ChatGPT Astra vs Claude: who wins each job?
GPT-6 Astra and Claude Fable 5.1 are both frontier models. They do not win for the same reasons. We compare them across eight business-critical rounds, then show you which model to choose for the work that actually matters.
The short answer
Neither model wins outright. GPT-6 Astra wins on computer use, autonomous workflows, finished business documents and system integration. Claude Fable 5.1 wins on long form writing, deep knowledge work and predictable cost at scale. Reasoning, coding and large context analysis are effectively tied.
Both list at $10 per million input tokens and $50 per million output tokens. Everything that separates them sits underneath that identical headline.
Key facts
- Claude Fable 5.1 released 1 September 2026. GPT-6 Astra released 3 September 2026. Two days apart, same headline price.
- Astra carries a 1,050,000 token context window. Fable 5.1 carries 1,000,000. Both cap output at 128,000 tokens.
- Astra bills any prompt over 272,000 input tokens at double input and 1.5 times output, applied to the entire request, not just the excess.
- Fable 5.1 cut cache reads by 75% to $0.25 per million, worth roughly 25% to 45% on agentic workloads.
- Cost per completed task is not cost per token. On independent testing, the cheaper Claude Opus 5 finished the same work for around 38% less than Fable 5.1.
- Astra is the first OpenAI model rated Critical for cybersecurity capability, and ships with tighter refusals in that area.
The scorecard
Eight rounds. Aqua marks Astra, violet marks Claude, lime marks a tied round. The colours follow the two models all the way down the page.
Built for difficult end to end work: computer use, browsing, research, code and finished professional artefacts.
wins
Built for demanding reasoning, knowledge work and long running agentic projects across large, stable context.
wins
| Category | Winner | Why |
|---|---|---|
| Complex reasoning | Draw | Different strengths, same ceiling |
| Long form writing | Claude | Holds argument across length |
| Coding | Draw | Depends on stack, harness and tooling |
| Computer use | Astra | Operates applications without an API |
| Documents and decks | Astra | Respects your existing templates |
| Large context analysis | Draw | 1.05M vs 1M, both reason well |
| Cost predictability | Claude | No hidden long context rate card |
| Business integration | Astra | Broader connected ecosystem |
| Specification | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Developer | OpenAI | Anthropic |
| Released | 3 September 2026 | 1 September 2026 |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens |
| Input price | $10 / million | $10 / million |
| Output price | $50 / million | $50 / million |
| Cached input | $1.00 / million | $0.25 / million |
| Long context penalty | 2x input, 1.5x output above 272K | None published |
| Batch pricing | 50% of standard | $5 in / $25 out |
Eight rounds. Clear winners.
Which AI is better at general reasoning?
It is a draw. Hand either model a messy business problem, a large dataset or a technical brief and you get sophisticated analysis back. Claude tends to hold context better when you want to explore a problem rather than receive a verdict. Astra reasons to a comparable standard and pulls naturally towards execution. It does not stop at recommending the fix.
Which AI is better for writing and content?
Claude wins narrowly. It is not that the copy is flashier, it is that the argument survives 2,000 words. That makes it strong for articles, strategy documents, long form reports, editing and research summaries. Astra writes well and sits inside a production ecosystem that can research, write, then build the surrounding deliverables, so it closes the gap the moment the task widens.
Which AI is better for coding?
A draw, and the benchmarks split. Fable 5.1 posts 55.8% on Terminal-Bench 4.0 with a large jump on scientific terminal work. OpenAI claims the state of the art for Astra on software engineering and computer use. Harnesses, repositories and verification loops change outcomes materially. Run the same hard task through both on your own stack and measure.
Which AI is better at computer use and autonomy?
Astra wins clearly. It is explicitly built to operate browsers, software and professional tools: filling forms, updating records, running research, moving across interfaces. That matters because real business processes do not live in one application. Traditional AI makes you carry information between stages. Agentic AI runs the chain itself.
Which AI is better for documents, decks and spreadsheets?
Astra wins. It is specifically built to follow an existing template and return slides, documents and spreadsheets that hold your layout and your tone. If you have watched someone paste AI output into a deck and spend an hour reformatting it, you understand why that is worth paying for. Claude produces strong content for these formats, but Astra produces the artefact.
Which AI handles large amounts of information better?
A draw on capability. Astra takes 1.05 million tokens, Fable 5.1 takes 1 million, and both reason genuinely well across that volume. The headline number is not the test: a million tokens is worthless if the important detail gets lost in the middle. The real difference here shows up on the invoice, not in the output.
Which AI costs less to run at scale?
Claude wins on predictability. Astra runs two rate cards, not one: any prompt over 272,000 input tokens is billed at double input and 1.5 times output across the entire request. Fable 5.1 went the other way and cut cache reads by 75% to $0.25 per million, which rewrites the arithmetic for any agent re-reading a stable knowledge base every turn.
Which AI integrates better with business systems?
Astra wins. Businesses rarely need an AI that answers questions. They need one that reaches email, calendars, files, CRM, project management and internal systems, where the work already lives. A slightly less capable model with access to the right information and permission to act will beat a smarter model working in isolation. Every time.
Independent benchmark indices
Artificial Analysis composite scores, same methodology applied to both models. Higher is better.
Worth reading carefully. On this third-party index Claude leads across all three measures, which sits at odds with OpenAI's own published comparisons. Vendor benchmarks and independent trackers use different scaffolding, safeguards and evaluation releases, so treat both as directional signals rather than a settled ranking. This is exactly why we tell clients to test on their own work.
What these models actually cost
Both list at $10 per million input tokens and $50 per million output tokens. That identical headline hides two very different commercial designs.
Cost per completed task
Artificial Analysis, maximum reasoning effort. Lower is better.
Headline token price is not cost per job. Fable 5.1 costs about 20% more per task than Fable 5, because it generates roughly 1.7 times more output. Opus 5, at $5 in and $25 out, scored three points lower on the same intelligence index and finished the work for around 38% less.
Read that twice, because it is the whole lesson. Verbosity costs money. Benchmark rank does not pay your invoice. Before you standardise on the top model, price the job, not the token.
Choose by outcome, not allegiance
The frontier is too close for blind platform loyalty. Start with the work you need done, the systems the model must reach, and the standard of proof you require.
Choose Astra when
- the task spans files, websites and applications
- you need a finished deck, document or spreadsheet
- browser and computer operation are central
- you want research converted into deliverables
- scheduled workloads can use Batch or Flex pricing
Choose Claude when
- the work is writing led or editorial
- you want a sustained strategic thinking partner
- the project runs autonomously over a long horizon
- deep research and knowledge work dominate
- heavy context reuse makes cache pricing decisive
The overall winner is your workflow
Astra is the better general work executor. Claude is the better long form thinking and writing specialist. The strongest businesses will run both, routing each task to the model that fits it.
The businesses that win with AI over the next 18 months will not be loyal to a model. They will measure cost per completed job rather than cost per million tokens, and increasingly they will not know, or need to know, which model is running underneath. What will matter is the result.
Frequently asked questions
Is GPT-6 Astra better than Claude?
Not universally. GPT-6 Astra is better at computer use, autonomous multi step workflows, and producing finished documents, slides and spreadsheets. Claude Fable 5.1 is better at long form writing, sustained knowledge work and predictable cost on agentic workloads. Reasoning, coding and large context analysis are effectively tied.
How much does GPT-6 Astra cost?
GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens on the standard API tier, with cached input at $1.00 per million. Prompts over 272,000 input tokens are billed at double the input rate and 1.5 times the output rate across the entire request. Batch and flex tiers are half price.
How much does Claude Fable 5.1 cost?
Claude Fable 5.1 costs $10 per million input tokens and $50 per million output tokens, with cache reads at $0.25 per million, a 75% reduction from Fable 5. The Batch API halves both rates to $5 and $25 per million. Claude Opus 5 sits below it at $5 input and $25 output.
What is the context window of each model?
GPT-6 Astra has a 1,050,000 token context window. Claude Fable 5.1 has a 1,000,000 token context window. Both support a maximum output of 128,000 tokens.
Which AI is better for writing blog posts and long form content?
Claude Fable 5.1, narrowly. It holds tone, structure and argument across a long piece more consistently. GPT-6 Astra writes to a similar standard and is stronger when the writing is one step in a larger production workflow that also builds the supporting documents.
Which AI is better for coding?
Too close to call from benchmarks alone. Claude Fable 5.1 posts 55.8% on Terminal-Bench 4.0 with strong scientific terminal results. GPT-6 Astra claims the state of the art on software engineering and computer use. The outcome depends more on your language, repository size, environment and tooling than on the models themselves, so test both on your own stack.
What is Claude Mythos 5.1 and can I use it?
Claude Mythos 5.1 runs the same underlying model as Claude Fable 5.1 without the production safeguards around cybersecurity, biology and chemistry. Access is restricted to a small set of vetted organisations in Anthropic's trusted access programmes. For almost every business, Fable 5.1 is the model you buy, at the same price and the same capability level.
Should my business pick one AI model or use both?
Use both, routed by task. Neither model dominates across every category, and the cost profiles reward different workload shapes. Route writing and analysis to one, workflow execution and document production to the other, and measure cost per completed job rather than cost per token.
Sources and methodology
This comparison uses official product pages, vendor published benchmarks and independent third-party tracking available on 15 September 2026. Vendor benchmarks are directional: differences in tools, scaffolding, safeguards and evaluation releases affect results.
- OpenAI: GPT-6 Astra launch, capabilities and benchmark methodology
- OpenAI API: GPT-6 Astra context, tools and pricing
- OpenAI: GPT-6 Astra system card and Preparedness Framework rating
- GPT-6 Astra rate card analysis, including the 272K context threshold
- Claude Fable 5.1 features, benchmarks and pricing
- Claude Fable and Mythos 5.1 rate card, with Artificial Analysis cost per task figures
Pricing, context windows and benchmark figures are current as at 15 September 2026. This market moves weekly. Verify against the vendors' own pricing pages before committing to a deployment budget.
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