GPT-6 Astra vs Claude Fable 5.1: Benchmarks, API Cost, and Real-World Use Cases

People searching for gpt 6 astra claude or claude astra are usually trying to compare OpenAI’s GPT-6 Astra with Anthropic’s Claude Fable 5.1. One point is worth clearing up immediately: Astra is an OpenAI model, not a Claude model. There is no current model called “Claude Astra.”

Both models have the same first-party list price: $10 per million input tokens and $50 per million output tokens. That does not make their real bills—or their results—the same. Prompt caching, the 272K long-input threshold, reasoning effort, token usage, and the agent harness can all change the outcome.

Practical answer

• Start with GPT-6 Astra for tool-heavy workflows, computer use, terminal tasks, and jobs that benefit from steering the agent while it is still working.
• Start with Claude Fable 5.1 for repeatedly reused large contexts, very long inputs, long-running autonomous work, and workloads that value higher output throughput.
• For coding, test both on your own repository. A model inside Codex or Claude Code is part of a larger system, so leaderboard scores do not transfer cleanly across tools.

Specifications, pricing, and public evaluation data in this article were checked on September 18, 2026. Recheck current provider and BetterToken pricing before moving a workload into production.

30-second comparison

Dimension	GPT-6 Astra	Claude Fable 5.1
Provider	OpenAI	Anthropic
Model ID	gpt-6-astra	claude-fable-5-1
Context / maximum output	1,050,000 / 128,000 tokens	1,000,000 / 128,000 tokens
Standard input / output	$10 / $50 per 1M tokens	$10 / $50 per 1M tokens
Prompt cache write	$12.50 per 1M tokens	$12.50 for 5 minutes; $20 for 1 hour per 1M tokens
Prompt cache read	$1 per 1M tokens	$0.25 per 1M tokens
Long-input rule	Above 272K input, the whole request is billed at 2× input/cache and 1.5× output	Standard pricing through the 1M context window
Independent Intelligence Index	53	53
Independent measured output speed	About 51.3 tokens/s	About 66.4 tokens/s
Best first test	Tool-heavy agents, computer use, interactive steering, terminal and scientific workflows	Reused long context, long-horizon agents, cache-heavy jobs, higher output throughput

This is not a universal ranking. It is a routing guide: choose the first candidate from the shape of the task, then validate it with your own inputs, tools, and acceptance criteria.

Why identical list prices produce different bills

1. A one-off standard request costs the same

For a request with 100K uncached input tokens and 10K output tokens:

Input: 0.1 × $10 = $1.00
Output: 0.01 × $50 = $0.50
Total: $1.50

At this point, the models cost the same. The difference appears when you reuse context, cross Astra’s 272K threshold, choose different effort levels, or one model consumes more tokens to finish the job.

2. Reusing a 100K prefix favors Fable’s cache-read price

Assume the first request writes a shared 100K-token prefix to cache, nine later requests read the full prefix from cache, and total output across the run is 50K tokens. For Fable, use the five-minute cache and assume each read lands inside the TTL. Each Astra request stays below 272K input.

Astra: 0.1 × $12.50 + 0.9 × $1.00 + 0.05 × $50 = $4.65
Fable: 0.1 × $12.50 + 0.9 × $0.25 + 0.05 × $50 = $3.975 ≈ $3.98

This calculation covers the shared prefix and output only; new input on each turn would be additional. Fable’s advantage depends on real cache hits. If the prefix changes, the TTL expires, or the application never reuses the exact cached segment, it must be written again.

3. A 300K input changes the economics sharply

For one request with 300K uncached input tokens and 20K output tokens:

Astra: 0.3 × $20 + 0.02 × $75 = $7.50
Fable: 0.3 × $10 + 0.02 × $50 = $4.00

Once Astra exceeds 272K input tokens, the surcharge applies to the entire request, not only the 28K tokens above the threshold. Input and cache rates double, while output rises to 1.5 times the standard rate. Loading a whole repository, a large contract set, or a research corpus can therefore produce a very different bill despite identical headline prices.

4. “Cheaper per token” and “cheaper per task” are not the same claim

Artificial Analysis reports two results that can look contradictory:

• At low reasoning effort, GPT-6 Astra costs about $0.82 per evaluation task, versus about $2.37 for Claude Fable 5.1.
• At max effort, using a representative 7:2:1 cache-hit/input/output token mix, Fable’s weighted cost is about $7.17 per 1M tokens, versus $7.70 for Astra.

Both can be true. Cost per task depends on success rate, reasoning length, and the selected effort. Weighted token cost assumes a fixed traffic mix. Production decisions should measure the cost of one acceptable outcome, not just a price-card row.

What the benchmarks actually say

Direct comparisons published by OpenAI

OpenAI’s GPT-6 Astra launch page includes these head-to-head results:

Benchmark	GPT-6 Astra	Claude Fable 5.1	Lead
AutomationBench	41.4%	31.4%	Astra
Terminal-Bench 4.0	57.9%	55.8%	Astra
DeepSWE v1.1	74.1%	67.4%	Astra
Terminal-Bench Science 0.1	64.6%	52.6%	Astra
GPQA Diamond	96.0%	93.7%	Astra
Humanity’s Last Exam with tools	57.2%	65.0%	Fable

These figures make Astra a strong first candidate for automation, terminal work, software engineering, and tool-assisted scientific workflows. They are still vendor-published results. Harnesses, tools, effort settings, prompts, and retry policies can differ, so the table should be treated as directional evidence rather than a universal verdict.

Independent comparison: the same overall index, different strengths

Artificial Analysis v4.3 gives both models an Intelligence Index of 53. On the same comparison page:

• Claude Fable 5.1 generates about 66.4 tokens per second, versus about 51.3 tokens per second for Astra at max effort.
• Astra leads on parts of the agent evaluation set, including AutomationBench-AA and Terminal-Bench 4.0.
• Fable leads on some reasoning-heavy measures, including SciCode and Humanity’s Last Exam.
• Results move with effort, fallback behavior, the tool environment, and the surrounding agent system.

The useful interpretation is not “53 equals 53, so they are interchangeable.” It is that the models sit in the same capability tier but distribute their strengths differently.

Choose by workload, not by logo

Start with GPT-6 Astra when

1. The workflow uses many tools and a person may intervene mid-run. OpenAI highlights asynchronous tool calls and mid-turn steering for Astra. In interactive agents, control during execution can matter more than a static answer score.
2. The job is terminal-heavy or automation-heavy. Astra’s Terminal-Bench and AutomationBench results make it a sensible first test for system configuration, operational workflows, and computer-use tasks.
3. The task mixes reasoning with scientific or professional software. Terminal-Bench Science, GPQA, and BenchCAD point to strong potential in tool-assisted engineering and scientific work.
4. You want a low-cost first pass at lower effort. Independent task-level measurements favor Astra at low effort, although real success rate and rework still need to be measured.

Start with Claude Fable 5.1 when

1. A large prefix is reused repeatedly. Common examples are a repository index, a product specification, a knowledge base, or a contract template queried across many turns. Fable’s cache-read price is one quarter of Astra’s.
2. Requests regularly exceed 272K input tokens. Fable does not apply Astra’s whole-request surcharge within its 1M context window.
3. The agent must work for a long time without supervision. Anthropic positions Fable 5.1 for demanding reasoning and long-horizon agentic work, including research and cross-system investigation.
4. Output throughput matters. In the independent comparison configuration, Fable produced tokens faster, which can help long-form generation and high-throughput pipelines.

For coding, compare “model + agent,” not only the model

A coding agent is not a single API call. Codex, Claude Code, Cursor, and custom agents decide:

• how the system prompt is assembled;
• which files enter context;
• when tools are called;
• whether failures trigger retries;
• how tests are run and interpreted;
• when context is compressed or cached.

An Astra score inside Codex and a Fable score inside Claude Code therefore compare two systems. Record the tool and version when you evaluate a model for production coding.

An important API difference: tool_choice

Claude Fable 5.1 does not support forced tool use. These settings return 400 invalid_request_error:

{"tool_choice":{"type":"any"}}

and:

{"tool_choice":{"type":"tool","name":"your_tool"}}

Supported values are auto and none. If tool arguments must follow a schema, use auto with strict tool use. If the response itself must follow a fixed JSON shape, use structured outputs.

For an application that relies on “this turn must call this exact tool,” this constraint can matter more than a benchmark lead. Moving between Astra and Fable is not always a one-line Model ID replacement.

Run a fair A/B test on your own work

Do not ask one broad question once and choose a winner. Select three to five tasks you would actually pay to run, then fix the inputs and acceptance criteria.

Record at least:

Metric	What to capture
Completion	Whether every acceptance condition is met, not whether the answer sounds polished
Automated tests	Passing and failing tests, plus any unrelated changes
Human rework	Follow-up prompts, manual edits, and review time
End-to-end time	From task submission to an acceptable result
Tokens	Input, output, cache write, and cache read separately
Total cost	Cost per accepted outcome, not only per request
Stability	Repeat each important task at least twice and note large variance

Localized prompt example: fixing a code defect

Goal: Fix duplicate processing of payment callbacks.

Allowed scope: Change only the callback idempotency logic and its related tests.

Do not: Refactor the payment module, upgrade dependencies, or change other payment channels.

Done when: Repeated delivery of the same callback credits the account only once, while existing valid callbacks behave exactly as before.

Verification: Run only the payment-callback tests. Stop after they pass; do not run an unrelated full test suite.

Use the same repository revision, branch, tool permissions, and test commands for both models. The winner is the system that delivers a mergeable change with less rework—not the one that writes the most confident explanation.

Localized prompt example: analyzing a long document set

Read the supplied agreement and attachments, and answer only these questions:

1. What triggers automatic renewal, and what is the latest cancellation deadline?
2. How much advance notice is required for a price change?
3. Which clauses could expose us to uncapped liability?

For every conclusion, cite the file name and clause number. If the material does not establish an answer, write “not found in the supplied material” instead of guessing. Finish with an action list ranked high, medium, and low risk.

This task tests long-context retrieval, evidence location, citation accuracy, and actionability. If the same corpus will receive repeated questions, capture the cache-hit rate as well.

Localized prompt example: a multi-step agent task

Investigate why the most recent deployment of this service failed.

Read the deployment logs and relevant configuration first, then identify the most likely root cause. Change code or configuration only when the evidence is sufficient. After any change, run the most relevant verification and summarize the change, the result, and any remaining risk. If permissions or information are missing, state exactly what is missing and do not invent an outcome.

This exposes tool selection, mid-run correction, recovery from errors, and the quality of the final evidence chain—not just prose quality.

Comparing both models through BetterToken

The current BetterToken catalog uses these exact Model IDs:

Model	Model ID	API key group	Typical protocol
GPT-6 Astra	gpt-6-astra	GPT	OpenAI-compatible / Responses
Claude Fable 5.1	claude-fable-5-1	Claude	Anthropic-compatible; OpenAI-compatible in selected tools

Do not apply one Base URL to every client:

• For Codex over OpenAI Responses, BetterToken documentation uses https://www.bettertoken.ai/v1.
• For Claude Code over Anthropic Messages, ANTHROPIC_BASE_URL is https://bettertoken.ai without /v1.

Create the required API key group, follow the client-specific guide, and record model, request status, input, output, cache, and cost in the dashboard. Keep the task material identical and include the client name and version in the test record.

Final decision matrix

Main requirement	First model to test	Why
Complex tools, computer use, terminal automation	GPT-6 Astra	Strong tool and terminal evaluations; mid-turn steering
Repeated queries over a large repository or knowledge base	Claude Fable 5.1	Lower cache-read price
Inputs regularly above 272K	Claude Fable 5.1	No Astra whole-request long-input surcharge
Lower cost per task at low effort	GPT-6 Astra	Better independent low-effort task cost
Higher output throughput	Claude Fable 5.1	Faster output in the independent comparison
Scientific, CAD, and professional-software workflows	GPT-6 Astra	Strong vendor-published tool-assisted results
Long autonomous research or cross-system investigation	Claude Fable 5.1	Designed for long-horizon agentic work and friendlier long-context economics
Production coding	Test both	Repository and agent harness often matter more than the aggregate score

Frequently asked questions

What is Claude Astra?

There is no current model named Claude Astra. Astra refers to OpenAI’s GPT-6 Astra; the Anthropic model in this comparison is Claude Fable 5.1. claude astra is usually a mixed search phrase rather than a product name.

Which is more intelligent, GPT-6 Astra or Claude Fable 5.1?

Artificial Analysis v4.3 gives both an Intelligence Index of 53. Vendor benchmarks and individual tasks split in different directions, so one aggregate score cannot replace a workload-specific test.

Which API is cheaper?

Standard input and output list prices are identical. Repeated cache reads and inputs above 272K usually favor Fable, while independent low-effort task measurements favor Astra. Compare the total cost of an accepted result.

Which is better for coding?

There is no answer that holds for every repository. Astra can be strong in Codex and Fable in Claude Code. Test the same branch, permissions, task, and verification commands, then count human rework.

Do both support about one million tokens of context?

Yes. Astra supports 1,050,000 tokens and Fable supports 1,000,000; both list a 128,000-token maximum output. Astra’s higher rates apply to the full request when input exceeds 272K.

Can Fable 5.1 be forced to call a specific tool?

No. tool_choice values any and a named tool return a 400 error. Use auto or none, and validate this behavior before migrating an existing agent.

Can I test both through BetterToken?

Yes. Use the exact Model ID and matching API key group, then configure the protocol and Base URL for Codex, Claude Code, or your selected client. Use the current BetterToken pricing page and dashboard charges as the billing source of truth.

Sources

• OpenAI: Introducing GPT-6 Astra
• OpenAI API: GPT-6 Astra specifications and pricing
• Anthropic: Claude Fable 5.1 and Claude Mythos 5.1
• Claude Platform: model overview
• Claude Platform: API pricing
• Claude Platform: Fable 5.1 tool_choice limitation
• Artificial Analysis: GPT-6 Astra vs Claude Fable 5.1
• BetterToken: models and pricing
• BetterToken documentation: Codex
• BetterToken documentation: Claude Code