Last updated: 2026-09-07 — GPT-6 Astra released September 3, 2026. This article now covers the published benchmark table, the independent pushback, and what was true pre-release is kept as context.
Quick Answer
GPT-6 Astra's launch table shows ARC-AGI-3 in the high 90s (~98.6–99.9%), ~72.6% on OSWorld for computer use, 74.1% on DeepSWE v1.1, and a claimed ~68% agentic long-task accuracy. But an unusual share of these are vendor-reported, and independent evaluators — Artificial Analysis, Vellum, and Reddit threads — show mixed coding results versus Claude Fable 5.1 and Opus 5. Treat the official table as the ceiling, not the verdict.
What We Have (Math, Formally Verified)
The original reveal, on August 1, 2026, leaned on evidence rather than a leaderboard:
- 10 math problems previously considered human-level, solved at ~$2,000 in tokens each.
- Each proof verified by Lean 4, a formal proof assistant — mechanically checkable, unforgeable.
That part of the record stands: formal verification can't be gamed by contamination or memorization.
Published Scores Since Release
From OpenAI's announcement tables and third-party coverage:
| Benchmark | GPT-6 Astra | Comparison | Source quality |
|---|---|---|---|
| ARC-AGI-3 | ~98.6–99.9% | 87.8% (Claude Fable 5.1) | Vendor-reported |
| DeepSWE v1.1 (agentic coding) | 74.1% | 72.7% (GPT-5.6 Sol) | Vendor-reported |
| OSWorld (computer use) | ~72.6% | prior frontier ~70-72% | Third-party (MarkTechPost et al.) |
| ExploitBench | 100% | 78.5% (Sol), 70% (Opus 5) | Vendor-reported |
| ExploitGym | 42.4% | 30.3% (Sol) | Vendor-reported |
| Agentic long-task accuracy | ~68% | +10 pts over 2nd place | Vendor-reported |
Two things stand out. First, Astra's lead is largest exactly where independent verification is hardest (ARC-AGI-3, ExploitBench) and thinnest where practitioners can check it daily (agentic coding). Second, the ExploitBench/ExploitGym numbers double as a cybersecurity-capability statement — the same capability that earned the model its pre-release Critical rating.
The Independent Pushback
- Artificial Analysis published both a launch review and an evaluation noting Astra costs ~75% more than GPT-5.6 Sol (blended-price basis) and questioning its price-performance: per point of benchmark score, it's the most expensive OpenAI model to date.
- Vellum notes Astra leads "in OpenAI's table" — phrasing that matters: several numbers have not replicated independently yet.
- Reddit threads (r/OpenAI and others) argue Astra is "actually behind Fable, and even Opus" on real coding work — anecdotal, but consistent with the pattern above.
None of this means the scores are fake. It means the honest reading is: Astra is clearly the strongest OpenAI model; whether it's the strongest model overall depends on which benchmark — and whose run — you trust.
Which Numbers Actually Matter
For most developer decisions, rank the table like this:
- Cost per solved task, not per token. Astra's $10/$50 pricing plus a long-context surcharge past ~272K input tokens means a 2x quality win can still be a 5x cost loss on volume work.
- Your own repo. Agentic coding benchmarks (DeepSWE at 74.1% vs Sol's 72.7%) are within run-to-run noise for real codebases. The only benchmark that matters for your workflow is your task suite.
- Computer use, if that's your use case. OSWorld ~72.6% is a genuine, practically-visible lead — desktop automation is where Astra's advantage is hardest to dispute.
- Cache economics. Cache reads at $1.00/M vs $10 fresh input means agent loops with stable system prompts see very different real costs than the headline price implies.
How to Evaluate Astra Yourself
Don't trust the marketing — including ours:
- Your own repo — 10-20 representative tasks, run twice each.
- A/B against your current model — same tasks, compare output quality and cost.
- Cost-weighted — a 10% quality gain isn't worth 2x+ the price for volume tasks.
The practical setup is a multi-model gateway where you can flip between Astra and cheaper models per task:
from openai import OpenAI
client = OpenAI(api_key="sk-teamo-xxxxxx", base_url="https://api.teamorouter.com/v1")
# try model="gpt-6-astra" vs "deepseek-v4-pro" / "claude-fable-5-1" on the same task
FAQ
Q: What is GPT-6 Astra's best benchmark score? Its headline is ARC-AGI-3 at ~98.6–99.9% (vendor-reported) and OSWorld computer use at ~72.6% (third-party corroborated). The math-proof record — 10 Lean 4-verified problems from the August reveal — remains its most tamper-proof credential.
Q: Is Astra better than Claude Fable 5.1? On OpenAI's table, yes (98.6%+ vs 87.8% on ARC-AGI-3). On independent coding impressions, it's contested — several evaluators and practitioner threads put Fable 5.1 ahead on real software work. See Fable 5.1 vs GPT-6 Astra for the decision-level comparison.
Q: Why do people say the benchmarks are disputed? Because a large share are vendor-reported, independent re-runs show mixed coding results against Fable 5.1 and Opus 5, and the price makes the score-per-dollar picture worse than the raw table suggests.
Q: What should I watch for after release? Independent SWE-style replications, cost per task (not per token), and real-world multi-agent performance — plus your own repo tests.
Summary
Astra's published table is impressive and partially verified: formally-checked math, a real computer-use lead, and strong agentic numbers that haven't fully replicated. Judge it on your own tasks, and route by task so cost stays sane. Sign up for TeamoRouter to A/B Astra against Fable 5.1 and cheaper models on the same key.
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TeamoRouter — flip between Astra, Claude, and cheaper models on the same task, one key.