Introduction
OpenAI announced GPT-6 Astra on September 3, 2026, and framed it as the beginning of what the company calls the "AGI era." The launch numbers include 97.6% on FrontierMath Tier 4 v2, 96% on GPQA Diamond, 100% on ExploitBench, and a near-saturation result on ARC-AGI-3, the benchmark built specifically to resist this kind of progress.
The scores are real. The most-quoted one comes with an asterisk that matters more than the number.
What OpenAI Reported
The claimed results at launch, as reported by OpenAI at the time of publication:
| Benchmark | Score | What it measures |
|---|---|---|
| FrontierMath Tier 4 v2 | 97.6% | Research-level mathematics |
| GPQA Diamond | 96% | Graduate-level science questions |
| BenchCAD | 95.9% | Programmatic CAD — generating executable CadQuery programs across 106 industrial part families |
| DeepSWE v1.1 | 74.1% | 113 long-horizon engineering tasks in live open-source repos, checked by program-based verifiers |
| ExploitBench | 100% | Cyber-exploitation, scored as a 16-flag ladder from reaching vulnerable code to arbitrary code execution |
| ARC-AGI-3 | 98.6% | Learning the rules of an unfamiliar interactive environment |
Only one of the near-perfect scores is a step change, which is clearer against what the field scored a few months ago. On GPQA Diamond, Claude Opus 5 sits at 94.2% and Gemini 3.1 Pro at 94.3%; Astra's 96% is a 1.7-point move on a converged benchmark and means little. FrontierMath Tier 4 is the opposite — GPT-5.5 scored 35.4%, Claude Opus 4.7 22.9% — so 97.6% is a different order of result. Astra ran the v2 revision rather than the cut those models took, so treat the gap as indicative; nothing about the version explains a jump that size.
Cybersecurity is the outlier. OpenAI restricted those capabilities at launch and routed expanded access through a separate program it calls Daybreak Blue. It has not published the reasoning, but a perfect score on a ladder ending at arbitrary code execution is the obvious context.
Coding is the honest number. DeepSWE v1.1 at 74.1% is strong but nowhere near saturation, and its tasks come from live repositories — which makes it the score most predictive of whether the model helps on a real codebase.
The defining product capability is not on this list. GPT-6 Astra ships with computer use — driving browsers, spreadsheets, and desktop applications through the same interfaces a person uses, without a purpose-built API integration for each one.
The ARC-AGI-3 Asterisk
ARC-AGI-3 tests whether a model can learn the rules of an unfamiliar interactive environment from scratch, which is why a near-perfect claim on it carries so much rhetorical weight for a reasoning model. ARC Prize ran and published its own evaluation, as it does for every frontier submission.
The independent results split in two:
- 62.7% on the ARC-AGI-3 Semi-Private set using ARC Prize's standard harness, the configuration every provider runs identically. That run cost roughly $26,000 in compute.
- 99.9% using a provider adapter harness, which preserves OpenAI's opaque reasoning state between requests and compacts long conversations so the model can reuse its own prior work. That run was about 3.66x faster and used 49% fewer tokens.
Same model, same tasks, a 37-point gap. The adapter is not cheating — reusing reasoning state across turns is a real platform capability — but the near-perfect figure measures the model plus OpenAI's proprietary scaffolding, while 62.7% is what survives an apples-to-apples comparison against other providers. The 98.6% that led the launch materials sits between the two and matches neither.
ARC Prize was blunt about the interpretation: saturating the benchmark "would not represent 'proof of achieving AGI,'" because ARC-AGI-3 has a tightly bounded scope and deterministic, closed-ended mechanics. It also reported a striking secondary result — Astra used fewer actions than the median human tester on 96% of completed levels, so on efficiency, not just accuracy, it is past a human baseline drawn from about 500 general-public participants.
VentureBeat flagged a second gap: GDPval, OpenAI's own measure of performance on real occupational tasks, is absent from the launch materials. For a release marketed on economic generality, that is the omission worth watching.
Availability and Pricing
The rollout started with enterprise customers already in OpenAI's Daybreak program, and extends over the following days to ChatGPT Plus, Pro, Business, and Enterprise, plus the OpenAI API, Microsoft Azure, and AWS Bedrock. There is no free tier. Enterprise administrators have to enable the model per workspace — it is off by default at launch.
API pricing, per million tokens:
| Mode | Input | Cached input | Output |
|---|---|---|---|
| Standard | $10 | $1 | $50 |
| Fast | $20 | — | $100 |
Fast mode delivers up to twice the throughput at twice the price. Pro, Business, and Enterprise plans also get a higher-effort GPT-6 Astra Pro variant.
The model ID is gpt-6-astra. It carries a 1,050,000-token context window — matching GPT-5.6 — with 128,000 max output tokens and an April 30, 2026 knowledge cutoff. Input is text and image; output is text only. Tooling includes web search, code interpreter, computer use, and MCP.
Conclusion
GPT-6 Astra is a substantial release: a million-token context, native computer use that changes what an AI agent can be pointed at, and a FrontierMath result that is genuinely discontinuous with what frontier models scored six months ago. Whether it is the start of an "AGI era" is a claim the benchmarks cannot support, and ARC Prize — whose benchmark supplied the headline — said so first.
For anyone actually evaluating the model, the saturated scores no longer discriminate between frontier systems and can be skipped. DeepSWE's 74.1%, and whatever computer-use reliability you can measure on your own workflows, are the numbers with headroom left in them, which makes them the only ones still carrying information.
Sources
- GPT-6 Astra: A new generation of intelligence — OpenAI announcement, September 3, 2026
- OpenAI launches GPT-6 Astra and says welcome to the "AGI era" — The New Stack, September 3, 2026
- OpenAI's GPT-6 Astra on ARC-AGI-3 — ARC Prize, September 2026
- GPT-6 Astra — ARC-AGI results — ARC Prize
- 'Welcome to the AGI era': OpenAI launches GPT-6 Astra — VentureBeat, September 3, 2026
- GPT-6 Astra model reference — OpenAI API docs
- ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents — benchmark paper
- BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD — benchmark paper
- DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks — benchmark paper