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BenchLM
Anthropic logo
Model A
Claude Fable 5

Anthropic

82.23/100

Supported · Public rank #3

90% interval 80.0–84.4

Claude Fable 5 vs GPT-6 Astra

Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
GPT-6 Astra

OpenAI

81.88/100

Estimated · Public rank #5

90% interval 70.4–93.4

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    GPT-6 Astra

    GPT-6 Astra has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Claude Fable 5 only
13
GPT-6 Astra only
16
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Claude Fable 5
84.6
GPT-6 Astra
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude Fable 5
89.2
GPT-6 Astra
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Fable 5
Not measured
GPT-6 Astra
95.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5
Not measured
GPT-6 Astra
96.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not measured
GPT-6 Astra
97.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
57.9
GPT-6 Astra
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Fable 5
$0.035
Fits in one request
GPT-6 Astra
$0.035
Fits in one request

Modeled costs are equal

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
GPT-6 Astra
$0.65
Fits in one request

Modeled costs are equal

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Fable 5
$0.9
Fits in one request
GPT-6 Astra
$0.9
Fits in one request

Modeled costs are equal

Costs use the listed standard API rates.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Reasoning profile

Claude Fable 5

Reasoning

GPT-6 Astra

Reasoning

Weight access

Claude Fable 5

Proprietary

GPT-6 Astra

Proprietary

License

Claude Fable 5

Proprietary

GPT-6 Astra

Proprietary

Release date

Claude Fable 5

2026-06-09

GPT-6 Astra

2026-09-03

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.65 vs $0.65. Cache-heavy agent loop: $0.9 vs $0.9.
Context tradeoff
GPT-6 Astra has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence29 rows

Agentic

  • Terminal-Bench 3.0

    Claude Fable 534.0%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • OSWorld-Verified

    Claude Fable 585%
    Source
    GPT-6 Astra—

    Not directly comparable

  • OSWorld 2.0

    Claude Fable 5—
    GPT-6 Astra72.6%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Claude Fable 5—
    GPT-6 Astra57.70%
    Source

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Fable 5—
    GPT-6 Astra64.6%
    Source

    Not directly comparable

  • ExploitGym

    Claude Fable 5—
    GPT-6 Astra42.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Fable 5—
    GPT-6 Astra59.3%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    GPT-6 Astra—

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 580%
    Source
    GPT-6 Astra—

    Not directly comparable

  • FrontierSWE v2

    Claude Fable 548.0%
    Source
    GPT-6 Astra—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • cursorBench31

    Claude Fable 570.6%
    Source
    GPT-6 Astra—

    Not directly comparable

  • cursorBench32

    Claude Fable 570.5%
    Source
    GPT-6 Astra—

    Not directly comparable

  • VulcanBench v3

    Claude Fable 589.5%
    Source
    GPT-6 Astra—

    Not directly comparable

  • deepSwe

    Claude Fable 5—
    GPT-6 Astra74.1%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude Fable 5—
    GPT-6 Astra95%
    Source

    Not directly comparable

  • ARC-AGI-3

    Claude Fable 5—
    GPT-6 Astra62.7%
    Source

    Not directly comparable

  • MRCR v2 256K-512K

    Claude Fable 5—
    GPT-6 Astra100.0%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    Claude Fable 5—
    GPT-6 Astra96.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Fable 5—
    GPT-6 Astra96%
    Source

    Not directly comparable

  • GPQA-D

    Claude Fable 5—
    GPT-6 Astra96.0%
    Source

    Not directly comparable

  • HealthBench Professional

    Claude Fable 5—
    GPT-6 Astra63.4%
    Source

    Not directly comparable

  • HealthBench Hard

    Claude Fable 5—
    GPT-6 Astra36.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tier 4)

    Claude Fable 5—
    GPT-6 Astra97.600%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    GPT-6 Astra—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    GPT-6 Astra—

    Not directly comparable

  • ScreenSpot Pro

    Claude Fable 5—
    GPT-6 Astra92.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or GPT-6 Astra?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Fable 5 or GPT-6 Astra?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Claude Fable 5 or GPT-6 Astra?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Claude Fable 5 or GPT-6 Astra?

For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.035 on GPT-6 Astra; repository review costs $0.65 and $0.65; the cache-heavy agent loop costs $0.9 and $0.9. Costs use the listed standard API rates.

Which has the larger context window, Claude Fable 5 or GPT-6 Astra?

GPT-6 Astra has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 3, 2026

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