GPT-6 Astra vs Claude Opus 5.5: Price, Context, Benchmarks and Cost Per Task

September 25, 2026. Three weeks separate the two flagship launches of the month. OpenAI released GPT-6 Astra on September 3 and called it the most intelligent and aligned model in the world. Anthropic answered on September 22 with Claude Opus 5.5, priced at less than half of Astra per token and, on Anthropic's own numbers, matching or beating it on most agentic coding and knowledge work. For an agency deciding which model sits behind a client's intake agent, document pipeline or CRM automation, the question is not which one tops a leaderboard. It is what a task costs, where the hidden pricing edges are, and which vendor's claims survive a look at the footnotes. Every figure below is read from the two companies' own pricing and model pages on September 25.

The price list, side by side

1. Base tokens. Astra is $10.00 per million input tokens and $50.00 per million output on OpenAI's standard tier. Opus 5.5 is $4.00 input and $20.00 output. Per token, Opus 5.5 is 60% cheaper on both sides.
2. Cached input. Astra bills cached reads at $1.00 per million, 10% of input. Opus 5.5 bills cache reads at $0.20, which Anthropic's pricing page lists as a 0.05x multiplier, half the usual 0.1x. Cache writes are $12.50 on Astra and $5.00 on Opus 5.5 for the five-minute cache, or $8.00 for the one-hour cache Anthropic offers and OpenAI does not.
3. Fast and batch tiers. Astra's Fast mode doubles the price to $20 and $100; Opus 5.5's Fast mode is $8 and $40 for up to 2.5x speed. Batch halves both: Astra to $5 and $25, Opus 5.5 to $2 and $10.
4. Context window. Both are one million tokens with 128,000 maximum output. The difference is what you pay for using it. OpenAI's model page states that prompts over 272K input tokens are billed at 2x input and cache rates and 1.5x output for the full request, so Astra becomes $20, $2 and $75 past that line. Anthropic's pricing page says the full 1M window is billed at standard pricing, with a 900K token request charged at the same per-token rate as a 9K request.
5. Knowledge cutoff. Astra's model page lists April 30, 2026. Anthropic's model table lists June 2026 for Opus 5.5.
6. Retirement. Anthropic commits to keep Opus 5.5 available until at least September 22, 2027. OpenAI publishes no equivalent date for Astra.

What a real task costs

Take a typical agent turn: 50,000 input tokens of which 40,000 are a cached system prompt and history, plus 5,000 output tokens. On Astra that is $0.10 for the fresh input, $0.04 for the cached read and $0.25 for the output, $0.39 in total. On Opus 5.5 it is $0.04, $0.008 and $0.10, about $0.15. Opus 5.5 runs that turn for 38% of Astra's price, and the gap widens the more the agent leans on cache, because Anthropic's cache read is a fifth of Astra's. Now take a document job: 400,000 input tokens and 4,000 output. Opus 5.5 charges $1.60 plus $0.08, $1.68. Astra crosses OpenAI's 272K threshold, so the whole request bills at the long context rate: $8.00 plus $0.30, $8.30. That is a five to one difference driven entirely by a pricing rule most comparisons never mention. Our 272K cliff explainer has the full arithmetic.

The benchmarks, with the caveats each vendor attaches

Anthropic's launch page prints a table with Astra in it. On Terminal-Bench 4.0 it reports Opus 5.5 at 66.4% against Astra at 57.9%, the Astra figure being OpenAI's own report at high effort. On FrontierCode it is 54.4% against 53.3%. On Zapier's AutomationBench, the business workflow test, Astra leads at 41.4% against Opus 5.5 at 40.0%, and Anthropic notes its runs were scored without fallback models, so safeguard interventions counted as failures. On Terminal-Bench-Science, Astra leads clearly at 64.6% against 58.7%. On Humanity's Last Exam with tools Opus 5.5 reports 67.7% against Astra's 57.2%. Anthropic's cost claims are that Opus 5.5 at default effort beats Astra on FrontierCode at roughly 20% of the cost per task and matches it on Terminal-Bench 4.0 at about 40% of the cost. OpenAI's launch page does not compare against Opus 5.5, which did not exist on September 3; it reports Astra at 64.6% on Terminal-Bench Science against 52.6% for Claude Fable 5.1 at about 31% lower estimated API cost, 59.3% on Agents' Last Exam against 55.5% for Claude Opus 5, a 99.9% score on ARC-AGI-3 and 98% on FrontierMath Tier 4. Both vendors say the same thing in different words: Anthropic writes that benchmark margins have become a less reliable guide to real-world differences, and OpenAI leans on efficiency claims like 65% fewer output tokens than Opus 5 at its best settings. Treat every number above as a vendor-run result until an independent harness reproduces it.

Where each one actually wins

Astra's launch page is built around computer use: filling forms, updating CRM records, running frontend QA, laying out a circuit board, and OpenAI reports it going beyond an authorized target 0% of the time on an internal scope test against 48% for GPT-5.6 Sol without production safeguards. If the job is an agent that drives a browser or a desktop app, OpenAI has put its evidence there. Opus 5.5's page is built around long agentic coding runs and token efficiency: a 680,000-line migration in under a day, a 200,000-line audit in under three hours where Opus 5 took over 20, output more than 30% faster than Opus 5. If the job is a long unattended engineering or document task where tokens compound, Anthropic has put its evidence there, and its flat 1M pricing means a large context never reprices the request.

What it means for operators

For most SMB and agency workloads the decision is a cost decision first, because both models clear the capability bar for intake, qualification, document extraction and CRM work. On list price Opus 5.5 is 60% cheaper per token, cheaper again on cache reads, and immune to a long-context surcharge, which is why a document-heavy build such as an AI calling and intake agent for mortgage lenders, where a single file can carry hundreds of pages of statements and disclosures, prices very differently on the two platforms. Astra earns its premium where computer use is the product, or where a workflow is already tuned to OpenAI's tools and the migration cost outweighs the token saving. The practical move is to run the same twenty tasks through both, log tokens and cost per task rather than per token, and let the invoice decide. That is the evaluation an AI automation build should start with, and the reason to hire an AI engineer who prices the cliff, the cache and the fast tier before the first prompt is written.