AI Coding Live News

On November 10, Google DeepMind and Artificial Analysis launched MKA (Multimodal Knowledge Agent), a benchmark that tests whether agents can read visually dense files such as architectural drawings, engineering schematics, and clinical scans and deliver slide decks, spreadsheets, reports, and marked-up images. Its 200 private tasks span 10 industries and 83 occupations, with about 70 expert rubric checks per task. Artificial Analysis tested 21 models at launch: Claude Fable 5.2 (max) leads with a Climb WSR of 57%, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%. Fable averages $31.97 per task versus $6.29 for Carbon. Ten public tasks are on Hugging Face.

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Independent evaluator Artificial Analysis published two new results on November 10. On AA-3D, which tests how well a model acting as an agent builds interactive three.js web scenes, Claude Fable 5.2 (Max) scores 1271 Elo, 67 points ahead of second-place Claude Sonnet 5.5 (1204); its 95% confidence interval of 1246 to 1297 does not overlap any other model, so the lead is outside the margin of error. It is 132 points above Fable 5.1 (1139) and ahead of the top OpenAI and Google entries, GPT-6 Astra (1168) and Gemini 4 Argon (1147). On VIALS-AA, a life-science image reasoning benchmark, Fable 5.2 scores 67%, ahead of previous leader Gemini 4 Carbon (64%) and GPT-6 Astra (63%). AA also found opposite strategies: Fable averages about 37 tool calls per task, Astra about 8.

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Early on November 10 SGT, the official AI at Meta account introduced Muse Ember, which Meta calls its smallest and most efficient model so far. It targets the work agents repeat all day: tool calls, coding subtasks, routing, extraction and long document parsing. Muse Ember has a 1M-token context window, up to 512K output tokens, and native understanding of text, images, audio and video. Pricing is $0.10 per million input tokens, $0.004 per million cached input tokens and $0.50 per million output tokens, and it is live on the Meta Model API (as muse-ember-1.0) and in Muse Code. Artificial Analysis measured an Intelligence Index score of 36, the same as Claude Haiku 5.5 (max), at about $0.14 per task, roughly a third of Haiku's cost. It is the first model trained using Muse Wonder Full, and the weights are not open.

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On November 8 (morning of November 9 in Singapore), independent evaluator Artificial Analysis released version 2 of its Decision Index for decision models. The headline metric is now a Decision Score that rewards both correct answers and well-calibrated confidence, the private-data share rises from 25% in v1 to 45%, and two new evaluations are added: AA-Paperclip with 1,500 private documents and AA-Symmetry for answer consistency. Among decision models, Nace AI's Drex Max v1.1 and Fastino Labs' GLiDE v1.2 both score 73, with Drex Max narrowly ahead, and Base Labs' Mammoth 27B scores 72. General LLMs score higher, with Claude Haiku 5.5 (Max) at 77 and GPT-6 Luna (Max) at 76, but cost $6,240 and $1,530 per 1M tasks versus $102 for Drex Max.

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On 2026-11-06, Artificial Analysis (@ArtificialAnlys) launched VIALS-AA: an independent rerun of Handshake AI VIALS (161 tasks across 8 scientific image domains) in the open Stirrup harness with code execution and image tools, three repeats per task, headline metric pass^3. Vendor board: Gemini 4 Carbon (high) ~64%, GPT-6 Astra (max) ~63%, Claude Opus 5.5 (max with fallback) ~61%; open-weights lead Kimi K3.1 Pro ~45%.

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On 2026-11-06 Artificial Analysis launched Endpoint Accuracy Index v1.1: each serverless endpoint is scored as a percentage of a self-hosted official-weights reference across equally weighted BFCL-500, HLE-250, and AA-LCR-25. Vendor claim: nearly 1 in 4 of 100+ benchmarked endpoints score below the reference. AA-LCR-25 moved to v1.1 with repeats raised from 10 to 25 (average 95% CI ~±4.7 to ±2.6), and coverage expanded to six open-weights models including GLM-5.3 Flash and Muse Spark Open.

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On November 5 Thinking Machines Lab introduced Inkling 2 Small, the first second-generation model: a 594B-total / 17B-active MoE with image input and variable thinking effort. The company reports an Artificial Analysis Intelligence Index score of about 31, with large gains versus prior Inkling models in agentic coding, vision and knowledge work. It is available now on Tinker for inference and fine-tuning with closed weights; full Apache 2.0 weights are planned next week after external safety testing. Tinker list pricing is about $0.30/$1.00 per million input/output tokens at 64K context.

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On November 5, the BenchFlow team publicly launched FrontierPhysics (arXiv:2611.06910): 56 end-to-end physics-research tasks authored by 42 physicists from their own work across nine domains. Agents must do literature review, planning, research/experiments, deterministic artifacts, and a manuscript. Across 22 models in 26 configurations, no setup passed more than 46 of 168 rollouts; the best reported Pass@1 is about 27.4% for Claude Opus 5.5 in Claude Code. Expert trajectory review finds the main gaps are rigorous reasoning and expert intuition, not missing physics facts. Artificial Analysis said it will partner to host the evaluation.

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Independent evaluator Artificial Analysis published Tencent Hunyuan Hy4 results on the Intelligence Index v5 on November 5 (08:28 UTC): a composite score of 35, tying Grok 4.7 (xhigh) and one point behind Claude Haiku 5.5 (max). Among China-based labs it ranks second only to Kimi K3.1 Pro (46). On agentic coding it scores 31% on FrontierCode 2.0 and 37% on Terminal-Bench 4.0; cost per Intelligence Index task is about $0.78, roughly one-tenth of same-score Grok 4.7.

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Independent evaluator Artificial Analysis announced on X on November 4 that its six Capability Indexes (Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering, Economics) are now v2.0, aligned with Intelligence Index v5.0 evaluations and weighting Non-Hallucination and Agentic Tool Use more heavily. Across the indexes, HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1 and GDPval-AA is removed; Engineering gains a new Science capability. Per Artificial Analysis, Claude Opus 5.5 (max) leads all six, with GPT-6 Astra (max) second in Finance & Accounting, Legal and Engineering. Among open-weights models, Kimi K3.1 Pro (weights not yet released) leads all six at #5 to #8.

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On October 31, independent evaluator Artificial Analysis released AA-Omniscience v1.1, its knowledge and hallucination benchmark and part of Intelligence Index v5. The question set shrinks from 6,000 to 3,000, dropping previously public and saturated questions, fixing errors and ambiguities, and adding new ones, and a new 300-question public set on Hugging Face is calibrated to the private set's difficulty, with 0.99 Pearson and Spearman correlation on Net Score. Gemini 4 Carbon (high) leads with a Net Score of 54, ahead of Claude Opus 5.5 at 48 and Claude Fable 5.1 at 46; Fable 5.1 has the highest accuracy (68%) and Gemini 4 Argon the lowest hallucination rate (4%). Models score about 1.9 points lower than on v1 on average, and MiniMax M3.1 Flash drops 22 points. All figures are as reported by Artificial Analysis.

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Voice model company Cartesia launched Managed Agents on Oct 29, an end-to-end voice agent platform for enterprise phone calls that tunes its own speech-to-text and text-to-speech models together with the agent harness, and bundles telephony, knowledge bases, tool calls and outbound batches. Cartesia says it ranks #1 on Artificial Analysis's Speech Agent Arena with a 1115 Elo and scores 87.4% accuracy and 90.7% experience on ServiceNow's EVA-Bench (vendor-reported, with GPT-5.5 as the reasoning model). Launch pricing is 50% off at $0.03 per minute.

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At GitHub Universe 2026 on Oct 28, GitHub made Copilot Fusion generally available. It merges the multi-model orchestration of the Project HydraFusion research preview with Copilot auto model selection: users pick Copilot Fusion and an effort level (Standard, High or Max) in the model picker, and the system decides which models to use and whether to run a Single, Cascade or Critique workflow. GitHub says Fusion Max scored 60 on the Artificial Analysis Coding Agent Index, two points below Claude Code with Opus 5.5 Max, at about half the API cost per task, about half the tokens and about 40% less time. Each underlying model is billed at its standard rate, and paid plans get 30% off through Nov 30.

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Google DeepMind announced Gemini 4 Carbon on Oct 28, calling it its strongest model, just two weeks after Gemini 4 Argon. The official blog says it ranks first on v5.0 of the Artificial Analysis Intelligence Index at 59 and leads three cybersecurity evaluations: 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching; it also gains 89 points to rank fourth on WebDev Arena at 1769. Access starts this week for a subset of cyber defenders in the Fairwind program, with developers, businesses and consumers to follow. Introductory pricing matches Argon at $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 afterwards.

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