AI Coding Live News

Indian AI company Sarvam released Sarvam Prism on November 3, a "System One" decision model for enterprise workflows: given a context and questions with fixed answer options, it returns a probability for every option in a single forward pass instead of generating text. Sarvam says that across the 25 decision models it tested, Prism averages 71.8 over three indices with a median response time of 44 ms on one B200, the highest score under 100 ms, and scores 82.5 on its Indian-language benchmark. Weights are on Hugging Face under Apache 2.0, with a hosted API as well. All scores are Sarvam's own measurements.

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DeepSeek Harness, DeepSeek's open-source, MIT-licensed agent app, announced Creator Mode on its official X account on November 2: users describe the plugin they want in plain language, and the agent turns it into a plugin they can use. Demos include a 3D landmark gallery, a 3D Transformer visualizer, a video editing workbench and a paper reader. DeepSeek also published seven bilingual demo prompts on cdn.deepseek.com, cautioning that some plugins took several rounds of conversation, so a single prompt may not reproduce the video exactly. The post drew about 20,000 likes and 1.69 million views. No benchmarks were published.

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On November 2 OpenAI published LifeVal, an evaluation of whether AI can actually get everyday tasks done for ordinary people, such as finding childcare, accessing government services, budgeting, ordering groceries and adapting recipes. The task list draws on the American Time Use Survey, the Multinational Time Use Study and a new demographically representative OpenAI survey of 3,995 people. OpenAI says it has used LifeVal for the past year to track and improve its models, and it lists several best recorded GPT-6 Astra task scores, many at 100%. All scores are vendor-reported and have not been independently reproduced.

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On Nov 2, semiconductor and AI research firm SemiAnalysis released GDPx, a benchmark of agentic capability across 11 domains: chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement (RSI), hardware design, materials science, alignment and software engineering. Its 206 tasks were built by 14 specialist data partners, with SemiAnalysis doing final QA. On the official leaderboard Claude Opus 5.5 leads at 12.0%, followed by Gemini 4 Carbon at 10.9% and GPT-6 Astra at 9.6%; nearly every model scores 0 on finance and legal. SemiAnalysis says safety classifiers drag down Opus 5.5 and GPT-6 Astra, and plans a new version every 3–6 months. All scores are as reported by SemiAnalysis.

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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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YC Labs, a research project at Y Combinator, and multi-agent environment startup Olam Labs published "Agents in Games" on Oct 29. Fifty GPT-6.1 Sol agents told to "build a thriving civilization" in survival Minecraft spent three hours huddled in shelters rationing food, sending about 5,900 messages and inventing ration ledgers, permits and job titles. In nine Bed Wars games, 10 GPT-6.1 Sol agents beat 10 Claude Opus 5.5 agents 6–3; in seven 50-vs-50 Mindustry matches lasting about 9.5 hours, GPT won 6–1. The leader was almost always whichever agent was listed first in the directory, and stuck GPT agents fetched the game's public source code on their own. The Minecraft harness is open source. These are one lab's experimental observations, not a standardized benchmark.

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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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On Oct 28, Mixpanel founder Suhail Doshi announced his new company Particle on X, with its first model, Particle-1, live now. Particle's blog says it is a reinforcement-learning post-train of DeepSeek V4.1 Flash that uses 28.4% to 52.2% fewer output tokens and costs 34.4% to 51.6% less per task across four coding and data engineering benchmarks, with pass rates inside the base model's confidence intervals. It is available through the Particle API, OpenRouter and Vercel AI Gateway; Particle's docs list $0.25 input and $1.00 output per million tokens with a 1,048,576-token context window.

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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 27 Liquid AI released LFM3, a new generation of foundation models built on a tri-hybrid architecture of gated short convolutions, sliding-window attention and global attention, pre-trained on 22T tokens. The first six models all branch from LFM3-1.7B-Base at around 2B parameters, with weights on Hugging Face under the LFM Open License v1.0. Liquid says the text model LFM3-1.7B needs about 1 GB at 4-bit, supports 256K context and beats models several times its size on several agent benchmarks.

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