AI Coding Live News

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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Within an hour of Anthropic releasing Claude Fable 5.2, Cursor and Perplexity both shipped the model and posted results from their in-house evals. Cursor's official account says Fable 5.2 scores 56.9% on CursorBench at default (High) effort, up from 49.2% for Fable 5.1, and 60% at max effort, the top score on the board. Cursor's chart puts the default setting at about $9.70 average cost per task. Perplexity's official account says Fable 5.2 set a record 0.851 on its WANDR benchmark at $14.51 per task, on the performance-cost Pareto frontier; Gemini 4 Argon scores 0.802 ($7.88), GPT-6 Astra 0.735 and Fable 5.1 0.684. Both are vendor-run evals, not independent public benchmarks.

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Public benefit corporation Robocurve (@robocurve) published "You Can't See Everything: Perceptual Attacks on Embodied AI" at 19:44 UTC on 2026-11-06 and will present it at CoRL Late Breaking Results. The team had Claude Fable 5.1, Claude Opus 5.5, GPT-6 Astra, GPT-6.1 Sol and Gemini 4 Argon control a real bimanual robot in a closed loop through the Inspect Robots framework on five unsafe tasks, 900 trials in total. Per the paper, models attempted the tasks in 81/300 trials (27%) with unaltered footage, 263/300 (88%) after a Gaussian blur and 291/300 (97%) with frame-by-frame doctored footage; the "stab the object" doll task hit 60/60 with doctored footage. All props were empty bottles or unpowered appliances. At capture the original post had about 11.2K likes and 3.63M impressions.

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Starting 2026-11-07, many developers on X complained that Claude Opus 5.5 felt "nerfed" in Claude Code: ignoring instructions, repeating mistakes and spawning sub-agents for small jobs. User Shane Melaugh (@shanemelaugh) posted that he had Gemini 4 Argon analyze 1,400 of his Claude Code sessions and found that around November 3 the model name stayed the same but its reasoning style took a sharp turn; at capture the post had about 966 likes, 134 replies and 112k impressions. T3 CEO Theo (@theo) later said that after digging in, Anthropic appears to be rolling out a change to the reasoning summarizer per account, and that he saw no sign of degraded capability. Anthropic had not commented publicly at the time of writing. This is a community debate, not a confirmed downgrade or an official announcement.

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CellType, a YC W26 startup, released BioSimBench 1.0 on November 4. It uses real lab experiments to test whether language models can predict which genes go up or down after a cell is hit by a drug, a gene knockdown or an immune signal. Among 12 frontier models, Gemini 4 Argon ranks first with an overall score of 0.184, ahead of Claude Sonnet 5.5 (0.160) and GPT-6 Astra (0.159), while two lab replicates agree at 0.462. A 27B model CellType trained in its BioSimGym RL environments beats the best frontier model on all four prediction tracks.

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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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Kaggle announced ScarfBench with IBM Research, a 204-task benchmark that tests whether coding agents can migrate enterprise Java apps between Jakarta EE, Quarkus and Spring without changing behavior. IBM's October 27 technical note shows Gemini 4 Argon with Antigravity first on the smoke score at 75.2%, Claude Opus 5.5 with Claude Code highest on all-tests-pass at 72.1%, and GPT-6.1 Sol with Codex cheapest at $1.25 per solved task.

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Perplexity CEO Aravind Srinivas posted early evals of Gemini 4 Carbon on X: it scored a record 0.857 on Perplexity's Wide-And-Deep-Research (WANDR) long-form research benchmark at $5.86 per task, versus 0.802 ($7.88) for Gemini 4 Argon and 0.735 ($13.34) for GPT-6 Astra. He called Carbon the strongest model Perplexity has seen for long-form research and answer quality, and said it will come to all Pro and Max users inside Perplexity Computer once there is enough token capacity.

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AI data company micro1 released Dual-Arm Sim, which has 10 general-purpose frontier models control a two-arm Franka Panda robot in a simulated operating room built on NVIDIA Isaac Sim, using only camera images and robot sensors, across four tasks: swapping instruments, replacing a faulty drug cartridge, passing a sample between hands, and packing a surgical kit. Only 11 of 800 trials were completed, all by Gemini 4 Argon (9) and GPT-6 Astra (2). Models claimed "task complete" in 213 trials and were wrong every time. All 200 traces of the cartridge-swap task are public on Hugging Face.

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Cognition, the company behind Devin, posted on X at 01:45 UTC on Oct 30 that Google's new Gemini 4 Carbon scores 40% on its FrontierCode 2.0 coding benchmark at $4.04 per task, near the top and behind only Claude Opus 5.5, GPT-6 Astra and Claude Sonnet 5.5. The earlier Gemini 4 Argon scores 26.3% at a higher $6.41 per task. The results are Cognition's own runs, and the leaderboard on its site has been updated.

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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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Cognition, the maker of Devin, released FrontierCode 2.0 on Oct 27. It keeps the method of maintainer-written tasks graded on whether the code is actually mergeable, but draws tasks from more repositories, adds security patches and performance work, uses deliberately underspecified prompts and adds many thoroughness criteria. In Cognition's results Claude Opus 5.5 leads at 45.5%, GPT-6 Astra follows at 42.8%, and Grok 4.7 and Gemini 4 Argon drop into the 20s, leaving the benchmark far from saturated.

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On Oct 26, Google's official @googlegemma account said a set of vLLM optimizations built with Gemini 4 Argon lifted gemma-4-E2B-it throughput on a single A100 from about 37,900 to 108,000+ tokens/sec, roughly 2.85x, with 100% token parity. The PRs were filed by Luciano Martins of Google DeepMind, and most are still under review.

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