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BenchLM
Anthropic logo
Model A
Claude Opus 4.7 (Adaptive)

Anthropic

71.62/100

Estimated · Public rank #24

90% interval 61.8–81.5

Claude Opus 4.7 (Adaptive) 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

GPT-6 Astra has the higher public score estimate, 81.88 versus 71.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Opus 4.7 (Adaptive)

    Claude Opus 4.7 (Adaptive) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    GPT-6 Astra

    GPT-6 Astra has the lower estimated token cost for this stated workload. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Claude Opus 4.7 (Adaptive)

    Claude Opus 4.7 (Adaptive) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • 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

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
5
Claude Opus 4.7 (Adaptive) only
16
GPT-6 Astra only
11
Like-for-like categories
1 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Reasoning

Like-for-like
Claude Opus 4.7 (Adaptive)
75.8
GPT-6 Astra
95.0
Weighted basis
1 vs 1 rows
Reading
GPT-6 Astra leads

Knowledge

Directional only
Claude Opus 4.7 (Adaptive)
60.0
GPT-6 Astra
96.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Claude Opus 4.7 (Adaptive)
75.1
GPT-6 Astra
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 4.7 (Adaptive)
78.6
GPT-6 Astra
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7 (Adaptive)
Not measured
GPT-6 Astra
97.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7 (Adaptive)
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7 (Adaptive)
65.1
GPT-6 Astra
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7 (Adaptive)
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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 Opus 4.7 (Adaptive)
$0.0175
Fits in one request
GPT-6 Astra
$0.035
Fits in one request

Claude Opus 4.7 (Adaptive) has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7 (Adaptive)
$0.325
Fits in one request
GPT-6 Astra
$0.65
Fits in one request

Claude Opus 4.7 (Adaptive) has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 4.7 (Adaptive)
$1.35
Fits in one request
Cached input priced at the published list-input rate
GPT-6 Astra
$0.9
Fits in one request

GPT-6 Astra has the lower modeled cost

Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

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

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Opus 4.7 (Adaptive)

Not published

GPT-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

Provider availability

Claude Opus 4.7 (Adaptive)

Not sourced

GPT-6 Astra

Limited Availability · OpenAI Responses API, ChatGPT Plus, Pro, Business, and Enterprise

OpenAI GPT-6 Astra model documentation

Reasoning profile

Claude Opus 4.7 (Adaptive)

Reasoning

GPT-6 Astra

Reasoning

Weight access

Claude Opus 4.7 (Adaptive)

Proprietary

GPT-6 Astra

Proprietary

License

Claude Opus 4.7 (Adaptive)

Proprietary

GPT-6 Astra

Proprietary

Release date

Claude Opus 4.7 (Adaptive)

2026-04-16

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
GPT-6 Astra has the higher public score estimate, 81.88 versus 71.62, but the 90% score intervals overlap.
Workload cost
Repository review: $0.325 vs $0.65. Cache-heavy agent loop: $1.35 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 evidence32 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    GPT-6 Astra—

    Not directly comparable

  • BrowseComp

    Claude Opus 4.7 (Adaptive)79.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.7 (Adaptive)77.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.7 (Adaptive)78%
    Source
    GPT-6 Astra—

    Not directly comparable

  • CyberGym

    Claude Opus 4.7 (Adaptive)73.1%
    Source
    GPT-6 Astra—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.7 (Adaptive)18.2%
    Source
    GPT-6 Astra72.6%
    Source

    GPT-6 Astra leads this result

  • JobBench

    Claude Opus 4.7 (Adaptive)45.9%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Terminal-Bench 4.0

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra57.70%
    Source

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra64.6%
    Source

    Not directly comparable

  • ExploitGym

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra42.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra59.3%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.7 (Adaptive)87.6%
    Source
    GPT-6 Astra—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.7 (Adaptive)64.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    GPT-6 Astra—

    Not directly comparable

  • deepSwe

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra74.1%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 128K-256K

    Claude Opus 4.7 (Adaptive)59.2%
    Source
    GPT-6 Astra—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 4.7 (Adaptive)75.8%
    Source
    GPT-6 Astra95%
    Source

    GPT-6 Astra leads this result

  • ARC-AGI-3

    Claude Opus 4.7 (Adaptive)0.2%
    Source
    GPT-6 Astra62.7%
    Source

    GPT-6 Astra leads this result

  • MRCR v2 256K-512K

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra100.0%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra96.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    GPT-6 Astra96%
    Source

    GPT-6 Astra leads this result

  • GPQA-D

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    GPT-6 Astra96.0%
    Source

    GPT-6 Astra leads this result

  • HLE

    Claude Opus 4.7 (Adaptive)54.7%
    Source
    GPT-6 Astra—

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.7 (Adaptive)46.9%
    Source
    GPT-6 Astra—

    Not directly comparable

  • HealthBench Professional

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra63.4%
    Source

    Not directly comparable

  • HealthBench Hard

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra36.3%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    Claude Opus 4.7 (Adaptive)43.8%
    Source
    GPT-6 Astra—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra97.600%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude Opus 4.7 (Adaptive)43.6%
    Source
    GPT-6 Astra—

    Not directly comparable

  • CharXiv

    Claude Opus 4.7 (Adaptive)91%
    Source
    GPT-6 Astra—

    Not directly comparable

  • CharXiv w/o tools

    Claude Opus 4.7 (Adaptive)82.1%
    Source
    GPT-6 Astra—

    Not directly comparable

  • ScreenSpot Pro

    Claude Opus 4.7 (Adaptive)—
    GPT-6 Astra92.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 (Adaptive) or GPT-6 Astra?

GPT-6 Astra has the higher public score estimate, 81.88 versus 71.62, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Opus 4.7 (Adaptive) 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 Opus 4.7 (Adaptive) 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 Opus 4.7 (Adaptive) or GPT-6 Astra?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.035 on GPT-6 Astra; repository review costs $0.325 and $0.65; the cache-heavy agent loop costs $1.35 and $0.9. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.7 (Adaptive) 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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