A new price for frontier intelligence.
What the numbers say, what they leave out, and which model deserves your next task.
My launch-day recommendation: give Opus 5.5 a serious trial for everyday coding and document work. Keep Astra available for difficult tasks and established OpenAI workflows. A default is a starting point for testing, not a commitment to one vendor.
At standard API rates, equal uncached input and output quantities cost 60% less than Astra. That makes experiments, revisions, and second passes easier to justify. It does not promise a 60% cheaper finished project.
Astra’s API supports mid-turn steering and asynchronous tool calls. If your workflow uses those features, integration quality may matter more than a small benchmark gap. Replacing the model can also mean replacing part of the workflow.
Scope: this is a synthesis of published specifications and launch results, not a hands-on bake-off. Recommendations are editorial judgments. API costs and consumer subscriptions are different products.
Six shared results from Anthropic’s launch table. Higher is better. Scores describe these evaluations, not a universal intelligence ranking. Launch data ↗
A coding success rate and a professional-work Elo rating cannot sensibly be averaged. Even two percentages may measure very different things. The useful question is whether a test resembles the work you actually need done.
Read each row as a reason to investigate. For a website project, inspect the generated interface, test the forms, and measure how much correction it needs. For an analysis task, check the sources and calculations. A model that produces an impressive first answer can still leave more cleanup work.
The surrounding software matters, too: tools, prompts, retries, budgets, and access to files all influence an agent’s score. These figures do not establish how the two models would perform in an identical setup on your projects.
These are USD API token rates, not monthly chat-plan prices. Cache reads reuse a previously processed prompt prefix; the first write is billed separately. Sources: OpenAI and Anthropic.
Adjust the workload. Output includes billed reasoning tokens. Cached input is an existing cache hit, with no new cache write.
Astra standard rates apply at this input length.
Cost = requests × [(uncached input × input rate) + (cached input × cache-read rate) + (output × output rate)] ÷ 1,000,000. Above 272,000 input tokens, Astra’s entire request uses 2× input/cache rates and 1.5× output rates. Opus keeps standard long-context rates.
No tool, hosting, cache-write, tax, residency, batch, or fast-mode charges are included. The same text can tokenize differently across providers, and models can take different numbers of steps. This is arithmetic, not a forecast of your bill.
Cost per request as input grows, holding billed output at 10,000 tokens. Standard global pricing.
Astra’s surcharge starts when input exceeds 272K tokens. It applies to the full request, not only the excess. Astra specification ↗
Opus includes its 1M context at ordinary rates. Batch input/output are 50% off; fast mode is $8/$40 per million. Those are separate operating modes, excluded from this chart. Claude pricing ↗
A cheaper attempt loses its advantage if you have to repeat it enough times. Conversely, an expensive model can earn its price by resolving a stubborn issue quickly. Track dollars per accepted result and your own review time alongside token use.
For subscription use, the practical questions change: how often you hit a limit, whether the model is available in your plan, and whether the app fits your work. Do not translate this calculator into a ChatGPT-versus-Claude monthly subscription comparison.
The documented API limits are close. A large context window is capacity to accept material; it is not a guarantee that every detail will be retrieved or reasoned about correctly.
gpt-6-astraclaude-opus-5-5Astra specifications ↗ · Opus specifications ↗. Provider tokenizers differ. Opus separately offers 300K output in a Batch API beta.
OpenAI documents updates while work is underway, asynchronous tool execution, and changing reasoning effort while preserving the prompt cache. The application still runs tools and manages pending results. These features can help a well-designed agent remain responsive during a long task; they do not appear automatically in every third-party interface. Astra guide ↗
Opus 5.5 cannot disable thinking or force a specific tool call. Older thinking blocks have model/conversation constraints, and the earlier computer-use tool is rejected on some platforms. Between-tool progress text also changes response shape. Existing integrations need a compatibility check, especially if they stream progress messages. Migration guide ↗
Anthropic describes clearer communication and more than 30% faster output than Opus 5. That comparison is with its predecessor, not Astra. Opus is available in paid Claude plans and through its API/cloud offerings. Product information ↗
I have not measured a controlled Astra-versus-Opus latency test or blind writing preference test for this report. A tokens-per-second headline also misses time spent thinking, using tools, and repairing mistakes. The best measure for your work is elapsed time to a usable result.
Both accept image inputs, but native model output and an app’s image-generation tools are different capabilities. For screenshots or charts, test whether the model reads the details correctly. For site design, judge the rendered page on a phone. Neither a vision score nor a code score settles visual taste.
This is the workflow I would use to make the decision. It favors reversible experiments and measures the work you actually accept.
Start a trial with Opus 5.5. Give both models the same feature brief, repository state, and acceptance checks. Inspect functionality and maintainability, then use the app on mobile. Keep the model that gets you to an acceptable result with the least total intervention.
Run a blind comparison on a real deliverable. Verify every consequential citation, calculation, and quoted passage. Compare the amount of editing required, not just how polished the opening paragraph looks. A shorter report is valuable only if it preserves the evidence you need.
Try Astra as a second approach before spending another hour repairing the first model’s answer. Give it the original problem and evidence, with unsuccessful attempts clearly identified. Independent reasoning is more useful than asking a second model to rubber-stamp a confident conclusion.
Keep the existing model until a replacement passes regression checks. Test tool selection, malformed responses, interruptions, permissions, and recovery from partial failures. A small cost saving is easy to erase with one broken scheduled workflow.
Pick four coding tasks, two research tasks, two document tasks, and two automations from your own backlog. Define “done” before either model starts. Run the same tools and starting inputs, allow the same budget, and repeat ambiguous results.
Record acceptance, dollars, elapsed time, corrections, and serious errors. Where possible, hide the model name from the reviewer. Select a default only after it meets your quality bar; retain the other model for tasks where it earns its additional cost. This follows OpenAI’s general selection advice to establish accuracy first, then optimize cost and latency. Model selection ↗
Checked September 22, 2026. Official release pages take precedence over pre-release posts still appearing in search. This report is a dated snapshot, not a live leaderboard.
The values are transcribed from the launch table; no benchmark was rerun here. Cross-provider conditions can differ. There is no new independent replication in this report, no statistical test of the gaps, and no basis for treating this sample as every task that matters. Elo has its own scale and is never converted into a percentage.
No. Safety policies, refusal behavior, and safeguards are part of a production system, but this report has not run a shared safety evaluation. Neither the price charts nor the task charts support declaring either model universally safer.
A controlled evaluation on your projects showing that Astra consistently saves more human time, or that Opus needs more retries than its price advantage can absorb. Sustained reliability, current availability, and integration costs deserve more weight than a single launch-day result.