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Radar

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BenchLM
Anthropic logo
Model A
Claude Opus 4.7

Anthropic

70.22/100

Supported · Public rank #18

90% interval 63.9–76.6

Claude Opus 4.7 vs GPT-4.1

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
GPT-4.1

OpenAI

43.81/100

Supported · Public rank #174

90% interval 29.5–58.1

Decision reading

Claude Opus 4.7 has the higher public score, 70.22 versus 43.81, and the 90% score intervals do not overlap.

3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Opus 4.7

    Claude Opus 4.7 leads on the public coding lane, 62.7 to 38.8, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-4.1

    GPT-4.1 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-4.1

    GPT-4.1 has the lower estimated token cost for this stated workload. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. GPT-4.1 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

    GPT-4.1

    GPT-4.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Agentic work

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

    Not enough matched evidence

    GPT-4.1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
3
Claude Opus 4.7 only
10
GPT-4.1 only
4
Like-for-like categories
1 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Like-for-like
Claude Opus 4.7
62.7
Supported · #15/183
GPT-4.1
38.8
Supported · #143/183
Basis
BenchAlign lane · 5 vs 1 public rows
Reading
Claude Opus 4.7 leads

Knowledge

Directional only
Claude Opus 4.7
64.9
Estimated · #26/181
GPT-4.1
40.7
Supported · #135/181
Basis
BenchAlign lane · 2 vs 2 public rows
Reading
Directional only

Agentic

Not comparable
Claude Opus 4.7
58.5
Supported · #32/151
GPT-4.1
Not ranked
Basis
BenchAlign lane · 4 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.7
Not ranked
GPT-4.1
67.2
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7
60.8
Unranked · 2 rankable rows
GPT-4.1
28.1
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7
Not ranked
GPT-4.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7
Not ranked
GPT-4.1
50.2
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7
Not ranked
GPT-4.1
50.3
#80/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

  • FrontierMath v2 (Tiers 1-3)

    Math

    Claude Opus 4.7: 43.793%GPT-4.1: 5.517%Normalized gap 38.3Shared source

  • FrontierMath v2 (Tier 4)

    Math

    Claude Opus 4.7: 22.917%GPT-4.1: 0.000%Normalized gap 22.9Shared source

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
$0.0175
Fits in one request
GPT-4.1
$0.006
Fits in one request

GPT-4.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7
$0.325
Fits in one request
GPT-4.1
$0.124
Fits in one request

GPT-4.1 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
$1.35
Fits in one request
Cached input priced at the published list-input rate
GPT-4.1
$0.52
Fits in one request
Cached input priced at the published list-input rate

GPT-4.1 has the lower modeled cost

Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. GPT-4.1 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.

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 4.7

1M

Anthropic model overview

GPT-4.1

1M

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

Not published

GPT-4.1

Not published

Reasoning profile

Claude Opus 4.7

Non-Reasoning

GPT-4.1

Non-Reasoning

Weight access

Claude Opus 4.7

Proprietary

GPT-4.1

Proprietary

License

Claude Opus 4.7

Proprietary

GPT-4.1

Proprietary

Release date

Claude Opus 4.7

2026-04-16

GPT-4.1

2025-04-14

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
Claude Opus 4.7 has the higher public score, 70.22 versus 43.81, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.325 vs $0.124. Cache-heavy agent loop: $1.35 vs $0.52.
Context tradeoff
Both models list 1M.

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 evidence17 rows

Agentic

  • Gert Labs

    Shared source
    Claude Opus 4.765.59%
    GPT-4.125.65%

    Claude Opus 4.7 leads this result

  • ResearchClawBench

    Claude Opus 4.720.7%
    Source
    GPT-4.1—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.713.9%
    Source
    GPT-4.1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Opus 4.768.5%
    Source
    GPT-4.1—

    Not directly comparable

Coding

  • Vibe Code Bench

    Claude Opus 4.771.00%
    Source
    GPT-4.1—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.782.8%
    Source
    GPT-4.1—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.738.5%
    Source
    GPT-4.1—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Opus 4.785.1%
    Source
    GPT-4.1—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Opus 4.782.0%
    Source
    GPT-4.1—

    Not directly comparable

  • SWE-bench Verified

    Claude Opus 4.7—
    GPT-4.154.6%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Opus 4.790.2%
    Source
    GPT-4.1—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Opus 4.789.9%
    Source
    GPT-4.1—

    Not directly comparable

  • MMLU

    Claude Opus 4.7—
    GPT-4.190.2%
    Source

    Not directly comparable

  • GPQA

    Claude Opus 4.7—
    GPT-4.166.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Claude Opus 4.743.793%
    GPT-4.15.517%

    Claude Opus 4.7 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Claude Opus 4.722.917%
    GPT-4.10.000%

    Claude Opus 4.7 leads this result

Instruction following

  • IFEval

    Claude Opus 4.7—
    GPT-4.187.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 or GPT-4.1?

Claude Opus 4.7 has the higher public score, 70.22 versus 43.81, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Opus 4.7 or GPT-4.1?

Claude Opus 4.7 leads the public coding lane, 62.7 to 38.8, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Opus 4.7 or GPT-4.1?

GPT-4.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Opus 4.7 or GPT-4.1?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.006 on GPT-4.1; repository review costs $0.325 and $0.124; the cache-heavy agent loop costs $1.35 and $0.52. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.7 or GPT-4.1?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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