AI Coding Live News

On November 12 Perplexity released pplx-decider-v1.3 (built on Qwen3.8-27B, about 26.1B parameters) and the distilled pplx-decider-flash-v1.3 (built on Qwen3.5-4B, about 4.5B). The weights are on Hugging Face under Apache 2.0, and both models are served through the Decisions API and OpenRouter. The new Locate feature extracts verbatim spans by label or instruction and returns character offsets. Perplexity says v1.3 averages 91.6% accuracy across 63 classification tasks, 6.54 points above its production classifiers.

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On November 9, Anthropic's developer blog claude.dev published a selection guide by Addy Osmani, "Which Claude model should you build with?"; the ClaudeDevs post drew about 9,200 likes and 12,000 bookmarks. The core advice: start most coding and knowledge work on Opus 5.5 at medium effort (the Claude Code default); drop to Sonnet 5.5 for well-scoped bug fixes and spec-driven work; use Haiku 5.5 for classification, routing, extraction, and log-reading subagents; and move up to Fable 5.2 for major refactors, migrations, and deep research. Before switching models, tune effort and give the model a way to verify its own work. The guide also lists prices, default effort levels, and compatibility differences that can trigger 400 errors when migrating.

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AI math company Axiom announced on Oct 27 that swarms of dozens of AI agents formalized the Classification of Finite Simple Groups (CFSG; the original proof runs about 15,000 pages across 500+ articles by about 100 mathematicians) in the Lean proof assistant in 24 days, producing about 43.1M lines of Lean and 2.05M declarations, with the repo public on GitHub. The work surfaced 34 noteworthy deviations from the published record, and AxiomProver produced a new alternative argument for the Unbalanced Group Theorem.

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On Oct 21, Baseten's research team Base Labs released open decision models mammoth-1 (27B) and elephant-1 (9B), full fine-tunes of Qwen3.5 that skip text generation and return per-option probabilities in one forward pass for routing, classification and scoring. Per Baseten, mammoth-1 tops the Jev Decision Index 0.4 at 71.24 and elephant-1 ties for #1 on the Artificial Analysis Decision Index. Weights are on Hugging Face and the Baseten API is free through Oct 27.

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On Oct 9 Microsoft released Microsoft-Decision-1, a decision-scoring model that returns calibrated probabilities over fixed options instead of generating text, aimed at routing, classification, verification, agent guardrails and AI judging. Microsoft reports top accuracy across 36 blind benchmarks, ~35x lower P50 latency than GPT-6 Sol and a 1.3% decision-flip rate under perturbation. Available on Microsoft Foundry and OpenRouter, 32K context, $0.042/M input, free output.

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On Oct 7 Anthropic shipped Claude Haiku 5.5 (claude-haiku-5-5), completing the Claude 5.5 family. It targets high-volume, cost-sensitive work such as summaries, compaction, classification and coding subagents, and Anthropic calls it its fastest model at standard speed. Prompts up to 100K cost $0.10 input / $0.50 output per million tokens, and it is the first Haiku with effort levels. It scores 72.4% on the OSWorld 2.1 offline subset (Haiku 4.5: 15.7%) and 39.2% on Terminal-Bench 4.0. Sonnet 5.5 cache reads were cut 50% the same day, and Max/Team plans get monthly API credits.

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Ollama shipped v0.35.0 on 2026-09-28 (GitHub prerelease): Decision Models via TypeSafe’s Jev API at /v1/systemone. Instead of free text, models answer choice/noul/score questions with options, probabilities, and confidence—for ticket triage, model routing, and classification. First models: Nimble (Bespoke Labs) and Tev1 (Together AI). ~7 commits / 66 files vs v0.34.4; Settings no longer blocks on discovery; macOS update menu and stalled MLX downloads fixed.

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