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BenchLM

Claude Opus 5.5 vs GPT-5.1

Updated September 23, 2026. Rank says Claude Opus 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo
Claude Opus 5.5

Anthropic

88.45/100

Estimated · Public rank #2

90% interval 76.9–100.0

Model B
OpenAI logo
GPT-5.1

OpenAI

58.29/100

Supported · Public rank #46

90% interval 48.2–68.4

Shared results
0
Claude Opus 5.5 only
47
GPT-5.1 only
4
Like-for-like categories
1 / 8
Estimated: Claude Opus 5.5 · Supported: GPT-5.1How the comparison works

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 5.5

    Claude Opus 5.5 leads on the public coding lane, 87.1 to 39.8, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    Claude Opus 5.5

    Claude Opus 5.5 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.1

    GPT-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Cache-heavy agent loop cost

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

    Claude Opus 5.5

    Claude Opus 5.5 has the lower estimated token cost for this stated workload. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.1

    GPT-5.1 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-5.1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

87.1Claude Opus 5.539.8GPT-5.1

Like-for-like · BenchAlign v5.6

Claude Opus 5.5 leads the like-for-like coding row.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

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

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.6 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 5.5
87.1
Supported · #1/135
GPT-5.1
39.8
Supported · #62/135
Basis
BenchAlign v5.6 lane · 9 vs 1 public rows
Reading
Claude Opus 5.5 leads

Knowledge

Directional only
Claude Opus 5.5
90.0
Supported · #1/160
GPT-5.1
51.4
Estimated · #50/160
Basis
BenchAlign v5.6 lane · 16 vs 0 public rows
Reading
Directional only

Agentic

Not comparable
Claude Opus 5.5
88.8
Supported · #1/105
GPT-5.1
Not ranked
Basis
BenchAlign v5.6 lane · 11 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 5.5
78.5
#4/18
GPT-5.1
76.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 5.5
88.8
#3/50
GPT-5.1
71.3
Unranked · 1 rankable row
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 5.5
Not ranked
GPT-5.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 5.5
Not ranked
GPT-5.1
87.9
#27/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 5.5
Not ranked
GPT-5.1
49.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 5.5
$0.014
Fits in one request
GPT-5.1
$0.00625
Fits in one request

GPT-5.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5.5
$0.26
Fits in one request
GPT-5.1
$0.0925
Fits in one request

GPT-5.1 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 5.5
$0.32
Fits in one request
GPT-5.1
$0.375
Fits in one request
Cached input priced at the published list-input rate

Claude Opus 5.5 has the lower modeled cost

GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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 5.5

$0.2 per 1M cached input tokens

Claude Opus 5.5 model documentation

GPT-5.1

Not published

Reasoning profile

Claude Opus 5.5

Reasoning

GPT-5.1

Reasoning

Weight access

Claude Opus 5.5

Proprietary

GPT-5.1

Proprietary

License

Claude Opus 5.5

Proprietary

GPT-5.1

Proprietary

Release date

Claude Opus 5.5

2026-09-22

GPT-5.1

2025-11-13

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: $0.26 vs $0.0925. Cache-heavy agent loop: $0.32 vs $0.375.
Context tradeoff
Claude Opus 5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 5.5 or GPT-5.1?

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 Opus 5.5 or GPT-5.1?

Claude Opus 5.5 leads the public coding lane, 87.1 to 39.8, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Opus 5.5 or GPT-5.1?

GPT-5.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Opus 5.5 or GPT-5.1?

For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.00625 on GPT-5.1; repository review costs $0.26 and $0.0925; the cache-heavy agent loop costs $0.32 and $0.375. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 5.5 or GPT-5.1?

Claude Opus 5.5 has the larger documented context window: 1M, compared with 400K.

Benchmark evidence

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

Browse raw public benchmark evidence51 rows

Agentic

  • Terminal-Bench 4.0

    Claude Opus 5.566.40%
    Source
    GPT-5.1—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Opus 5.558.7%
    Source
    GPT-5.1—

    Not directly comparable

  • AutomationBench

    Claude Opus 5.540.0%
    Source
    GPT-5.1—

    Not directly comparable

  • HLE w/ tools

    Claude Opus 5.567.7%
    Source
    GPT-5.1—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 5.548.7%
    Source
    GPT-5.1—

    Not directly comparable

  • LAB all-pass (Harvey held-out)

    Claude Opus 5.58.3%
    Source
    GPT-5.1—

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    Claude Opus 5.591.2%
    Source
    GPT-5.1—

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 5.577.8%
    Source
    GPT-5.1—

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Opus 5.582.4%
    Source
    GPT-5.1—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Opus 5.572.2%
    Source
    GPT-5.1—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Opus 5.526.9 turns
    Source
    GPT-5.1—

    Not directly comparable

  • Gert Labs

    Claude Opus 5.5—
    GPT-5.141.24%
    Source

    Not directly comparable

Coding

  • FrontierCode 1.1 Main

    Claude Opus 5.554.4%
    Source
    GPT-5.1—

    Not directly comparable

  • cursorBench40

    Claude Opus 5.557.8%
    Source
    GPT-5.1—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 5.589.9%
    Source
    GPT-5.1—

    Not directly comparable

  • SWE Multilingual

    Claude Opus 5.593.9%
    Source
    GPT-5.1—

    Not directly comparable

  • SWE Multimodal

    Claude Opus 5.561.4%
    Source
    GPT-5.1—

    Not directly comparable

  • DeepSWE

    Claude Opus 5.574.2%
    Source
    GPT-5.1—

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Opus 5.563.6%
    Source
    GPT-5.1—

    Not directly comparable

  • FrontierSWE v2

    Claude Opus 5.562.3%
    Source
    GPT-5.1—

    Not directly comparable

  • ProgramBench

    Claude Opus 5.591.2%
    Source
    GPT-5.1—

    Not directly comparable

  • Vibe Code Bench

    Claude Opus 5.5—
    GPT-5.124.61%
    Source

    Not directly comparable

Multimodal

  • Chartography (tools)

    Claude Opus 5.589.0%
    Source
    GPT-5.1—

    Not directly comparable

  • Chartography (no tools)

    Claude Opus 5.564.4%
    Source
    GPT-5.1—

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    Claude Opus 5.50.730
    Source
    GPT-5.1—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Claude Opus 5.50.962
    Source
    GPT-5.1—

    Not directly comparable

  • Biomedical image analysis

    Claude Opus 5.571.4%
    Source
    GPT-5.1—

    Not directly comparable

  • OfficeQA

    Claude Opus 5.578.9%
    Source
    GPT-5.1—

    Not directly comparable

  • OfficeQA Pro

    Claude Opus 5.567.7%
    Source
    GPT-5.1—

    Not directly comparable

Knowledge

  • HLE w/o tools

    Claude Opus 5.564.4%
    Source
    GPT-5.1—

    Not directly comparable

  • HealthBench (raw)

    Claude Opus 5.568.1%
    Source
    GPT-5.1—

    Not directly comparable

  • HealthBench (length-adjusted)

    Claude Opus 5.560.6%
    Source
    GPT-5.1—

    Not directly comparable

  • HealthBench Professional

    Claude Opus 5.565.6%
    Source
    GPT-5.1—

    Not directly comparable

  • HealthBench Professional (raw)

    Claude Opus 5.577.1%
    Source
    GPT-5.1—

    Not directly comparable

  • BioMysteryBench (human-solvable)

    Claude Opus 5.589.3%
    Source
    GPT-5.1—

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Claude Opus 5.550.0%
    Source
    GPT-5.1—

    Not directly comparable

  • SpatialBench Verified

    Claude Opus 5.572.0%
    Source
    GPT-5.1—

    Not directly comparable

  • SingleCellBench

    Claude Opus 5.561.2%
    Source
    GPT-5.1—

    Not directly comparable

  • Morphology-to-molecule matching

    Claude Opus 5.534.0%
    Source
    GPT-5.1—

    Not directly comparable

  • Medicinal chemistry

    Claude Opus 5.563.5%
    Source
    GPT-5.1—

    Not directly comparable

  • Protein Design

    Claude Opus 5.560.2%
    Source
    GPT-5.1—

    Not directly comparable

  • Protein Design library ranking

    Claude Opus 5.556.0%
    Source
    GPT-5.1—

    Not directly comparable

  • De novo protein-binder design

    Claude Opus 5.582.6%
    Source
    GPT-5.1—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Opus 5.573.7%
    Source
    GPT-5.1—

    Not directly comparable

  • Protocols (understanding)

    Claude Opus 5.569.0%
    Source
    GPT-5.1—

    Not directly comparable

Multilingual

  • GMMLU

    Claude Opus 5.594.3%
    Source
    GPT-5.1—

    Not directly comparable

  • MILU

    Claude Opus 5.593.1%
    Source
    GPT-5.1—

    Not directly comparable

Math

  • ArXivMath Aug. 2026 (no tools)

    Claude Opus 5.591.2%
    Source
    GPT-5.1—

    Not directly comparable

  • ArXivMath Aug. 2026 (tools)

    Claude Opus 5.596.9%
    Source
    GPT-5.1—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 5.5—
    GPT-5.131.034%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 5.5—
    GPT-5.112.500%
    Source

    Not directly comparable

51 public results · 0 shared

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Last updated September 23, 2026