Claude Opus 5.5 vs GPT-6 Astra

The short answer: start with Claude Opus 5.5 for coding, general knowledge work and cost-sensitive agents. Its API list price is $4 per million input tokens and $20 per million output tokens, compared with $10 and $50 for GPT-6 Astra. Start with Astra when the job depends on OpenAI's Responses API tool stack, needs mid-turn steering or async tool calling, or resembles the scientific and automation tasks where Astra leads the published comparison.

There is no neutral universal benchmark for every real workload. The direct scores below come from Anthropic's Opus 5.5 launch evaluation. OpenAI's official documentation supplies Astra's current product specifications, pricing and tools. That distinction matters: a vendor's head-to-head table is useful evidence, but you should validate the ranking on your own tasks.

Pricing: Opus 5.5 has the clear list-price advantage

At standard API rates, Astra costs 2.5 times as much per uncached input token and 2.5 times as much per output token. Its cached input is five times the price of an Opus 5.5 cache read. On a simple workload using one million input tokens and 200,000 output tokens, the list-price calculation is $8 for Opus 5.5 and $20 for Astra, before tool fees, cache effects, batch discounts or different token usage.

Per-token pricing is not the same as cost per completed task. A more expensive model can cost less when it succeeds in one run, needs fewer output tokens or saves review time. OpenAI says Astra can use fewer output tokens than earlier OpenAI models on several evaluations. Anthropic says Opus 5.5 uses fewer tokens per task than Opus 5. Run the same representative jobs and record accepted-result cost rather than comparing list prices alone.

Coding: Opus 5.5 leads the published direct tests

Opus 5.5 leads Astra on Terminal-Bench 4.0 and FrontierCode v1.1 in Anthropic's table. The Terminal-Bench gap is meaningful; the FrontierCode gap is narrow. Anthropic positions Opus 5.5 for sprawling codebase migrations and audits, while OpenAI describes Astra as a model for end-to-end coding and software-engineering work.

The products around the models matter. Claude Code is Anthropic's coding-agent environment. Astra supports hosted shell, apply patch, skills, computer use, MCP and tool search through OpenAI's Responses API. Compare the full agent system—model, harness, permissions, tools and verification—not just an isolated prompt.

Research and knowledge work: the task decides

Opus 5.5 has a large lead on GDPval-AA and Humanity's Last Exam in Anthropic's comparison, suggesting strength across professional knowledge work and broad reasoning. Astra leads Terminal-Bench-Science, which measures agentic scientific research. OpenAI's documentation specifically positions Astra for research and document creation, while Anthropic highlights source-grounded reports, financial analysis and business work for Opus 5.5.

For a research deployment, test source recall, citation accuracy, numerical correctness and whether the answer covers every part of the question. A fluent report with one fabricated figure is worse than a less polished report that keeps evidence traceable.

Automation and computer use

Astra narrowly leads AutomationBench in the published table. OpenAI also gives Astra async tool calling, mid-turn steering, changeable reasoning effort during a conversation, structured outputs, computer use and a broad set of hosted tools. Those features can simplify an application that already runs on the Responses API.

Anthropic reports Opus 5.5 as its leader in computer use, but the launch table does not publish a directly comparable Astra OSWorld result. Do not turn a missing cell into a win for either model. Run your browser or desktop workflow with the same environment, timeout, retry policy and approval boundaries.

Context, documents and multimodality

OpenAI documents a 1.05-million-token context window and 128,000 maximum output tokens for Astra, with text and image input. Requests above 272,000 input tokens use higher long-context pricing for the full request. The Opus 5.5 announcement confirms document, coding and knowledge-work use cases but did not yet expose the complete platform specification table when checked, so this article avoids assigning it a context limit by analogy.

Large context windows do not eliminate context rot. Retrieval quality can decline as irrelevant material grows. Give either model a curated evidence set, preserve source locations and use compaction or staged summaries for long-running work.

Communication and control

Anthropic says Opus 5.5 puts important information first, uses less jargon and follows writing rules more reliably than Opus 5. OpenAI says Astra follows long instructions well, supports focused clarification, and can be steered during a turn. Its official guide also notes that Astra tends toward detailed, formatted answers unless the desired style is specified.

For either model, test behavior under conflicting instructions, late requirement changes, failed tools and impossible tasks. The best collaborator is the one whose mistakes your workflow can detect and recover from.

How to run a fair evaluation

1. Create 20–50 representative tasks with an objective scoring rubric.
2. Use comparable effort settings, tools, timeouts and retry limits.
3. Record the exact model, date, input and output tokens, tool charges, latency and human review time.
4. For code, run tests and inspect diffs. For research, open citations and recalculate important numbers.
5. Choose the least expensive configuration that meets your accuracy and reliability threshold.

Verdict

Opus 5.5 is the stronger default value. It wins four of the six published head-to-head rows above and charges 60% less per standard input and output token than Astra. GPT-6 Astra remains the better starting point for some scientific, automation and OpenAI-native agent workflows. The narrow benchmark gaps should be treated as ties until your own evaluation separates them.