AI Coding Live News

On Oct 28 independent evaluator Artificial Analysis released Intelligence Index v5, its biggest revision since v4 in January: it adds Cognition's FrontierCode 2.0 and Terminal-Bench-Science, swaps Humanity's Last Exam for HLE-Diamond, and now draws 60% of its weight from private or held-out test sets. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra at 57 and GPT-6.1 Sol at 55; the top open-weights model, Kimi K3.1 Pro (weights not yet released), scores 46, widening the gap from 3 points in v4.3 to 12. It also launched Progress Index v1 for comparing models across years.

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On 2026-10-23 (17:15 UTC) the official @Kimi_Moonshot account launched Kimi K3.1 Pro, alongside K3.1 Pro Highspeed (2-3x faster). Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and full weights are promised by 2026-11-04. The official tech blog describes it as a post-trained version of K3 with much larger multi-domain joint RL: a 2.8T-total / 104B-active MoE, 1M-token context, multimodal. Vendor-reported scores: 65.7% on Terminal-Bench 4.0 (GPT-6 Astra 58.2%, Claude Opus 5.5 66.4%) and 92.7% on BenchCAD with tools. The Kimi API pricing page lists kimi-k3.1-pro at $2 input, $10 output and $0.20 cached input per million tokens, down from K3 at $3/$15.

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On 2026-10-23 (01:41 UTC) the official Kimi account @Kimi_Moonshot posted a 24-second, retro-CRT-style video with no caption text. The Kimi Code terminal at the start of the clip reads "Model: Kimi K3.1" and "Version: 2.2.0", the status bar shows "Kimi K3.1 thinking: high", and a notice says "Kimi Code Desktop is here". The user then types "Build a complex procedural 3D scene in Three.js." and the model calls the Write tool to create ./scene.js. This is the first time the Kimi K3.1 name has appeared in official material. The post has no caption and no release date, benchmark, price or parameter details, so this is a teaser and not a confirmed release.

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SemiAnalysis revealed the first Google TPUv7 benchmark submission for the 2.8T Kimi K3 MoE model, led end-to-end by Inferact (the creators of vLLM) and Google using the TorchTPU vLLM stack. At a hardware rental cost of just $1.21/chip-hour (vs $2.31 for GB300 and $1.86 for GB200), TPUv7 delivers ~2.2M tokens per $1 TCO in the ultra-high interactivity regime (~390 tok/s/user), outpacing NVIDIA GB200 NVL72, B300, and AMD MI355X on cost-performance.

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Arena announced a major update to Code Arena: WebDev: DeepSeek-V4.1-Flash-20261017 (Max) clinched the #1 position among open models with 1670 points, surpassing Kimi K3 (Max) at a 10x lower cost. Ranking #12 overall, it outperforms Claude Opus 5 (High) and narrows the gap with closed-source frontier GPT-6 Sol (Max) to roughly 10 points, resetting the Pareto efficiency curve at $0.30 input / $1.20 output per million tokens.

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vLLM and NVIDIA unveiled a unified CuTe DSL decode megakernel (~12,000 LoC authored by NVIDIA CentML's Benjamin Chislett) for Moonshot AI's frontier Kimi K3 model on NVIDIA GB300 NVL72. By fusing attention, MoE, and cross-GPU communication, the optimization cuts per-layer latency from 130 µs (23 kernels) to 69 µs (13 kernels), delivering 842 tokens/s at batch=1 with DSpark speculative decoding (1.73x faster than stock vLLM) and up to 1.8x higher P90 per-user interactivity (403.2–423 tok/s/user) on SemiAnalysis AgentX, with DeepSeek-V4.1-Flash (PR #61868) and GLM-5.3 support coming next.

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A vLLM blog post by NVIDIA engineer Benjamin Chislett details an agent-assisted CuTe DSL decode path for Kimi K3 on NVIDIA GB300 that fuses attention, MoE and NVLink communication, cutting per-layer time from 130 µs across 23 kernels to 69 µs across 13. With DSpark speculative decoding on 16 GPUs it reaches 842 tok/s at batch 1, 1.73x stock vLLM; PR #61867 was merged on Oct 16.

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On 2026-10-01 DigitalOcean launched Agent Droplets for Managed Agents (public preview): Pro $50/mo (15% off) and Team $200/mo (20% off) bundling Harness Runtime microVMs, DO-hosted open-model inference (Kimi K3, GLM 5.3), session storage, and Action Gateway (16,000+ tools). Works with Claude Code, Codex CLI, OpenCode. Paired with prepaid Inference and Agents Balance.

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Inferact open-sourced inferact/tpu-megakernels on 2026-09-23: a Pallas megakernel collection for TPU v7. Their blog reports Kimi K3 at 709 tok/s on 16× TPU v7 Ironwood vs 452 tok/s on 16× GB200 under DSpark speculative decoding (acceptance length 6), and roughly 1.4–2× the GB200 baseline at batch sizes 1–8 without speculation. All 92 MoE layers of Kimi K3 run in a single kernel with cross-layer weight prefetch.

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The vLLM project (@vllm_project) released v0.30.0 with 762 commits from 315 contributors. Highlights include hybrid-attention hot paths for Kimi K3 (KDA/AttnRes/MLA), DeepSeek-V4.1-Flash (MXFP8 KV and async Engram), and Qwen3.8-Flash-Next (QSA/PLE, lower sparse-GQA overhead); plus HiSparse host-tier decode, Model Runner V2 EAGLE3 pipeline parallelism with adaptive verification, dual-key Gumbel-max watermarking, Fast Start CUDA IPC weight cache, and new models such as GLM-5.3-Flash, K2-Horizon, Cohere Compass, and Bailing V3 VL.

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@ArtificialAnlys evaluated StepFun Step 5 Preview (600B-total / 27B-active MoE): Artificial Analysis Intelligence Index 44, matching Kimi K3 (max) at about $0.72 per Index task (~2.8x cheaper). Pricing is about $1/$2.70 per 1M input/output tokens with $0.05/M cached input; 1M context; open weights planned for October 15. It still lags peers on agentic evals such as GDPval-AA and Terminal-Bench 4.0.

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@Kimi_Moonshot announced Kimi K3 is now on Amazon Bedrock for coding, document analysis, and extended agent workflows, with Bedrock access, encryption, and auditing controls, plus explicit prompt caching. AWS published a Bedrock model card so teams can call K3 inside the AWS stack.

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