Claude Opus 5.5 vs GPT-6 Astra: Which Fits Your Coding Work?

Claude Opus 5.5 is the lower-priced starting candidate for API coding work; GPT-6 Astra is a reasonable candidate when you already use OpenAI’s tools or need to evaluate its documented reasoning controls. Neither choice is a proven coding winner here. The useful question is which model completes your repository task at an acceptable total cost, including failed attempts and review time.

This comparison uses official documentation checked on September 24, 2026. We did not run a head-to-head model benchmark. Prices below are the vendors’ direct standard API list prices in USD, not Ofox quotes, subscription allowances or measured bills. Recommendations are starting hypotheses based on those documented differences.

Opus 5.5 vs Astra at a glance

Sources: Opus 5.5 model documentation, Astra model documentation, and the Opus 5.5 changes.

Opus 5.5 also documents up to 300K output tokens on the Message Batches API with the output-300k-2026-03-24 beta header; that is a separate batch configuration, not the synchronous limit above.

Both offer roughly million-token context capacity, but that does not mean you should paste an entire repository into every request. Retrieve relevant files, state the acceptance criteria and keep tool output focused. Different tokenizers can count the same repository differently, so equal token counts are an accounting example rather than equal amounts of source code.

What the price difference means for a coding task

For an illustrative request with 100,000 uncached input tokens and 10,000 billed output tokens, the arithmetic is:

At these equal token volumes, Astra costs 2.5 times as much and Opus’s calculated token cost is 60% lower. This is not a claim that Opus solves the same task for 60% less: reasoning usage, output length, retries and acceptance rates can differ. Billed output is not just the visible patch; use the provider’s usage record rather than estimating from displayed text.

The difference becomes more important for long inputs. Astra charges the higher rate for the whole request once input exceeds 272K tokens, rather than charging only the excess. Opus 5.5’s standard rates apply across its full context window. At an assumed 300,000 uncached input tokens plus 10,000 billed output tokens, the respective totals are $1.40 for Opus and $6.75 for Astra. These examples exclude caching, batch discounts, fast-mode premiums, regional uplifts, tool charges and taxes. They are calculated examples, not API tests. See OpenAI pricing and Claude pricing.

For repeated repository context, evaluate caching separately. Astra lists $1 per million cached input tokens and $12.50 per million cache-write tokens at the short-context rate; both cache rates double when input exceeds 272K tokens. Opus 5.5 lists $0.20 for cache reads, $5 for five-minute cache writes and $8 for one-hour writes. Those are different billing categories with different rules, not interchangeable discounts. Count writes, misses and expiry before estimating savings. Our Opus pricing guide and Astra pricing guide explain the broader accounting.

API compatibility can matter more than a leaderboard

A model comparison is also an integration comparison. According to Anthropic’s migration notes, Opus 5.5 rejects disabled thinking and manually specified thinking budgets. It also rejects forced tool_choice values such as any or a named tool; auto and none remain supported. That does not mean it lacks tool calling. It means an adapter that forces a tool must change its control flow rather than just replace the model name.

Astra exposes the reasoning effort levels in the table. The same label, such as high, is not a standardized unit of compute across vendors. Do not compare one model at its default with the other at an expensive maximum and describe the result as a model-only difference.

An existing Claude workflow makes Opus a practical first candidate; an existing OpenAI tool workflow makes Astra a practical first candidate. These are integration-cost judgments, not claims about accuracy. For repository review, require reproducible findings as described in our Opus code-review workflow.

Which one should you try first?

For a routine task, the first question may be whether either model is necessary. A less expensive candidate could already pass your acceptance criteria. The Sol, Luna and Astra selection guide covers that separate decision. This article focuses on Opus 5.5 versus Astra rather than treating all price tiers as equivalent.

Make the final choice on accepted work

Use the same repository commit, bug description, permitted tools and acceptance tests for both candidates. Start each run from a clean checkout. Keep hidden tests hidden, record effort and retry limits, and include failures rather than silently replacing them with successful reruns.

Record provider, exact model ID, request ID, input/cache/output usage, elapsed time, test results and human review minutes. Run several representative tasks more than once. Report security regressions, invented findings and unfinished work separately from average cost. A single polished answer is weak evidence for a repository-wide recommendation.

A useful decision metric is total evaluation cost divided by accepted tasks, with failed-attempt costs included. If no task passes, leave that ratio undefined; do not call the model cheap. Keep human review cost visible even when you cannot put a defensible dollar value on it.

For now, the defensible conclusion is specific: Opus 5.5 has lower published standard token rates, while Astra may be a better operational fit for an existing OpenAI workflow. Whether Astra’s higher bill buys better accepted results remains a question for your workload.