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BenchLM

Claude Sonnet 5.5 vs GPT-6.1 Sol

Updated September 29, 2026. Rank says Claude Sonnet 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

Claude Sonnet 5.5 has the higher public score estimate, 80.9 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 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 Sonnet 5.5

Anthropic

80.9/100

Estimated · Public rank #4

90% interval 69.4–92.4

Model B
OpenAI logo
GPT-6.1 Sol

OpenAI

66.86/100

Estimated · Public rank #23

90% interval 55.4–78.4

Shared results
6
Claude Sonnet 5.5 only
41
GPT-6.1 Sol only
3
Like-for-like categories
1 / 8
Estimated: Claude Sonnet 5.5 and GPT-6.1 SolHow 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 Sonnet 5.5

    Claude Sonnet 5.5 leads on the public coding lane, 85.1 to 67, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GPT-6.1 Sol

    GPT-6.1 Sol has the larger documented context window.

    Confidence: documented

  • Cache-heavy agent loop cost

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

    GPT-6.1 Sol

    GPT-6.1 Sol 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

  • Agentic work

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

    Not enough matched evidence

    GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

  • 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

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.

85.1Claude Sonnet 5.567.0GPT-6.1 Sol

Like-for-like · BenchAlign v5.7

Claude Sonnet 5.5 leads the like-for-like coding row, although the 90% intervals overlap.

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 v5.7 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 Sonnet 5.5
85.1
Supported · #1/143
GPT-6.1 Sol
67.0
Supported · #8/143
Basis
BenchAlign v5.7 lane · 9 vs 1 public rows
Reading
Claude Sonnet 5.5 leads · intervals overlap

Knowledge

Directional only
Claude Sonnet 5.5
80.6
Supported · #6/169
GPT-6.1 Sol
71.3
Estimated · #10/169
Basis
BenchAlign v5.7 lane · 16 vs 5 public rows
Reading
Directional only

Agentic

Not comparable
Claude Sonnet 5.5
67.1
Supported · #10/117
GPT-6.1 Sol
Not ranked
Basis
BenchAlign v5.7 lane · 11 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Sonnet 5.5
79.2
#7/27
GPT-6.1 Sol
79.4
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 5.5
83.3
Unranked · 7 rankable rows
GPT-6.1 Sol
83.9
Unranked · 1 rankable row
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 5.5
Not ranked
GPT-6.1 Sol
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 5.5
Not ranked
GPT-6.1 Sol
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 5.5
Not ranked
GPT-6.1 Sol
Not ranked
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 v5.7) 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 Sonnet 5.5
$0.007
Fits in one request
GPT-6.1 Sol
$0.007
Fits in one request

Modeled costs are equal

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 5.5
$0.13
Fits in one request
GPT-6.1 Sol
$0.13
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 Sonnet 5.5
$0.18
Fits in one request
GPT-6.1 Sol
$0.16
Fits in one request

GPT-6.1 Sol has the lower modeled cost

Costs use the listed standard API rates.

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.

Reasoning profile

Claude Sonnet 5.5

Reasoning

GPT-6.1 Sol

Reasoning

Weight access

Claude Sonnet 5.5

Proprietary

GPT-6.1 Sol

Proprietary

License

Claude Sonnet 5.5

Proprietary

GPT-6.1 Sol

Proprietary

Release date

Claude Sonnet 5.5

2026-09-28

GPT-6.1 Sol

2026-09-29

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 Sonnet 5.5 has the higher public score estimate, 80.9 versus 66.86, but the 90% score intervals overlap.
Workload cost
Repository review: $0.13 vs $0.13. Cache-heavy agent loop: $0.18 vs $0.16.
Context tradeoff
GPT-6.1 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Sonnet 5.5 or GPT-6.1 Sol?

Claude Sonnet 5.5 has the higher public score estimate, 80.9 versus 66.86, 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 Sonnet 5.5 or GPT-6.1 Sol?

Claude Sonnet 5.5 leads the public coding lane, 85.1 to 67, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Claude Sonnet 5.5 or GPT-6.1 Sol?

GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Sonnet 5.5 or GPT-6.1 Sol?

For the stated presets, chat costs $0.007 on Claude Sonnet 5.5 and $0.007 on GPT-6.1 Sol; repository review costs $0.13 and $0.13; the cache-heavy agent loop costs $0.18 and $0.16. Costs use the listed standard API rates.

Which has the larger context window, Claude Sonnet 5.5 or GPT-6.1 Sol?

GPT-6.1 Sol has the larger documented context window: 1.05M, compared with 1M.

Benchmark evidence

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

Browse raw public benchmark evidence50 rows

Agentic

  • Terminal-Bench 4.0

    Claude Sonnet 5.570.60%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • HLE w/ tools

    Claude Sonnet 5.564.5%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • DRACO

    Claude Sonnet 5.587.0%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Sonnet 5.559.9%
    Source
    GPT-6.1 Sol57.0%
    Source

    Claude Sonnet 5.5 leads this result

  • LAB all-pass (Harvey held-out)

    Claude Sonnet 5.510.0%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    Claude Sonnet 5.593.1%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Sonnet 5.585.2%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Sonnet 5.568.5%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Sonnet 5.531.6 turns
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • AutomationBench (Zapier 1.0.6)

    Claude Sonnet 5.544.7%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Toolathlon-Verified

    Claude Sonnet 5.577.8%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • AutomationBench

    Claude Sonnet 5.5—
    GPT-6.1 Sol36.1%
    Source

    Not directly comparable

  • ExploitGym

    Claude Sonnet 5.5—
    GPT-6.1 Sol35.1%
    Source

    Not directly comparable

Coding

  • FrontierCode 1.1 Main

    Claude Sonnet 5.546.2%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • cursorBench40

    Claude Sonnet 5.555.5%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • SWE-bench Pro

    Claude Sonnet 5.581.3%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • SWE Multilingual

    Claude Sonnet 5.590.3%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • SWE Multimodal

    Claude Sonnet 5.554.3%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • DeepSWE

    Claude Sonnet 5.571.0%
    Source
    GPT-6.1 Sol71.9%
    Source

    GPT-6.1 Sol leads this result

  • FrontierCode 1.1 Extended

    Claude Sonnet 5.559.1%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • ProgramBench

    Claude Sonnet 5.579.7%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • FrontierSWE v2

    Claude Sonnet 5.561.9%
    Source
    GPT-6.1 Sol—

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Claude Sonnet 5.561.6%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Chartography (tools)

    Claude Sonnet 5.590.2%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    Claude Sonnet 5.50.747
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Claude Sonnet 5.50.963
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Biomedical image analysis

    Claude Sonnet 5.572.2%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • OfficeQA

    Claude Sonnet 5.576.9%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • OfficeQA Pro

    Claude Sonnet 5.565.6%
    Source
    GPT-6.1 Sol—

    Not directly comparable

Knowledge

  • HealthBench (raw)

    Claude Sonnet 5.569.4%
    Source
    GPT-6.1 Sol56.7%
    Source

    Claude Sonnet 5.5 leads this result

  • HealthBench (length-adjusted)

    Claude Sonnet 5.565.4%
    Source
    GPT-6.1 Sol58.5%
    Source

    Claude Sonnet 5.5 leads this result

  • HealthBench Professional (raw)

    Claude Sonnet 5.577.1%
    Source
    GPT-6.1 Sol67.2%
    Source

    Claude Sonnet 5.5 leads this result

  • HealthBench Professional

    Claude Sonnet 5.569.2%
    Source
    GPT-6.1 Sol64.2%
    Source

    Claude Sonnet 5.5 leads this result

  • BioMysteryBench (human-solvable)

    Claude Sonnet 5.589.2%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Claude Sonnet 5.544.7%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • SpatialBench Verified

    Claude Sonnet 5.572.5%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • SingleCellBench

    Claude Sonnet 5.559.1%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Protein Design

    Claude Sonnet 5.551.0%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Morphology-to-molecule matching

    Claude Sonnet 5.525.0%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Medicinal chemistry

    Claude Sonnet 5.565.3%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Protein Design library ranking

    Claude Sonnet 5.554.8%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • De novo protein-binder design

    Claude Sonnet 5.582.3%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Sonnet 5.567.3%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • Protocols (understanding)

    Claude Sonnet 5.566.6%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • HLE w/o tools

    Claude Sonnet 5.556.9%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • HealthBench Hard

    Claude Sonnet 5.5—
    GPT-6.1 Sol36.2%
    Source

    Not directly comparable

Multilingual

  • GMMLU

    Claude Sonnet 5.592.1%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • MILU

    Claude Sonnet 5.591.6%
    Source
    GPT-6.1 Sol—

    Not directly comparable

Math

  • ArXivMath Aug. 2026 (no tools)

    Claude Sonnet 5.586.8%
    Source
    GPT-6.1 Sol—

    Not directly comparable

  • ArXivMath Aug. 2026 (tools)

    Claude Sonnet 5.595.2%
    Source
    GPT-6.1 Sol—

    Not directly comparable

50 public results · 6 shared

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Last updated September 29, 2026