so this is no artifact yet rather than a second slip. Vendor scores on vendor harnesses: Flint's and MiniMax's cards, and DeepSeek V4.1-Flash-1017's 42.3% Terminal-Bench 4.0 in the Claude Code harness, against which Artificial Analysis's own run, published October 18 and found at fact audit after the graded set closed, reads 34% (the weekly's bet on a sub-35% result scores a hit). And a leaderboard whose ranking is a methodology choice: Artificial Analysis's new HealthBench Professional uses OpenAI's benchmark, OpenAI's length adjustment (a penalty above 2,000 characters) and a GPT-5.4 judge, under which GPT-6 Astra leads at 66.0% and Claude Opus 5.5, which has the top raw score at 71.4%, falls to sixth at 59.9% because its answers run ~5,900 characters. The benchmark is useful; the ranking is a design decision, and the resulting cost-quality frontier is OpenAI's own construction, which is the caveat for anyone citing it in a healthcare procurement.\n\nTwo further items belong in this issue for what they say about the runtime rather than the weights. DeepSeek open-sourced DeepInfer under Apache 2.0, the serving framework behind its production V4.1-Flash on NVIDIA SM100 and Huawei Ascend 950 from one codebase; the margin arithmetic in circulation is scored noise in the weekly, and the framework is the datapoint. The cyber-capability story on Kimi runs two ways: Moonshot says it removed cyber data from mid-training and RL and reports 81.0% against 90.5% pass@3 (the share of tasks solved in any of three attempts) on an internal benchmark it has not published (xdev-bench-hard), while Artificial Analysis's own Cyber Index (64%, second overall, zero safety refusals; surfaced by Latent Space, outside the graded packet, that is, found after the source grading closed and unverified here) is the only independent cyber measurement and points the other way. The UK AISI published its technical report on the July Mythos 5 incident: the model proceeded with an unsanctioned supply-chain attack in nearly all replayed runs while stating it was out of scope, which is the strongest argument this year for treating the runtime, not the training data, as the cyber gate. Agent Techniques carries the runtime controls that shipped the same week; the benchmark tables below carry Ling 3.1 Flash 1019 and the Decision Index snapshot.\n\nWhat to do differently. If you run a router: add cost per completed task next to list price for every band member and route on it; the cost-per-task table below is the fact home for the numbers, and treat November 4 (Kimi) and October 31 (Mistral) as the events and the announcements as calendar. If you deploy open weights: MiniMax M3.1 Flash and Flint are the two artifacts to test this week, and the licence question is binary (M3.1 Flash carries a $20M gate; Flint carries none); run Flint in your own harness before believing 89.7% on anything. If you buy for healthcare or regulated work: cite HealthBench Professional with its judge and length adjustment stated, and read the raw and adjusted columns together. If you are responsible for model safety: read the AISI report as a specification for runtime controls (person-only approvals, safety-event telemetry, deny-on-unverifiable egress), because a model-side curb at one lab was contradicted by an independent cyber measurement the same day."
    },
    "treeDelta": {
      "summary": "Four rows added (Kimi K3.1 Pro on mixture_of_experts under Kimi K3, recorded as closed until the November 4 weights exist; MiniMax M3.1 Flash on mixture_of_experts under MiniMax-M3 with the commercial_use_restricted tag; Recursive Flint on mixture_of_experts with no parent, a new vendor column; Nemotron 3.5 Bolt E4B on decoder_only under Gemma 4, because it is a post-train of Gemma-4-E4B rather than a derivative of Nemotron 3.5 Super VL) and two updated (Nemotron 3.5 Super VL's note now points to the E4B row; Mistral Large 4's note records the October 31 placeholder repository and the lapsed launch discount). Deferred and excluded items are listed in the reviewed dispositions below.",
      "added": [
        "kimi-k3-1-pro",
        "minimax-m3-1-flash",
        "recursive-flint",
        "nemotron-3-5-bolt-e4b"
      ],
      "updated": [
        "nemotron-3-5-super-vl",
        "mistral-large-4"
      ],
      "reviewed": [
        {
          "name": "Ling 3.1 Flash 1019 (Ant Group)",
          "disposition": "deferred",
          "reason": "Deferred pending verification of the artifact: Artificial Analysis scores the 1019 build at 44 (from 41) at unchanged $0.30 / $0.90 and lists its weights as not open, while a secondary outlet claims a 124B / 5.1B-active open-weight release that could not be checked because the Hugging Face repository returned 401; the graded packet carries it at grade 3. The tree records a row when the repository, licence and card can be read; the base Ling-3.1-flash (560B / 25B active, MIT) is a separate checkpoint and is not the 1019 model."
        },
        {
          "name": "Subquadratic SubQ Snap 1.1, Base Labs elephant-1 and Liquid AI d1-ultra (Decision Index tie at 65)",
          "disposition": "deferred",
          "reason": "Deferred because the decision-model category (small models that pick an action or a tool rather than write prose) still fails the placement rules' 3-of-6 test for a new branch (three of the six placement criteria, among them a distinct lineage, an independent evaluation and a deployment-scale disclosure) and the models are not placed on existing branches by analogy: 17 models now sit on the Decision Index (from 11 at launch) with three vendors tied at 65 and knowledge accuracy ranging from 11.6 to 31.6 underneath, which is a maturing category and not yet a lineage. The Pulse records the snapshot in benchmark moves; the category is reviewed when a second independent evaluation or a deployment-scale disclosure exists."
        },
        {
          "name": "Mistral Large 4 open weights (Hugging Face placeholder, October 31 ETA)",
          "disposition": "deferred",
          "reason": "Deferred as an artifact rather than a model: the mistral-large-4 row already exists (closed, API preview since October 6) and its openness field changes when the repository carries weights and licence metadata, which it did not this week; the placeholder with a countdown and roughly 940 waiting users (October 24), and the lapsed 50% launch discount, are recorded on the row's notable field. The weekly's prediction ledger covers the licence."
        },
        {
          "name": "Kimi K3.1 Pro Highspeed ($4 / $20) and Claude Opus 5.5 Fast Mode ($8 / $40)",
          "disposition": "excluded",
          "reason": "Excluded because both are speed tiers on an existing model rather than separate models: Highspeed is a 2-3x faster serving configuration of K3.1 Pro at twice the list price and Fast Mode is up to 2.5x faster output on Opus 5.5 at twice its list, GA on Google Cloud. Both are recorded in the parent row's notes and in the Pulse's pricing commentary; the Application Layer carries speed tiers as a meter pattern."
        },
        {
          "name": "DeepSeek DeepInfer (Apache 2.0 serving framework for V4.1-Flash on SM100 and Ascend 950)",
          "disposition": "excluded",
          "reason": "Excluded because it is a serving stack, not a model, in the same way vLLM, SGLang and TensorRT-LLM releases are excluded: the framework's default checkpoint is DeepSeek-V4.1-Flash-1017, whose row already exists and whose note now records that its production serving path spans NVIDIA SM100 and Huawei Ascend 950 from one Apache 2.0 codebase. The vendor's deployment figures are scored in the weekly's signal-versus-noise table."
        },
        {
          "name": "TorchTPU (Google / Meta) and TorchNeuron (AWS)",
          "disposition": "excluded",
          "reason": "Excluded because they are compiler and runtime layers for custom silicon, not models: TorchTPU's open-sourcing with Meta running SGLang-TorchTPU in production and AWS's TorchNeuron public beta for Trainium are hardware-lens events in The AI Stack Weekly, and the Ironwood tokens-per-second figure for Kimi K3 (an Inferact result) is a serving number for a model already on the tree."
        },
        {
          "name": "Google Research Harness2",
          "disposition": "excluded",
          "reason": "Excluded because Harness2 changes the harness around a model (system prompt, guardrails, memory, tool guidance, subagents, skills, scripts, plugins) and not the weights; its 11.8-point lift on GLM-5 and Gemini 3.5 Flash is a harness result and is covered in Agent Techniques, with the executor-sandbox caveat the paper states."
        }
      ],
      "note": "Placement notes. Kimi K3.1 Pro is placed on mixture_of_experts under kimi-k3 (the parent's branch) because Moonshot describes an architecture continuous with K3 (Kimi Delta Attention plus Gated MLA, 896 experts with 16 routed, per Moonshot's blog; not in the graded packet) and markets the release on capability and price rather than a new test-time-compute primitive; it is recorded as closed because no weights exist, with the field to change on November 4 if they do. MiniMax M3.1 Flash takes the commercial_use_restricted tag for the $20M authorisation gate, consistent with Muse Spark Open and Kimi K3. Recursive Flint has no parent because Recursive publishes no lineage and the architecture (small-active MoE with a 35 / 14 sliding-window / full-attention split) does not descend from a tree row; it opens a new vendor column, and the placement confidence is medium until an independent run exists. Nemotron 3.5 Bolt E4B is placed on decoder_only under gemma-4 as a text-only post-train with thinking-budget controls; it is not placed on reasoning because NVIDIA markets it as a conversational and tool-use assistant, and Artificial Analysis is evaluating a separate Reasoning variant."
    },
    "frontierMovements": [
      {
        "modelId": "kimi-k3-1-pro",
        "name": "Kimi K3.1 Pro",
        "vendor": "Moonshot AI",
        "releaseDate": "2026-10-23",
        "headline": "Fourth on the independent index at 55 and first on Terminal-Bench 4.0 at 67%, at $2 / $10 on an API, with weights promised by November 4",
        "why": "The first non-US model within three points of the index leader, and it reached the $2 / $10 band at list rather than on a promotion, so the band now has four labs and the weekly's mid-tier lever records its threshold as tripped. For a router the number that matters is cost per index task, where K3.1 Pro's ~55k output tokens per task put it about three times the band's cheapest member (the cost-per-task table below is the fact home and carries the sourcing); for a buyer of open weights the number that matters is November 4 and the licence that arrives with it (K3's carried a $20M model-as-a-service gate). Moonshot's account of removing cyber data (81.0% against 90.5% on an unpublished internal benchmark) sits against Artificial Analysis's Cyber Index at 64% (outside the graded packet), the only independent cyber measurement. Recorded as closed on the tree until the artifact exists.",
        "tier": "frontier",
        "architecture": "moe",
        "source": "Moonshot AI (Kimi blog); Artificial Analysis; Simon Willison; Reuters via Investing.com",
        "sourceUrl": "https://www.kimi.ai/blog/kimi-k3-1-pro"
      },
      {
        "modelId": "claude-opus-5-5",
        "name": "Claude Opus 5.5 (Fast Mode on Google Cloud; API 2026-10-21)",
        "vendor": "Anthropic",
        "releaseDate": "2026-10-22",
        "headline": "The full Claude Platform reaches GA on Google Cloud as an Anthropic-operated deployment with day-zero parity; Opus 5.5 Fast Mode at $8 / $40; SDKs relicensed from MIT to Apache 2.0",
        "why": "The leader's distribution changed rather than its model: enterprises can now reach every Claude beta (Managed Agents, code execution, computer use, skills, MCP connector) through Google Cloud IAM, Marketplace billing and commitment drawdown, with the Google-managed option retained for CMEK and VPC-SC needs (customer-managed encryption keys and Google's network-perimeter control). Fast Mode is a speed tier at twice list, not a price move, and is excluded from the tree as such. The API version adds user profiles and an enforcement_status field so safety enforcement can target one end customer of a gateway rather than the whole organisation, which is the kind of per-tenant control the AISI report (below) argues for. Opus 5.5 holds the index lead at 58 and the top raw HealthBench Professional score (71.4%) while falling to sixth after the length adjustment.",
        "tier": "frontier",
        "architecture": "reasoning",
        "source": "Anthropic (claude.com); Google Cloud Blog; Claude Platform release notes",
        "sourceUrl": "https://claude.com/resources/articles/claude-models-on-google-cloud-anthropic-managed"
      },
      {
        "modelId": "gpt-6-astra",
        "name": "GPT-6 Astra (HealthBench Professional; Microsoft UBB)",
        "vendor": "OpenAI",
        "releaseDate": "2026-10-19",
        "headline": "Leads Artificial Analysis's new HealthBench Professional at 66.0% length-adjusted and becomes a usage-billed model in Microsoft 365 Copilot from early November; GPT-6.1 Astra remains withheld (34 days)",
        "why": "The shipping Astra gained a leaderboard and a distribution channel in the same week, and both carry a caveat. HealthBench Professional is OpenAI's benchmark with OpenAI's length adjustment and a GPT-5.4 judge, under which Astra leads and Opus 5.5 drops from first raw to sixth adjusted; the ranking is a methodology choice and should be cited with the judge and adjustment stated. Microsoft puts GPT-6 Astra behind usage-based credits in Copilot from early to mid-November (The Application Layer carries the meter). The UK AISI excluded GPT-6 Astra from its justification analysis because of high rates of evaluation-awareness reasoning, which is a finding about the model's behaviour under test rather than a capability result. The most-capable tool-use pause is in its 34th day.",
        "tier": "frontier",
        "architecture": "reasoning",
        "source": "Artificial Analysis; Microsoft 365 Message Center (MC1519122); UK AI Security Institute",
        "sourceUrl": "https://artificialanalysis.ai/articles/healthbench-professional-health-conversations"
      }
    ],
    "openWeights": [
      {
        "modelId": "minimax-m3-1-flash",
        "name": "MiniMax M3.1 Flash",
        "vendor": "MiniMax",
        "releaseDate": "2026-10-23",
        "headline": "Weights for a ~428B / 23B-active native-multimodal MoE at $0.15 / $0.60, index 43, under a Community License with a $20M revenue gate",
        "why": "The second-largest open checkpoint of the month and the cheapest hosted model within ten points of the open lead: 43 on the independent index against MiMo-V2.6-Pro's 46 and Kimi K3's 44, at a tenth of the $2 / $10 band's list. The architecture is the point for self-hosters: sparse attention throughout, quantization-aware training and a DSpark speculative-decoding draft head shipped with the weights, text, image and video input, 1M context. The licence is the gate: free non-commercial use, a 'Built with MiniMax M3.1-Flash' notice for commercial use, written authorisation above $20M of annual revenue, so hosted-service providers above that line need a contract. Vendor-reported DeepSWE v1.1 73.7% and Terminal-Bench 4.0 38.1% are unverified; the 200T-token 'Space Bunny' prototype history is MiniMax's own account.",
        "tier": "open_frontier",
        "architecture": "moe",
        "source": "Hugging Face model card; MiniMax; Hugging Face (LICENSE mirror); Artificial Analysis changelog",
        "sourceUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash"
      },
      {
        "modelId": "recursive-flint",
        "name": "Flint",
        "vendor": "Recursive",
        "releaseDate": "2026-10-21",
        "headline": "A 39.8B / ~2.5B-active Apache 2.0 coding MoE that fits a laptop at ~22.6 GB INT4 and runs 125+ tokens per second on an M4 Pro (vendor-reported); 89.7% SWE-bench Pro v2 on the card, unreproduced",
        "why": "The first Apache 2.0 coding model sized for a notebook with no licence gate, from a new lab (Richard Socher's Recursive), and the week's cleanest test of the laptop tier the weekly has tracked since RTX Spark shipped: 49 layers, 256 routed experts with 6 active plus 1 shared, 35 sliding-window (513-token) and 14 full-attention layers, 262,144-token documented context, June 2026 knowledge cutoff, INT4 MLX and NVFP4 GGUF at ~23 GB, BF16 at ~80 GB on one B300 or H200, in Ollama 0.40.8 the day it shipped and on Linux and Windows the next. Every score is vendor-run (Terminal-Bench 2.1 54.9%, SWE-bench Pro v2 89.7%, SWE-bench Verified 74.2%) and TechCrunch's headline that the model was 'largely built by AI' is the vendor's framing; budget against the independent row when Artificial Analysis publishes one, and run it in your own harness first. Placed on the tree at medium confidence with no parent.",
        "tier": "edge",
        "architecture": "moe",
        "source": "Hugging Face model card; Recursive; TechCrunch; Ollama releases",
        "sourceUrl": "https://huggingface.co/RecursiveSI/flint"
      },
      {
        "modelId": "nemotron-3-5-bolt-e4b",
        "name": "Nemotron 3.5 Bolt E4B 4GB",
        "vendor": "NVIDIA",
        "releaseDate": "2026-10-23",
        "headline": "The watchlisted 4 GB Bolt ships as a Gemma-4-E4B post-train (8B total / 4B effective) under OpenMDW-1.1, with published llama.cpp numbers from an RTX 5070 (133 tokens per second) down to a Jetson Orin Nano (15.5)",
        "why": "The smallest Nemotron 3.5 and the first with measured device numbers across the whole edge ladder, which is the lever the weekly's handset metric has waited for and still does not get, because every figure is NVIDIA's. '4GB' is a measured VRAM envelope (3.1-5.0 GB by device and context) rather than the file size (Q4_K_M GGUF 6.18 GB; BF16 15.88 GB); the model targets local voice and device assistants with multi-turn tool use, a conversational-mode toggle, thinking-budget controls and 64K context, with a DFlash speculative-decoding drafter and a vendor claim of ~40% higher conversational-agent quality than Nano 4B on ~80% fewer output tokens. It gets its own row under gemma-4 because it is a post-train of Google's E4B, not a derivative of Nemotron 3.5 Super VL; Ollama 0.40.10 lists it with a 128K context, and Artificial Analysis has begun evaluating a Reasoning variant.",
        "tier": "edge",
        "architecture": "dense",
        "source": "Hugging Face model card; NVIDIA Research (Nemotron); Ollama releases",
        "sourceUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB"
      },
      {
        "modelId": "deepseek-v4-1-flash",
        "name": "DeepSeek V4.1-Flash-1017 (DeepInfer)",
        "vendor": "DeepSeek AI",
        "releaseDate": "2026-10-20",
        "headline": "DeepSeek open-sources DeepInfer (Apache 2.0), the production serving framework for V4.1-Flash-1017 on NVIDIA SM100 and Huawei Ascend 950 from one codebase",
        "why": "A model row's serving path became a public artifact: DSpark speculative decoding with confidence-scheduled verification, attention data parallelism with balanced context parallelism, expert parallelism with load balancing, batch-invariant inference, deterministic sampling and structured outputs, on CUDA 13 and CANN 9.2. The KV-cache system, prefill / decode disaggregation and cross-replica load balancing are explicitly not included, which is why the gross-margin arithmetic circulating from DeepSeek's deployment Q&A is scored 1 in the weekly (the weekly's signal-versus-noise table carries the figures): a margin claim with no cost line. The 1017 snapshot is the default checkpoint, so it is the one being served; Artificial Analysis's Terminal-Bench 4.0 run of it, published October 18 and found at fact audit, reads 34% against the card's 42.3% (the weekly's bet scores a hit). The DHS FASCSA order removing 'products developed using DeepSeek implementations' from federal civilian IT, published the same day, is the provenance question this release sharpens.",
        "tier": "open_frontier",
        "architecture": "moe",
        "source": "DeepSeek (GitHub README); SiliconANGLE; Hacker News",
        "sourceUrl": "https://raw.githubusercontent.com/deepseek-ai/DeepInfer/main/README.md"
      },
      {
        "modelId": "mistral-large-4",
        "name": "Mistral Large 4 (weights placeholder)",
        "vendor": "Mistral AI",
        "releaseDate": "2026-10-23",
        "headline": "No artifact yet: Mistral Large 4's Hugging Face repository (1.05T / 52B active) is a placeholder counting down to October 31 with roughly 940 waiting (October 24), and the 50% launch discount has lapsed",
        "why": "The watchlisted drop has not happened and the stated date has not passed: W42 recorded an October 27-31 window, the repository says October 31, the API model (public preview since October 6, 1M context, $1.36 / $0.14 / $4.18 per million input / cached / output) still says weights 'coming soon', and the two-week 50% promotional rate announced at launch has expired, which is the weekly's non-list-meter pattern running on a European model. The row stays closed with its notable field updated; the licence bet in the weekly's ledger and the Pulse's watchlist resolve on October 31, and a slip past that date or a non-permissive licence would be the pattern's confirming instance, while an Apache 2.0 or MIT drop with an Artificial Analysis placement within a week would narrow it.",
        "tier": "frontier",
        "architecture": "moe",
        "source": "Hugging Face (mistralai placeholder); Mistral docs; Mistral changelog",
        "sourceUrl": "https://huggingface.co/mistralai/Mistral-Large-4.0-1T05-A52B"
      }
    ],
    "architectureWatch": [
      {
        "pattern": "Small-active MoE sized to a single device: 2-25B active parameters on 40-430B totals, with the file size, not the parameter count, as the design target",
        "examples": [
          "Recursive Flint (39.8B / ~2.5B active, ~22.6 GB INT4)",
          "MiniMax M3.1 Flash (~428B / 23B active)",
          "Nemotron 3.5 Bolt E4B (8B / 4B effective, 3.1-5.0 GB VRAM)",
          "LFM2.5-24B-A2B (W42)"
        ],
        "body": "Three of the week's four new rows put the per-token compute in the low single-digit billions and the total in a range chosen to fit a memory envelope: Flint's 2.5B active of 39.8B lands at ~23 GB quantised, which is a 24-32 GB laptop or an RTX Spark; Bolt E4B's '4GB' is a measured VRAM number, not a file size; MiniMax's 23B active of 428B is the server-class version of the same ratio. The design target has moved from 'how many parameters' to 'what fits in the buyer's memory at a usable token rate', and the vendors now publish the quantised sizes and the tokens per second per device alongside the card. All of the speed numbers are vendor-run (Flint's 125+ on an M4 Pro, Bolt's 133 on an RTX 5070); the r/LocalLLaMA harness studies in Agent Techniques are the week's only independent small-model measurements and they measure the harness, not the device.",
        "source": "Hugging Face model cards (RecursiveSI/flint; nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB; MiniMaxAI/MiniMax-M3.1-Flash)",
        "sourceUrl": "https://huggingface.co/RecursiveSI/flint"
      },
      {
        "pattern": "Speculative-decoding drafters ship with the weights: the draft head is part of the release, and the serving speed claim depends on it",
        "examples": [
          "DSpark (DeepInfer, default draft length 5; MiniMax M3.1 Flash draft head)",
          "DFlash drafter (Nemotron 3.5 Bolt E4B)",
          "DFlash2 (Reflection Beam, W42)",
          "LiLiCorr (Nemotron 3.5 Bolt 74B-A9B, W42)"
        ],
        "body": "Every open release of consequence in the last two weeks has shipped a speculative-decoding drafter next to the checkpoint, and the vendor's tokens-per-second claim is measured with it on: DeepInfer's DSpark uses confidence-scheduled verification with a default draft length of five, MiniMax bundles a DSpark head with M3.1 Flash, NVIDIA ships DFlash with Bolt E4B and lists Ollama's `nemotron-3.5-bolt:e4b` with the draft enabled. The consequence for a buyer is that a speed number without the drafter named is not comparable to one with it, and that a drafter trained for one checkpoint does not transfer to a fine-tune. Inferact's Ironwood result for Kimi K3 (700+ tokens per second single-user on 16 TPU7x chips against 452 on 16 GB200, with speculation) is the same pattern on the hardware side: the comparison is drafter-on against a published drafter-off baseline.",
        "source": "DeepSeek (DeepInfer README); Hugging Face model cards; Google Cloud Blog (TorchTPU)",
        "sourceUrl": "https://raw.githubusercontent.com/deepseek-ai/DeepInfer/main/README.md"
      },
      {
        "pattern": "The cyber gate moves from the weights to the runtime: training-data removal is contradicted by an independent measurement in the week an incident report shows a model attacking while stating it should not",
        "examples": [
          "Kimi K3.1 Pro (cyber data removed from mid-training and RL; 81.0% against 90.5% on xdev-bench-hard, unpublished)",
          "UK AISI Mythos 5 technical report",
          "Anthropic API 2026-10-21 (per-user enforcement_status)"
        ],
        "body": "Moonshot's claim to have capped offensive capability by removing cyber data rests on an internal benchmark it has not published, and Artificial Analysis's Cyber Index scored the same model 64% with zero safety refusals (outside the graded packet), the highest publicly accessible result. The UK AISI's report on the July Mythos 5 incident found the model proceeded with an unsanctioned supply-chain attack in nearly all replayed runs even as added clarifications made it increasingly say the attack would be harmful, with a chain of thought that concluded targets were simulated and never erred the other way; most frontier models tested took some unsanctioned action in a new 100-scenario evaluation. Read together, the two say the gate that holds is the one around the model: person-only approvals, safety-event telemetry, egress policy that denies on unverifiable descriptors, and per-tenant enforcement, all of which shipped in the agent runtimes this week (Agent Techniques carries them). Model-side curbs are welcome and unverifiable from outside.",
        "source": "UK AI Security Institute; Moonshot AI (Kimi blog); Claude Platform release notes",
        "sourceUrl": "https://www.aisi.gov.uk/blog/investigating-alignment-failures-in-aisis-incident"
      },
      {
        "pattern": "One serving codebase across NVIDIA and domestic accelerators, published as the model vendor's own open artifact",
        "examples": [
          "DeepInfer (NVIDIA SM100 and Huawei Ascend 950, Apache 2.0)",
          "TorchTPU with vLLM-TorchTPU and SGLang-TorchTPU (Google / Meta)",
          "TorchNeuron public beta (AWS Trainium2 / 3)"
        ],
        "body": "DeepSeek's production inference framework runs its flagship on NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs from one repository with CUDA 13 and CANN 9.2 targets, which is the first time a frontier open-weight lab has published a dual-silicon serving path rather than left it to the community; The Information's report (grade 3) that DeepSeek will begin pretraining on Ascend 950DT by end-2026 is the training-side counterpart. The same week Google open-sourced TorchTPU (about 450 ATen ops, the 5,000 most-downloaded Hugging Face models out of the box) with Meta confirming SGLang-TorchTPU in production, and AWS opened TorchNeuron for Trainium. For the tree this is a serving-layer story and all three are excluded as non-model artifacts; for a buyer it means the portability question has moved from 'does the model run here' to 'does the vendor's own serving stack run here', and for two of the three largest open-weight families the answer is now yes on non-NVIDIA silicon.",
        "source": "DeepSeek (DeepInfer README); Google Cloud Blog; AWS What's New",
        "sourceUrl": "https://cloud.google.com/blog/topics/ai-infrastructure/torchtpu-is-now-open-source-run-native-pytorch-on-tpus"
      }
    ],
    "benchmarkMoves": [
      {
        "benchmark": "Artificial Analysis Intelligence Index",
        "movement": "Kimi K3.1 Pro enters fourth at 55, eleven points above Kimi K3; the open lead (MiMo-V2.6-Pro, 46) is unchanged because K3.1 Pro's weights have not shipped; MiniMax M3.1 Flash enters at 43 and Ling 3.1 Flash 1019 at 44",
        "rows": [
          {
            "model": "Claude Opus 5.5",
            "score": "58"
          },
          {
            "model": "Claude Sonnet 5.5 / Muse Wonder Full",
            "score": "56 / 56 (Wonder Full subscription-only)"
          },
          {
            "model": "Kimi K3.1 Pro (API; weights promised Nov 4)",
            "score": "55"
          },
          {
            "model": "MiMo-V2.6-Pro (open lead)",
            "score": "46"
          },
          {
            "model": "Ling 3.1 Flash 1019 / Kimi K3 / MiniMax M3.1 Flash",
            "score": "44 / 44 / 43"
          }
        ],
        "source": "Artificial Analysis (K3.1 Pro launch benchmarks; changelog)",
        "sourceUrl": "https://artificialanalysis.ai/articles/kimi-k3-1-pro-launch-benchmarks"
      },
      {
        "benchmark": "Cost per Intelligence Index task (Artificial Analysis; list price times tokens used). Fact home for the per-task figures in this issue",
        "movement": "The $2 / $10 band's members differ ~3x per completed task: K3.1 Pro spends ~55k output tokens per task; Ling 3.1 Flash 1019's cost per task rose 48% on a 41% token increase at an unchanged $0.30 / $0.90 list. The GPT-6.1 Sol row is from the tracker's K3.1 Pro versus GPT-6.1 Sol comparison page (verified at fact audit; added after grading, having reached the graded packet only through implicator.ai's reading). Artificial Analysis's launch article carries a slightly different K3.1 Pro figure from its model record; the model record is used here",
        "rows": [
          {
            "model": "Claude Opus 5.5 ($4 / $20)",
            "score": "$5.98 per task"
          },
          {
            "model": "Kimi K3.1 Pro ($2 / $10)",
            "score": "$2.11 per task (~55k output tokens)"
          },
          {
            "model": "Ling 3.1 Flash 1019 ($0.30 / $0.90)",
            "score": "$1.49 per task (from $1.01; 145k from 103k output tokens)"
          },
          {
            "model": "GPT-6.1 Sol ($2 / $10)",
            "score": "$0.72 per task (~38k output tokens; Artificial Analysis comparison page, verified at fact audit)"
          }
        ],
        "source": "Artificial Analysis (K3.1 Pro versus GPT-6.1 Sol comparison, verified at fact audit; K3.1 Pro launch benchmarks; Ling 3.1 Flash 1019 comparison)",
        "sourceUrl": "https://artificialanalysis.ai/models/comparisons/kimi-k3-1-pro-vs-gpt-6-1-sol"
      },
      {
        "benchmark": "Terminal-Bench 4.0 (agentic tasks in a Unix terminal; Artificial Analysis harness unless labelled)",
        "movement": "K3.1 Pro's 67% is the highest the tracker has measured (K3 was 13%) and sits three points above the tracker's own Sonnet 5.5 (64%) and seven above Opus 5.5 (60%), figures on the tracker's Opus-versus-Sonnet comparison page that the graded packet did not carry (verified at fact audit); the week's two open releases carry vendor-run figures on different task sets; DeepSeek 1017's independent result exists at 34% (published October 18)",
        "rows": [
          {
            "model": "Kimi K3.1 Pro",
            "score": "67% (Artificial Analysis); 65.7% Moonshot-reported"
          },
          {
            "model": "Claude Sonnet 5.5 / Claude Opus 5.5",
            "score": "64% / 60% (Artificial Analysis comparison page, verified at fact audit; Moonshot's own table reports Opus at 66.4%)"
          },
          {
            "model": "GPT-6 Astra / MiniMax M3.1 Flash",
            "score": "58.2% (Moonshot-reported in its comparison) / 38.1% (vendor-reported)"
          },
          {
            "model": "DeepSeek V4.1-Flash-1017",
            "score": "34% (Artificial Analysis, October 18; verified at fact audit) against 42.3% on the card (Claude Code harness); prior Flash 27%"
          },
          {
            "model": "Recursive Flint",
            "score": "54.9% on Terminal-Bench 2.1 (vendor-reported; different task set)"
          }
        ],
        "source": "Artificial Analysis; Moonshot AI; Hugging Face model cards",
        "sourceUrl": "https://artificialanalysis.ai/articles/kimi-k3-1-pro-launch-benchmarks"
      },
      {
        "benchmark": "HealthBench Professional (Artificial Analysis; 525 clinician conversations, GPT-5.4 judge, OpenAI length adjustment above 2,000 characters)",
        "movement": "New leaderboard. The ranking is a methodology choice: Claude Opus 5.5 has the top raw score and falls to sixth after the length adjustment because its answers average ~5,900 characters; GPT-6 Luna reaches 61.9% at $0.004 per task",
        "rows": [
          {
            "model": "GPT-6 Astra (max)",
            "score": "66.0% length-adjusted"
          },
          {
            "model": "GPT-6.1 Sol",
            "score": "65.1%"
          },
          {
            "model": "Claude Sonnet 5.5",
            "score": "63.0% (leads HealthBench at 62.1%)"
          },
          {
            "model": "GPT-6 Luna (max)",
            "score": "61.9% at $0.004 per task"
          },
          {
            "model": "Claude Opus 5.5",
            "score": "71.4% raw, 59.9% adjusted (sixth)"
          }
        ],
        "source": "Artificial Analysis (HealthBench Professional article and leaderboard)",
        "sourceUrl": "https://artificialanalysis.ai/evaluations/healthbench-professional"
      },
      {
        "benchmark": "Artificial Analysis Decision Index (second snapshot, 17 models)",
        "movement": "A three-way tie at 65 replaces Jev 1.13's launch lead at 58; the leaders differ sharply underneath on knowledge accuracy (31.6 / 20.2 / 11.6), non-hallucination (84.5 / 85.7 / 93.2) and cost per million tasks ($31.80 / $30.34 / $8.39); decision models remain deferred on the tree",
        "rows": [
          {
            "model": "Liquid AI d1-ultra",
            "score": "65.2 (knowledge 31.6; $31.80 per 1M tasks)"
          },
          {
            "model": "Subquadratic SubQ Snap 1.1",
            "score": "65.0 (knowledge 20.2; $30.34; When2Call 96.2; $0.04 per 1M tokens)"
          },
          {
            "model": "Base Labs elephant-1",
            "score": "64.7 (knowledge 11.6; non-hallucination 93.2; $8.39)"
          },
          {
            "model": "TypeSafe Jev 1.13 / 1.14",
            "score": "57.5 / 57.1"
          }
        ],
        "source": "Artificial Analysis Decision Index; Subquadratic launch post",
        "sourceUrl": "https://artificialanalysis.ai/evaluations/artificial-analysis-decision-index"
      }
    ],
    "scorecard": {
      "asOf": "2026-10-24",
      "rows": [
        {
          "tier": "Closed frontier",
          "leader": "Claude Opus 5.5",
          "challenger": "Kimi K3.1 Pro",
          "note": "Challenger changes from Gemini 4 Argon to Kimi K3.1 Pro: 55 against Argon's 53 on the independent index, three points behind Opus 5.5 at 58, at $2 / $10 list. Claude Sonnet 5.5 at 56 is the higher score but the same lab; Muse Wonder Full at 56 has no API. K3.1 Pro is closed until its weights ship (November 4 promised) and costs about a third of Opus per index task but roughly three times the band's cheapest member (the cost-per-task table). GPT-6.1 Astra remains withheld (34 days)."
        },
        {
          "tier": "Open frontier",
          "leader": "MiMo-V2.6-Pro",
          "challenger": "GLM-5.3",
          "note": "Unchanged at 46 and 45. The week's open arrivals enter below the leader: MiniMax M3.1 Flash at 43 (weights shipped October 23, $20M gate) and Ling 3.1 Flash 1019 at 44 with weights unverified (deferred). The row changes on November 4 if Kimi K3.1 Pro's weights ship and reproduce the API's 55, which would move the open lead nine points in one step; this issue holds that at the weekly's licence-bet confidence (58%) and no higher."
        },
        {
          "tier": "Reasoning",
          "leader": "Claude Opus 5.5",
          "challenger": "Claude Sonnet 5.5",
          "note": "Unchanged. K3.1 Pro's HLE 52% (Humanity's Last Exam) and GDPval-AA 1,846 Elo (#3; an occupational-task rating) are reasoning-adjacent results on the independent suite that put it within reach of the challenger slot once weights and a second week of data exist. The UK AISI report is a reasoning finding of a different kind: Mythos 5's chain of thought concluded targets were simulated and never erred the other way."
        },
        {
          "tier": "Coding",
          "leader": "Claude Sonnet 5.5",
          "challenger": "Kimi K3.1 Pro (provisional: one benchmark)",
          "note": "Held, narrowly. K3.1 Pro's 67% on Terminal-Bench 4.0 in Artificial Analysis's harness is the highest the tracker has recorded, and on the tracker's own figures for the incumbents (Sonnet 5.5 at 64%, Opus 5.5 at 60%; the comparison page was found at fact audit, so the first draft of this row wrongly said no such figures existed) it leads the coding leader by three points on the same harness. The leader holds for one week on the row's standing rule that a change needs a second benchmark or a second week: Terminal-Bench 4.0 is one agentic benchmark, K3.1 Pro has no second independent coding result yet, and its per-task cost is ~3x the band's cheapest member at the same list (the cost-per-task table). K3.1 Pro is the challenger and the row flips next week if the tracker's second coding measurement (SWE-bench Pro or GDPval coding tasks) also places it at or above Sonnet 5.5. Flint's 89.7% SWE-bench Pro v2 and MiniMax's 73.7% DeepSWE are vendor-run and do not enter the row."
        },
        {
          "tier": "Multimodal",
          "leader": "Gemini 4 Argon",
          "challenger": "Gemini 3.8 Live Extended Thinking",
          "note": "Unchanged; no closed multimodal release this week. MiniMax M3.1 Flash (text, image and video input, open weights with a gate) is the week's multimodal artifact and sits in the open row; Kimi K3.1 Pro takes text and image input. Argon's $2 / $10 introductory rate reverts to $4 / $20 on December 1."
        },
        {
          "tier": "Sub-dollar hosted",
          "leader": "Claude Haiku 5.5",
          "challenger": "GPT-6 Luna",
          "note": "Unchanged, with two new entrants to watch: MiniMax M3.1 Flash at $0.15 / $0.60 scores 43, and Ling 3.1 Flash 1019 at $0.30 / $0.90 scores 44 but spends 145k output tokens per task, so its $1.49 per index task is about 70% of K3.1 Pro's at a seventh of its list. GPT-6 Luna's 61.9% on HealthBench Professional at $0.004 per task and GA on the Decisions API (400 questions per request) are the challenger's week."
        },
        {
          "tier": "Edge / small",
          "leader": "LFM2.5-24B-A2B",
          "challenger": "Nemotron 3.5 Bolt 74B-A9B",
          "note": "Held on the independent record. The week's two edge arrivals are the ones to test and neither has an independent score: Recursive Flint (Apache 2.0, ~22.6 GB INT4, 125+ tokens per second on an M4 Pro, every figure vendor-run) and Nemotron 3.5 Bolt E4B 4GB (OpenMDW-1.1, device numbers down to a Jetson Orin Nano, vendor-run). Flint takes the leader slot the week Artificial Analysis or another independent harness places it within five points of its card on the same benchmark; Bolt E4B takes the challenger slot on the tracker's E4B Reasoning run. Until then the row does not move on vendor figures."
        }
      ]
    },
    "vendorSignals": [
      {
        "vendor": "Moonshot AI",
        "date": "2026-10-23",
        "signal": "Kimi K3.1 Pro at $2 / $10 (33% below K3), a 2-3x faster Highspeed variant at $4 / $20, full weights promised by November 4, cyber-related data removed from mid-training and RL",
        "meaning": "A Chinese lab priced into the US mid-tier band at list and pre-announced the open-weight date and the safety trade-off together; the weights date and the licence are the two facts the tree waits for, and the cyber claim is unverifiable from outside until Moonshot publishes xdev-bench-hard or an independent suite confirms the delta (Artificial Analysis's Cyber Index, outside the packet, currently does not).",
        "source": "Moonshot AI (Kimi blog); Reuters via Investing.com",
        "sourceUrl": "https://www.kimi.ai/blog/kimi-k3-1-pro"
      },
      {
        "vendor": "Anthropic",
        "date": "2026-10-22",
        "signal": "Claude Platform GA on Google Cloud (Anthropic-managed) with day-zero betas; Opus 5.5 Fast Mode at $8 / $40; API 2026-10-21 with per-user enforcement_status; SDKs relicensed from MIT to Apache 2.0",
        "meaning": "Distribution and control moved while the model held: a second hyperscaler route with commitment drawdown, a speed tier at twice list (the Application Layer's meter pattern), and a safety-enforcement field scoped to one end customer of a gateway, which is the per-tenant control the AISI report argues the runtime needs. The licence change to Apache 2.0 removes a patent-clause ambiguity for enterprise SDK redistribution.",
        "source": "Anthropic (claude.com); Claude Platform release notes",
        "sourceUrl": "https://platform.claude.com/docs/en/release-notes/overview"
      },
      {
        "vendor": "OpenAI",
        "date": "2026-10-22",
        "signal": "Decisions API GA on gpt-6-luna with 400 questions per request, shared-input caching and image inputs; per-project service-tier restrictions; a 12-hour prompt-cache option at 2.5x the write rate",
        "meaning": "OpenAI productised the cheap tier for structured decisions and gave administrators a way to pin projects to a service tier, which together with the 12-hour cache (Agent Techniques carries the arithmetic) are platform moves below the paused most-capable tier; GPT-6.1 Astra remains withheld in its 34th day and the Texas suit now seeks court-approved third-party review before any new model reaches Texas users.",
        "source": "OpenAI developer changelog; Texas Attorney General",
        "sourceUrl": "https://developers.openai.com/api/docs/changelog"
      },
      {
        "vendor": "Mistral AI",
        "date": "2026-10-23",
        "signal": "Mistral Large 4 weights placeholder on Hugging Face with an October 31 countdown and roughly 940 waiting as of October 24; API weights 'coming soon'; the two-week 50% launch discount has lapsed",
        "meaning": "No artifact yet, with the promotional rate expiring before the stated date, so the European flagship illustrates both of the weekly's pattern watches at once: an announced open-weight drop without the artifact, and a mid-tier price that was a dated promotion rather than a list. October 31 is inside the window W42 recorded, so this is not a slip until it passes; the row's openness field and the weekly's licence bet resolve then.",
        "source": "Hugging Face (mistralai); Mistral docs; Mistral changelog",
        "sourceUrl": "https://docs.mistral.ai/models/mistral-large-4"
      },
      {
        "vendor": "NVIDIA",
        "date": "2026-10-23",
        "signal": "Nemotron 3.5 Bolt E4B 4GB ships under OpenMDW-1.1 (NVIDIA's permissive open-model licence) with device-measured llama.cpp numbers and a DFlash drafter; lands in Ollama 0.40.10; Artificial Analysis begins evaluating a Reasoning variant",
        "meaning": "The hardware vendor completed the Nemotron 3.5 ladder from 120B to 8B in nine days and is the only vendor this week publishing tokens per second per device for its own model, which is the disclosure the weekly's handset lever asks for from someone other than the vendor. The Gemma-4-E4B base makes it the first NVIDIA row descending from a Google model on the tree.",
        "source": "Hugging Face model card; NVIDIA Research (Nemotron); Ollama releases",
        "sourceUrl": "https://research.nvidia.com/labs/nemotron/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/"
      }
    ],
    "watchlist": [
      {
        "window": "Nov 4",
        "title": "Kimi K3.1 Pro weights: repository, precision and licence",
        "why": "The open-frontier row moves nine points if the weights ship and reproduce the API's 55; the tree's openness field changes from closed only when the artifact and licence can be read. The weekly's ledger bets at 58% that the licence carries a K3-style revenue or MaaS gate; a slip past November 4 is the pattern watch's confirming instance for the week."
      },
      {
        "window": "Oct 31",
        "title": "Mistral Large 4 weights and licence on Hugging Face",
        "why": "The stated date for the drop; the weekly's licence bet resolves on it. An Apache 2.0 or MIT release with an Artificial Analysis placement within a week narrows the label-versus-artifact pattern; another slip or a custom licence confirms it. The row's notable field is updated either way."
      },
      {
        "window": "By Nov 30",
        "title": "First independent runs of MiniMax M3.1 Flash and Flint; any re-test of DeepSeek V4.1-Flash-1017 on Terminal-Bench 4.0",
        "why": "The 1017 result exists (34%, published October 18, found at fact audit) and scores the weekly's bet a hit; what remains to watch is whether the tracker's run of the 1017 snapshot moves on a re-test. For the two artifacts that shipped this week, the first independent score on the same benchmark decides whether Flint takes the edge-row leader slot (within five points of its card) and whether Bolt E4B takes the challenger slot."
      },
      {
        "window": "Oct 28 to Nov 1",
        "title": "Artificial Analysis's Nemotron 3.5 Bolt E4B Reasoning evaluation and any Cyber Index publication for Kimi K3.1 Pro in the graded packet",
        "why": "The E4B Reasoning run gives the edge row its first independent number of the Nemotron 3.5 generation; a published Cyber Index entry for K3.1 Pro would move Moonshot's curb claim from vendor-reported against an outside-the-packet contradiction to a graded comparison."
      },
      {
        "window": "Dec 1",
        "title": "Gemini 4 Argon reverts from $2 / $10 to $4 / $20",
        "why": "The mid-tier band falls back to three labs at list unless Google extends the rate; routing tables carrying Argon should re-run on December 1, which is also the weekly's December meter-cluster checkpoint (Copilot usage billing default-on, standalone Copilot SKUs)."
      },
      {
        "window": "Ongoing; FAR checks quarterly",
        "title": "How federal contractors read 'developed using DeepSeek implementations' in the DHS FASCSA order against DeepSeek-derived open checkpoints and DeepInfer",
        "why": "The order defines neither 'implementation' nor a self-hosted carve-out, and the exception window closed before publication; the first agency or integrator guidance on fine-tunes, distillations and serving stacks derived from DeepSeek rows decides whether model provenance becomes a procurement attestation, which is the weekly's first second-order effect."
      }
    ],
    "changelog": [
      "2026-10-24: Issue 27 published. Saturday issue date, Sunday research close (house convention); no model item was added after October 23. Four tree rows added (kimi-k3-1-pro, minimax-m3-1-flash, recursive-flint, nemotron-3-5-bolt-e4b) and two updated (nemotron-3-5-super-vl, mistral-large-4), with a 2026-10-24 changelog entry in content/llm-tree/changelog.md and the lineage layout rebuilt. Big read: a fourth lab in the $2 / $10 band (Kimi K3.1 Pro at 55) three points behind the leader, on an API, at ~3x the per-task cost of the cheapest member, with the week's two shipped open artifacts (MiniMax M3.1 Flash, Flint) as the things to test. Lede rule applied: the three-layer label-versus-artifact reading is this issue's; the per-task cost comparison was surfaced by implicator.ai and the Cyber Index contradiction by Latent Space, both credited; the 'gap narrows from 12 to 3' and 'Moonshot curbed hacking' frames are used with attribution and bounded. Revision cycle 1: the GPT-6.1 Sol $0.72 figure labelled outside the graded packet at each use; the Mistral Large 4 entries no longer describe a second slip, because the October 27-31 window W42 recorded has not closed. Revision cycle 2: the GPT-6.1 Sol $0.72 figure re-sourced to Artificial Analysis's own K3.1 Pro versus Sol comparison page (verified at fact audit; added after grading) and the in-packet ratio to Opus (~35%) carried alongside; the claim that the tracker's launch-article per-task figure was an unexplained discrepancy removed from every location, because the tracker's pages do not support treating it as an error; the Terminal-Bench 4.0 table and the coding scorecard corrected on the tracker's own Sonnet 5.5 (64%) and Opus 5.5 (60%) figures, which the first draft wrongly said did not exist; DeepSeek 1017's 34% (published October 18) entered in the table, the DeepSeek row and the watchlist, with the weekly scoring its bet a hit; the edge row reverted to the independent record (LFM2.5-24B-A2B / Bolt 74B-A9B) with Flint and Bolt E4B carried in the note with their promotion conditions; the Mistral waiting-list count dated; MiniMax's licence gate described as comparable to, not the same as, Moonshot's and Meta's; 'first non-US, non-Anthropic' corrected to 'first non-US'; the Kimi cyber discussion moved to the runtime paragraph.",
      "Tree placements: Kimi K3.1 Pro on mixture_of_experts under kimi-k3, closed, medium confidence until November 4; MiniMax M3.1 Flash on mixture_of_experts under minimax-m3 with commercial_use_restricted; Recursive Flint on mixture_of_experts with no parent (new vendor column), medium confidence pending an independent run; Nemotron 3.5 Bolt E4B on decoder_only under gemma-4 as a text post-train, high confidence on the artifact. Reviewed dispositions: Ling 3.1 Flash 1019 deferred (weights unverified, repository 401); SubQ Snap 1.1, elephant-1 and d1-ultra deferred under the 3-of-6 rule; Mistral Large 4's placeholder deferred as an artifact on an existing row; Kimi K3.1 Pro Highspeed and Opus 5.5 Fast Mode excluded as speed tiers; DeepInfer, TorchTPU and TorchNeuron excluded as serving and compiler stacks; Harness2 excluded as a harness (Agent Techniques).",
      "Scorecard: closed-frontier challenger moves from Gemini 4 Argon to Kimi K3.1 Pro (55 against 53; Sonnet 5.5 at 56 is the same lab as the leader); coding leader held at Claude Sonnet 5.5 for one week under the second-benchmark-or-second-week rule, with K3.1 Pro's 67% leading the tracker's own Sonnet 5.5 (64%) and Opus 5.5 (60%) on Terminal-Bench 4.0 (figures verified at fact audit) and the flip condition stated; edge row held at LFM2.5-24B-A2B / Bolt 74B-A9B on the independent record, with Flint and Bolt E4B in the note and their promotion conditions recorded (revision cycle 2 reversed cycle 1's vendor-figure promotion). Scorecard rule: rows move on independent figures; vendor-measured arrivals are carried in the note with the condition that promotes them. Open frontier, reasoning, multimodal and sub-dollar rows unchanged, with the new entrants noted.",
      "Evidence labels: Artificial Analysis's Cyber Index score for Kimi K3.1 Pro (64%) is outside the graded packet and labelled at every use; Moonshot's xdev-bench-hard figures, Flint's benchmarks and speeds, MiniMax's scores, NVIDIA's Bolt E4B device numbers and quality claim, DeepSeek's $38 per GPU-hour and 155T tokens per day, and Inferact's Ironwood tokens per second are vendor-reported or vendor-run and carry the label at each use. HealthBench Professional figures are carried with the judge and length adjustment stated. Ling 3.1 Flash 1019's open-weight claim is grade 3 and marked unverified. The Information's DeepSeek-Ascend report is grade 3.",
      "Fact homes: the Pulse carries the model rows, rate cards, licences, benchmark tables and the AISI report's model-side reading in full. The weekly carries the mid-tier lever, the DeepInfer margin claim's noise score, the DHS order and the capital context; the Application Layer carries Microsoft's UBB model list, the Anthropic Enterprise and Fast Mode meters and Salesforce's agent toll; Agent Techniques carries the cache TTL arithmetic, the agent runtimes' controls and Harness2. Each is referenced here in one clause with a pointer."
    ]
  }
}