Model A

Anthropic

69.84/100

Supported · Public rank #24

90% interval 60.5–79.2

Claude Opus 4.6 vs GPT-5.4 Pro

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

Model B

GPT-5.4 Pro

OpenAI

61.75/100

Estimated · Public rank #63

90% interval 50.2–73.3

Decision reading

Claude Opus 4.6 has the higher public score estimate, 69.84 versus 61.75, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 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.

Long documents
Prompts that approach the documented context limit
GPT-5.4 Pro
GPT-5.4 Pro has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Claude Opus 4.6
Claude Opus 4.6 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
Claude Opus 4.6
Claude Opus 4.6 has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.4 Pro 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 4.6
Claude Opus 4.6 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
GPT-5.4 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.4 Pro is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.

28 Claude Opus 4.6 only

5 Shared

5 GPT-5.4 Pro only

Shared results: 5
Claude Opus 4.6 only: 28
GPT-5.4 Pro only: 5
Like-for-like categories: 0 / 8

2 categories rest 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.

Agentic

Directional only

Claude Opus 4.6: 54.6; Supported · #39/151
GPT-5.4 Pro: 58.1; Estimated · #33/151
Basis: BenchAlign lane · 9 vs 1 public rows
Reading: Directional only

Knowledge

Directional only

Claude Opus 4.6: 61.8; Estimated · #33/181
GPT-5.4 Pro: 60.7; Estimated · #39/181
Basis: BenchAlign lane · 9 vs 4 public rows
Reading: Directional only

Coding

Not comparable

Claude Opus 4.6: 56.5; Supported · #40/183
GPT-5.4 Pro: Not ranked
Basis: BenchAlign lane · 8 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

Claude Opus 4.6: 65.8; Unranked · 2 rankable rows
GPT-5.4 Pro: 70.1; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.6: 58.6; Unranked · 3 rankable rows
GPT-5.4 Pro: 69.0; Unranked · 4 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.6: Not ranked
GPT-5.4 Pro: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 4.6: 59.5; #28/48
GPT-5.4 Pro: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 4.6: 52.3; #76/120
GPT-5.4 Pro: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.6 and GPT-5.4 Pro
Category	Claude Opus 4.6	GPT-5.4 Pro	Basis	Reading
Agentic	54.6Supported · #39/151	58.1Estimated · #33/151	Directional onlyBenchAlign lane · 9 vs 1 public rows	Directional only
Knowledge	61.8Estimated · #33/181	60.7Estimated · #39/181	Directional onlyBenchAlign lane · 9 vs 4 public rows	Directional only
Coding	56.5Supported · #40/183	Not ranked	Not comparableBenchAlign lane · 8 vs 0 public rows	Not comparable
Reasoning	65.8Unranked · 2 rankable rows	70.1Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	58.6Unranked · 3 rankable rows	69.0Unranked · 4 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	59.5#28/48	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Instruction following	52.3#76/120	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	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 (Tier 4)
Math
Claude Opus 4.6: 22.900%GPT-5.4 Pro: 37.500%Normalized gap 14.6Shared source
FrontierMath v2 (Tiers 1-3)
Math
Claude Opus 4.6: 40.700%GPT-5.4 Pro: 50.000%Normalized gap 9.3Shared source
HLE
Knowledge
Claude Opus 4.6: 53%GPT-5.4 Pro: 58.7%Normalized gap 5.7Claude Opus 4.6 source GPT-5.4 Pro source
BrowseComp
Agentic
Claude Opus 4.6: 83.7%GPT-5.4 Pro: 89.3%Normalized gap 5.6Claude Opus 4.6 source GPT-5.4 Pro source
HLE w/o tools
Knowledge
Claude Opus 4.6: 40%GPT-5.4 Pro: 42.7%Normalized gap 2.7Claude Opus 4.6 source GPT-5.4 Pro 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 4.6: $0.0175; Fits in one request
GPT-5.4 Pro: $0.12; Fits in one request

Claude Opus 4.6 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.6: $0.325; Fits in one request
GPT-5.4 Pro: $2.04; Fits in one request

Claude Opus 4.6 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 4.6: $1.35; Fits in one request; Cached input priced at the published list-input rate
GPT-5.4 Pro: $8.40; Fits in one request; Cached input priced at the published list-input rate

Claude Opus 4.6 has the lower modeled cost

Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.4 Pro 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.

SpecificationClaude Opus 4.6GPT-5.4 Pro

Context window

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

Claude Opus 4.6

GPT-5.4 Pro

1.05M

OpenAI GPT-5.4 Pro model documentation

API model ID

Claude Opus 4.6

Not sourced

GPT-5.4 Pro

gpt-5.4-pro

OpenAI GPT-5.4 Pro model documentation

Cached-input rate

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

Claude Opus 4.6

Not published

GPT-5.4 Pro

Not published

OpenAI pricing

Documented inputs

Claude Opus 4.6

Not sourced

GPT-5.4 Pro

text, image

OpenAI model catalog

Documented outputs

Claude Opus 4.6

Not sourced

GPT-5.4 Pro

text

OpenAI model catalog

Provider availability

Claude Opus 4.6

Not sourced

GPT-5.4 Pro

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Claude Opus 4.6

Non-Reasoning

GPT-5.4 Pro

Reasoning

Weight access

Claude Opus 4.6

Proprietary

GPT-5.4 Pro

Proprietary

License

Claude Opus 4.6

Proprietary

GPT-5.4 Pro

Proprietary

Release date

Claude Opus 4.6

2026-02-01

GPT-5.4 Pro

2026-03-05

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 4.6 has the higher public score estimate, 69.84 versus 61.75, but the 90% score intervals overlap.
Workload cost: Repository review: $0.325 vs $2.04. Cache-heavy agent loop: $1.35 vs $8.40.
Context tradeoff: GPT-5.4 Pro 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 evidence38 rows

Agentic

Terminal-Bench 2.0
Claude Opus 4.665.4%
Source
GPT-5.4 Pro—
Not directly comparable
BrowseComp
Claude Opus 4.683.7%
Source
GPT-5.4 Pro89.3%
Source
GPT-5.4 Pro leads this result
OSWorld-Verified
Claude Opus 4.672.7%
Source
GPT-5.4 Pro—
Not directly comparable
Claw-Eval
Claude Opus 4.670.4%
Source
GPT-5.4 Pro—
Not directly comparable
DeepSearchQA
Claude Opus 4.673.7%
Source
GPT-5.4 Pro—
Not directly comparable
CyberGym
Claude Opus 4.666.6%
Source
GPT-5.4 Pro—
Not directly comparable
Gert Labs
Claude Opus 4.661.85%
Source
GPT-5.4 Pro—
Not directly comparable
ResearchClawBench
Claude Opus 4.619.9%
Source
GPT-5.4 Pro—
Not directly comparable
JobBench
Claude Opus 4.636.7%
Source
GPT-5.4 Pro—
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.680.8%
Source
GPT-5.4 Pro—
Not directly comparable
SWE-bench Verified*
Claude Opus 4.675.6%
Source
GPT-5.4 Pro—
Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%
Source
GPT-5.4 Pro—
Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%
Source
GPT-5.4 Pro—
Not directly comparable
SWE-Rebench
Claude Opus 4.665.3%
Source
GPT-5.4 Pro—
Not directly comparable
React Native Evals
Claude Opus 4.684.1%
Source
GPT-5.4 Pro—
Not directly comparable
Vibe Code Bench
Claude Opus 4.657.57%
Source
GPT-5.4 Pro—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.626.9%
Source
GPT-5.4 Pro—
Not directly comparable

Reasoning

ARC-AGI-2
Claude Opus 4.6—
GPT-5.4 Pro83.3%
Source
Not directly comparable

Knowledge

GPQA
Claude Opus 4.691.3%
Source
GPT-5.4 Pro—
Not directly comparable
GPQA-D
Claude Opus 4.689.2%
Source
GPT-5.4 Pro—
Not directly comparable
SuperGPQA
Claude Opus 4.695%
Source
GPT-5.4 Pro—
Not directly comparable
MMLU-Pro
Claude Opus 4.682%
Source
GPT-5.4 Pro—
Not directly comparable
MMLU-Pro (Arcee)
Claude Opus 4.689.1%
Source
GPT-5.4 Pro—
Not directly comparable
HLE
Claude Opus 4.653%
Source
GPT-5.4 Pro58.7%
Source
GPT-5.4 Pro leads this result
HLE w/o tools
Claude Opus 4.640%
Source
GPT-5.4 Pro42.7%
Source
GPT-5.4 Pro leads this result
HealthBench Hard
Claude Opus 4.614.8%
Source
GPT-5.4 Pro—
Not directly comparable
MedXpertQA (Text)
Claude Opus 4.652.1%
Source
GPT-5.4 Pro—
Not directly comparable
FrontierScience
Claude Opus 4.6—
GPT-5.4 Pro36.7%
Source
Not directly comparable
FrontierScience Research
Claude Opus 4.6—
GPT-5.4 Pro36.7%
Source
Not directly comparable

Math

AIME25 (Arcee)
Claude Opus 4.699.8%
Source
GPT-5.4 Pro—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared source
Claude Opus 4.640.700%
GPT-5.4 Pro50.000%
GPT-5.4 Pro leads this result
FrontierMath v2 (Tier 4)
Shared source
Claude Opus 4.622.900%
GPT-5.4 Pro37.500%
GPT-5.4 Pro leads this result
IPhO 2025 (Theory)
Claude Opus 4.6—
GPT-5.4 Pro93.5%
Source
Not directly comparable
FrontierMath (legacy)
Claude Opus 4.6—
GPT-5.4 Pro50%
Source
Not directly comparable

Multimodal

MMMU-Pro
Claude Opus 4.677.3%
Source
GPT-5.4 Pro—
Not directly comparable
ERQA
Claude Opus 4.651.6%
Source
GPT-5.4 Pro—
Not directly comparable
ScreenSpot Pro
Claude Opus 4.683.1%
Source
GPT-5.4 Pro—
Not directly comparable
MedXpertQA (MM)
Claude Opus 4.664.8%
Source
GPT-5.4 Pro—
Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.6 or GPT-5.4 Pro?

Claude Opus 4.6 has the higher public score estimate, 69.84 versus 61.75, 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 4.6 or GPT-5.4 Pro?

GPT-5.4 Pro is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Opus 4.6 or GPT-5.4 Pro?

GPT-5.4 Pro scores higher for agentic tasks on the public lane, 58.1 to 54.6. GPT-5.4 Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Opus 4.6 or GPT-5.4 Pro?

For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.12 on GPT-5.4 Pro; repository review costs $0.325 and $2.04; the cache-heavy agent loop costs $1.35 and $8.40. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.6 or GPT-5.4 Pro?

GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

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Last updated September 4, 2026

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Claude Opus 4.6 vs GPT-5.4 Pro

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

What is actually comparable

Category results, on a stated basis

Agentic

Knowledge

Coding

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Claude Opus 4.6 or GPT-5.4 Pro?

Which is better for coding, Claude Opus 4.6 or GPT-5.4 Pro?

Which is better for agentic tasks, Claude Opus 4.6 or GPT-5.4 Pro?

Which costs less, Claude Opus 4.6 or GPT-5.4 Pro?

Which has the larger context window, Claude Opus 4.6 or GPT-5.4 Pro?

Related comparisons

Watch Claude Opus 4.6 vs GPT-5.4 Pro