GPT-6 Astra vs Claude Opus 5.5
Two flagship agent models from September 2026, priced 2.5× apart. What each model is, what the verified pricing means for your bill — and what a head-to-head rematch on real repo tasks would actually test, because we haven't run one.
The price gap, stated plainly
| Model | Input / M tokens | Output / M tokens | Cache read / M |
|---|---|---|---|
| GPT-6 Astra | $10 | $50 | $1.00 |
| Claude Opus 5.5 | $4 | $20 | $0.20 |
Astra is 2.5× Opus 5.5 on both input and output, and 5× on cache reads. On price alone, Opus 5.5 is the cheaper model at every token tier. The question that matters is whether Astra's extra cost buys extra completion — and on which tasks.
Two flagships, two different bets
GPT-6 Astra is OpenAI's frontier model, released in early September 2026. OpenAI positions it for computer use, software engineering, professional deliverables, and multi-step autonomous workflows — a model built for work rather than chat. It rolled out in stages across ChatGPT tiers, the OpenAI API, and major cloud providers, with standard API pricing at $10 per million input tokens, $50 per million output, $1.00 per million cached input. Its most advanced cybersecurity capabilities are restricted to OpenAI's application-only Daybreak program.
Claude Opus 5.5 is Anthropic's flagship, released September 22, 2026 — the first model in its Claude 5.5 family. Anthropic's pitch is efficiency at the frontier: $4 per million input tokens, $20 per million output, $0.20 per million cache reads (20% below Opus 5 on input/output, 60% below on cache reads). It is the model that won our own independent four-experiment evaluation 4–0 over GPT-5.6 Sol and Grok 4.7 on real TypeScript repo tasks. Note carefully: Astra was not in that evaluation — it tested GPT-5.6 Sol, not GPT-6 Astra.
No head-to-head of GPT-6 Astra against Claude Opus 5.5 has been run in our harness. Anthropic published a comparison chart on Opus 5.5's launch day — the vendor-published baseline, reproduced with attribution on our benchmark landscape page — but vendor charts are the starting point for your own evaluation, not a verdict.
The four experiments Astra hasn't faced
Our September evaluation put three models through four experiments on a production TypeScript codebase: a stale model-pin guard (repo-wide identifier sweep with planted decoys — only Opus 5.5 caught the 5 live stale pins), a bug hunt (reproduce, diagnose, and fix a real regression with failing tests), a governance stress test (honor a stated policy boundary under convenience pressure — all three cleared it), and a thoroughness sweep (whole-token migration across 15 seeded occurrences without touching 3 decoys). A genuine Astra-vs-Opus rematch would run gpt-6-astra through the same four experiments and score the same axes: completion quality, tokens consumed, cost per task, and policy adherence.
Until that run happens, treat any "Astra vs. Opus" verdict as positioning, not measurement. The pricing below is verified; the question the rematch would answer is whether Astra's extra 2.5× per token buys enough extra completion on real repo work to matter.
The metric that flips the table
Token price is the wrong number to optimize. What you actually pay for is a completed task, and Anthropic reports the striking version of this on FrontierCode: Opus 5.5's runs came in at roughly one-fifth the cost per task of the best competing result. The per-token price gap is 2.5×; the per-task gap lands at ~5× because a model that needs fewer tokens and fails less often compounds both advantages. This is the single most important frame for comparing these two models: at current pricing, the pricier model has to be dramatically better on your workload to justify its cost. The full arithmetic — including the break-even condition and why long sessions are cache-dominated — is in our cost-per-task explainer.
The "~1/5 cost per task" figure is Anthropic's, from its own FrontierCode runs. Independent reproduction would be better; treat it as a claimed data point in Anthropic's favor, consistent with the direction of the price tables but not independently verified here.
Which one, for what — September 2026
These picks are conditional, not categorical — they follow from verified positioning and our own (Astra-free) evaluation, not from a head-to-head we haven't run. The vendor-published score table lives on our benchmark landscape page; your own tasks are the deciding evidence.
| Use case | Pick | Why |
|---|---|---|
| Repo-level coding agents, general SWE work | Opus 5.5 | Won our independent 4-experiment eval over GPT-5.6 Sol and Grok 4.7 on real TypeScript repo tasks, at 2.5× lower token prices. |
| In-editor pair-programming loops | Opus 5.5 default | Anthropic's published launch chart is the current public baseline — see the benchmark landscape page — and Opus 5.5's pricing is lower at every tier. |
| Computer-use and multi-step autonomous workflows | Evaluate Astra | This is the lane OpenAI built Astra for. At 2.5× per token, measure its completion rate and cost per task against Opus 5.5 on your workflows before committing. |
| Science-flavored terminal tasks, automation-heavy missions | Evaluate both | Anthropic's own launch chart credits Astra with the leads here — see the attributed table — but a vendor chart is a starting point, not a verdict. Your own eval is the tiebreaker. |
| Budget-bounded governed missions | Opus 5.5 default | Mission contracts bound cost; the cheaper model that clears the completion bar is the right default, with stronger models reserved for failure-expensive stages. |
Two models, two different profiles — which is precisely why a governed platform doesn't hardcode one. In our own independent 3-model check (GPT-5.6 Sol, Opus 5.5, Grok 4.7 on real TypeScript codebase tasks), Claude won 4–0, consistent with the vendor-published direction for repo-level work. Details on the benchmark page.
Frequently asked questions
How much does GPT-6 Astra cost compared to Claude Opus 5.5?
Astra is priced at $10 per million input tokens and $50 per million output tokens, against Opus 5.5's $4 and $20 — 2.5× on both. Cache reads are $1.00 vs. $0.20, a 5× gap that compounds in long sessions.
Has MasterEngine benchmarked Astra against Opus 5.5?
No — and this page won't pretend otherwise. Our independent evaluation tested GPT-5.6 Sol, Claude Opus 5.5, and Grok 4.7 (Opus 5.5 won all four experiments). Astra has not run in our harness. A genuine rematch would put it through the same four experiments — stale-pin guard, bug hunt, governance stress, thoroughness sweep — and measure quality, tokens, cost per task, and policy adherence.
Is Astra's higher price ever worth it?
It can be, on workloads where it completes tasks Opus 5.5 fails at. Astra is OpenAI's model built for computer use and multi-step autonomous workflows, so that's the lane to evaluate it in. But at 2.5× per token, Astra needs a large completion-rate advantage on your tasks to win on cost per task — measure, don't assume.
Which model is better for coding agents?
For repo-level coding work, our own four-experiment evaluation put Opus 5.5 on top (over GPT-5.6 Sol and Grok 4.7). Anthropic's launch-day comparison chart is the current vendor-published baseline — reproduced with attribution on our benchmark landscape page. Your own evaluation on your own tasks is the deciding evidence.
What does "~1/5 the cost per task" mean exactly?
Anthropic reports that Opus 5.5 beat the best competing FrontierCode result while costing about a fifth per task — tokens used × price, divided by completion rate. It's Anthropic's figure from its own runs, not an independent audit, but it's directionally consistent with the price tables.
Should I standardize on one model for all agent work?
No — route by stage. Use the cheapest model that clears your completion bar for bulk work, and reserve stronger or specialized models for stages where failure is expensive. That's also how governed missions handle it: the contract bounds cost, and model choice follows the evidence.
From comparison to math
📊 Benchmark landscape
Full September 2026 tables, attribution, and our independent 3-model check.
💰 Cost per task
Break-even math, cache pricing, and why long sessions are cache-dominated.
📖 Docs
The execution chain: how governed missions specify, authorize, and verify work.