# GPT-6 Astra vs Claude Opus 5.5: The Two Flagships That Never Fully Met OpenAI's GPT-6 Astra entered limited preview on September 3, 2026; Anthropic's Claude Opus 5.5 shipped nineteen days later, on September 22. They are the two models everyone is comparing — but the head-to-head data comes from three different sources measured in two different weeks, so the comparison only works if you read the dates and the labels. This article lays out the verified numbers: the independent scores from Artificial Analysis, Anthropic's own head-to-head tables, and the community leaderboards. $10/$50 Astra Price per MTok $4/$20 Opus 5.5 Price per MTok 58 vs 53 AA Intelligence Index 19 days Release Gap 89.9% Opus 5.5 SWE-bench Pro 27K vs 78K Output Tokens per Task ## Two Flagships, Nineteen Days Apart GPT-6 Astra is the top tier of OpenAI's GPT-6 family (alongside Sol and Luna). It entered limited preview on September 3, 2026, with general availability to paid users on September 4 (Wikipedia). OpenAI describes it as built for "the hardest end-to-end work" — complex reasoning, coding, computer use, research — with `reasoning.effort` selectable from low to max. OpenAI's VP of research Aidan Clark said the pre-training run used more than 100,000 GPUs at the Stargate site in Texas, "by far" the company's largest training run (Wikipedia, citing press reports). Claude Opus 5.5 launched September 22, 2026 as the first model of the new Claude 5.5 family, positioned for "long-running agentic coding and knowledge work." It is Anthropic's first release since it called for pacing the frontier, and it was tested before release by external evaluators Frontier Design and METR (Anthropic). The context windows are near-identical: Astra lists 1,050,000 total context with 922,000 maximum input and 128,000 maximum output (OpenAI model docs); Opus 5.5 lists 1M context with 128K max output, up to 300K under the Batch API beta (Anthropic platform docs). The knowledge cutoffs differ: April 30, 2026 for Astra, June 2026 for Opus 5.5. ## The Numbers | **Benchmark** | **GPT-6 Astra** | **Claude Opus 5.5** | **Reported by**| --- | --- | --- | --- | AA Intelligence Index (max effort) | 53 (Sep 9) | 58 (Sep 22) | Artificial Analysis| | AA Coding Agent Index | 62 (in Codex) | no 5.5 figure published (Opus 5: 60) | Artificial Analysis| | FrontierCode | 53.3 (top) | 54.6 (default effort) | Anthropic (vendor)| | CursorBench | not listed | 52.5 (default effort) | Anthropic (vendor)| | Terminal-Bench 4.0 | 59% (high effort) | xhigh; "matches" per Anthropic | AA / Anthropic| | SWE-bench Pro | 69.2 (llm-stats); absent from BenchLM top 75 | 89.9 | BenchLM / llm-stats| | GDPval-AA v2.1 (Elo) | not published | 1846 (Fable 5.1: 1735) | Anthropic (vendor)| Artificial Analysis "Benchmarking GPT-6 Astra" (Sep 9, 2026) and "Claude Opus 5.5 takes the top spot" (Sep 22, 2026); Anthropic "Introducing Claude Opus 5.5" (Sep 22, 2026); BenchLM SWE-bench Pro leaderboard (Sep 2026); llm-stats (Sep 2026). The independent story: on Artificial Analysis's Intelligence Index, Astra at max effort scored 53 on September 9 — level with Claude Fable 5.1 and 6 points ahead of GPT-5.6 Sol. When Opus 5.5 was measured on September 22, it scored 58 and took the top of the index. On the Coding Agent Index, Astra in Codex scored 62, level with Fable 5.1 in Claude Code; the nearest listed figure is Claude Opus 5 (not 5.5) at 60. The SWE-bench Pro leaderboards have a data gap instead of a clean race. BenchLM's September 2026 leaderboard (75 models) lists Claude Opus 5.5 at 89.9% and Claude Fable 5.1 at 81.2%; GPT-6 Astra does not appear in the top 75. A different aggregator, llm-stats, lists Astra's SWE-bench Pro at 69.2% — but that figure comes from a different harness and is not row-comparable to BenchLM. The honest read: only Opus 5.5 has a published SWE-bench Pro figure in the leaderboard that actually contains both-model competition, and the gap's size is unknown. ## Why the Scores Can't Be Read Side by Side The 53 vs 58 gap is real, but it is not a same-day re-run: Astra's number was measured thirteen days before Opus 5.5 existed, and the two index entries come from separate evaluation runs. Several other asymmetries matter: - **Effort settings differ by benchmark.** AA ran Opus 5.5 at all five effort levels with Anthropic's default fallback enabled; the 58 is the max-effort entry. - **Safeguards were on for Anthropic's table.** Opus 5.5's head-to-head results were collected with production safeguards enabled; when they intervened (cybersecurity, biology, frontier-LLM tasks), the work was completed by a different Claude model, and Anthropic states this "likely reduces" the score. - **Standard error is ±2.6 points** for Opus 5.5 (±1.6–2 for the comparison models), per Anthropic — so its 52.5% vs Fable 5.1's 51.8% on CursorBench is within noise. - **The vendor's own caveat:** Anthropic says that "at these levels of capability… benchmark margins have become a less reliable guide to real-world differences," and that in its own use the gap between Opus 5.5 and Fable 5.1 is narrower than the scores suggest. #### Date every score Astra's 53 and Opus 5.5's 58 are thirteen days apart on the same independent leaderboard, and Anthropic's head-to-head claims are self-run with safeguards on. Neither source is a controlled A/B test. ## Architecture and Mechanism The most interesting technical difference is how each model thinks. Astra uses a new reasoning technique — reported by Fortune and The Information as "recurrent depth" or looped transformers — that increases efficiency but "works in a way that obscures some or all of the AI's reasoning," its chain of thought. Safety researchers raised monitorability concerns; OpenAI's answer includes asynchronous misalignment monitoring that triggers alerts (OpenAI docs; Wikipedia). Opus 5.5's approach is the inverse: adaptive thinking is always on and cannot be disabled (a breaking change from Opus 5), with depth controlled by an effort parameter. The token-economics trade is visible in AA's measurement: Astra used 27K output tokens per task at max effort — about a third of the 78K Fable 5.1 used for the same index score. Astra also halved its hallucination rate: AA-Omniscience hallucination fell from 92% (GPT-5.6 Sol) to 51% at max effort while accuracy rose 4 points (Artificial Analysis). On AA's long-horizon knowledge work test (AA-Briefcase), Astra gained ~90 Elo over GPT-5.6 Sol, but on GDPval-AA v2 it dropped ~45 Elo — using only 24 turns per task at max effort, versus 45 for Sol and 60 for Opus 5 / Fable 5.1. A more decisive agent that sometimes finishes early. ## Pricing and Cost per Task The sticker gap is the largest of the whole comparison: Astra costs $10/$50 per million input/output tokens (cached input $1, cache writes $12.5; prompts above 272K input tokens are billed at 2x input and 1.5x output), against Opus 5.5's $4/$20 (cache reads $0.20 — 60% cheaper than Opus 5's $0.50 — and a fast mode at $8/$40) (OpenAI model docs; Anthropic). Per-task cost changes the shape of the argument. AA measured Astra (max) at $3.26 per task on the Intelligence Index versus $7.63 for Fable 5.1 — the same score at roughly 40% of the cost — because Astra uses far fewer output tokens. Anthropic claims the mirror-image result on its own benches: at default effort, Opus 5.5 beats Astra at max effort on FrontierCode (54.6% vs 53.3%) and on GDPval-AA v2.1 (1846 Elo, versus Fable 5.1 at 1735 and Opus 5 at 1708) for "about a fifth of the cost per task." Both labs are telling the efficiency story; only AA's per-task figures are independent. ## Bottom Line Opus 5.5 is the current independent leader: 58 on the AA Intelligence Index, 89.9% on SWE-bench Pro (BenchLM), and $6 per million input tokens cheaper than Astra. Astra's case is token efficiency (27K vs 78K output tokens per task), the largest measured hallucination drop (92% to 51%), and the top score on the September 9 snapshot of the index. The genuine head-to-head is still missing: no independent lab has re-run both models in the same week under one harness, and the only numbers that put them in the same table are Anthropic's, gathered with safeguards enabled. Until that happens, the honest read is: Opus 5.5 leads on the public data available today, Astra is the stronger efficiency design, and the margin is thinner than any of the three sources admits. #### Caveats Independent AA Intelligence Index 58 vs 53, AA Coding Agent Index, $3.26 per-task cost, 92% to 51% hallucination — Artificial Analysis Vendor FrontierCode 54.6 vs 53.3, CursorBench 52.5, GDPval-AA v2.1 1846, "a fifth of the cost" claims — Anthropic's own tables, safeguards on Community SWE-bench Pro 89.9% (BenchLM) and 69.2% for Astra (llm-stats) — different aggregators, different harnesses ### Related Articles - [GPT-5.6: OpenAI's Next-Gen Model Family — Sol, Terra, Luna](/gpt-5-6-openai-sol-terra-luna.html) - [Qwen3.8 vs Kimi K3: The 2026 Chinese AI Model Showdown](/qwen3-8-vs-kimi-k3.html) - [Claude Fable 5: Anthropic's Newest Mythos Model](/claude-fable-5-anthropics-newest-mythos-model.html) OpenAI model docs gpt-6-astra; OpenAI latest-model guide; Wikipedia "GPT-6" (Sep 24, 2026); Artificial Analysis (Sep 9 and Sep 22, 2026); Anthropic "Introducing Claude Opus 5.5" (Sep 22, 2026); Anthropic platform docs; BenchLM SWE-bench Pro leaderboard (Sep 2026); llm-stats (Sep 2026) This article was drafted with AI assistance and reviewed by a human editor. By ZVHH Research — AI Tools & Software Reviews This article was drafted with AI assistance and reviewed by a human editor.