Models / Coding & Agentic Same model as GPT-6 Astra in Language Models, listed again here because it’s used as a coding model and scored on that rubric. It counts once in the directory’s totals.
Maker OpenAI
Released Sep 3, 2026 Strengths & weaknesses + Rated #1 of 128 models on LMArena’s WebDev arena, where people judge working web apps — 42 points clear of the next model + A 1,050,000-token context window, so a large codebase fits in one pass − No result yet on SWE-bench Verified’s standard harness, so its skill at fixing real repository issues isn’t measured here − Priced at the top of the market, at about $20 per million tokens blended
Evaluation In assessment · 55% measured Scored on GPT 6 Astra (Max) · sources as of Sep 26, 2026
Code qualityw45 —
No independent measurement yet
Web developmentw40 9.7
Rule B 1,792 rating (95% 1,780–1,803) from 4,908 votes · Scale 1,050–1,800, 0 to 10 · LMArena WebDev arena (gpt-6-astra-max) · Sep 25, 2026
Codebase contextw15 10.0
Rule B 1,050,000 tokens · Scale 8,192–1,048,576 tokens, 0 to 10 · OpenRouter, context window · Sep 26, 2026
Price & availability Measured the same way, deliberately kept out of the score: what a model costs doesn’t change what it can do.
w = weight, each criterion’s share of the overall score. Missing marks don’t count for or against. How scoring works
+ 600B mixture-of-experts with 27B active and a 1M-token context, built for long-horizon agentic work + $1.00 input and $2.70 output per million tokens, with open weights promised for October 2026 − Generated roughly 1.7× the median output tokens on Artificial Analysis's index, so real cost runs well above the headline rate − A preview with no independent coding-benchmark result yet, so it carries no overall score here
+ Excellent terminal automation, git operations, and CI/CD debugging + Far more token-efficient than reasoning-heavy rivals on routine tasks − Needs detailed, unambiguous instructions; struggles with vague requests − Smaller context window than some rivals, a constraint on huge monorepos
+ Strong at inferring intent from vague prompts and architectural context + 1M-token context supports coherent multi-file, cross-repo refactors − Can use many more tokens than leaner coding models on routine work − Narrates its reasoning at length, which slows down quick tasks
+ Open-weight performance within striking distance of proprietary leaders + Free to self-host, appealing for cost-sensitive or air-gapped teams − Requires serious infrastructure to run at full size − Tooling and IDE integrations are less mature than Copilot, Cursor, or Codex