Model A

Anthropic

69.94/100

Estimated · Public rank #23

90% interval 51.6–81.5

Claude Opus 4.7 (Adaptive) vs GPT-5.4 nano

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

Model B

GPT-5.4 nano

OpenAI

62.19/100

Supported · Public rank #61

90% interval 51.1–73.3

Decision reading

Claude Opus 4.7 (Adaptive) has the higher public score estimate, 69.94 versus 62.19, 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.

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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.

Agentic work
Tool use, computer use, and multi-step task completion
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) leads on the public agentic lane, 61 to 37.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano 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
GPT-5.4 nano
GPT-5.4 nano 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
Repository review cost
50K fresh input + 3K output tokens
GPT-5.4 nano
GPT-5.4 nano 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
Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, 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.

15 Claude Opus 4.7 (Adaptive) only

6 Shared

12 GPT-5.4 nano only

Shared results: 6
Claude Opus 4.7 (Adaptive) only: 15
GPT-5.4 nano only: 12
Like-for-like categories: 1 / 8

3 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

Like-for-like

Claude Opus 4.7 (Adaptive): 61.0; Supported · #21/151
GPT-5.4 nano: 37.3; Supported · #127/151
Basis: BenchAlign lane · 7 vs 6 public rows
Reading: Claude Opus 4.7 (Adaptive) leads

Coding

Directional only

Claude Opus 4.7 (Adaptive): 59.6; Estimated · #27/183
GPT-5.4 nano: 37.4; Supported · #150/183
Basis: BenchAlign lane · 3 vs 3 public rows
Reading: Directional only

Knowledge

Directional only

Claude Opus 4.7 (Adaptive): 61.9; Estimated · #31/181
GPT-5.4 nano: 48.5; Supported · #99/181
Basis: BenchAlign lane · 4 vs 5 public rows
Reading: Directional only

Multimodal

Directional only

Claude Opus 4.7 (Adaptive): 50.3; #36/48
GPT-5.4 nano: 23.8; #45/48
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.7 (Adaptive): 49.7; Unranked · 3 rankable rows
GPT-5.4 nano: 72.8; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.7 (Adaptive): Not ranked
GPT-5.4 nano: 43.9; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.7 (Adaptive): Not ranked
GPT-5.4 nano: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 4.7 (Adaptive): Not ranked
GPT-5.4 nano: 92.9; #9/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.7 (Adaptive) and GPT-5.4 nano
Category	Claude Opus 4.7 (Adaptive)	GPT-5.4 nano	Basis	Reading
Agentic	61.0Supported · #21/151	37.3Supported · #127/151	Like-for-likeBenchAlign lane · 7 vs 6 public rows	Claude Opus 4.7 (Adaptive) leads
Coding	59.6Estimated · #27/183	37.4Supported · #150/183	Directional onlyBenchAlign lane · 3 vs 3 public rows	Directional only
Knowledge	61.9Estimated · #31/181	48.5Supported · #99/181	Directional onlyBenchAlign lane · 4 vs 5 public rows	Directional only
Multimodal	50.3#36/48	23.8#45/48	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Reasoning	49.7Unranked · 3 rankable rows	72.8Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Math	Not ranked	43.9Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	92.9#9/120	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.

OSWorld-Verified
Agentic
Claude Opus 4.7 (Adaptive): 78%GPT-5.4 nano: 39%Normalized gap 39.0Claude Opus 4.7 (Adaptive) source GPT-5.4 nano source
Terminal-Bench 2.0
Agentic
Claude Opus 4.7 (Adaptive): 69.4%GPT-5.4 nano: 46.3%Normalized gap 23.1Claude Opus 4.7 (Adaptive) source GPT-5.4 nano source
HLE w/o tools
Knowledge
Claude Opus 4.7 (Adaptive): 46.9%GPT-5.4 nano: 24.3%Normalized gap 22.6Claude Opus 4.7 (Adaptive) source GPT-5.4 nano source
HLE
Knowledge
Claude Opus 4.7 (Adaptive): 54.7%GPT-5.4 nano: 37.7%Normalized gap 17.0Claude Opus 4.7 (Adaptive) source GPT-5.4 nano source
GPQA
Knowledge
Claude Opus 4.7 (Adaptive): 94.2%GPT-5.4 nano: 82.8%Normalized gap 11.4Claude Opus 4.7 (Adaptive) source GPT-5.4 nano 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 (Adaptive): $0.0175; Fits in one request
GPT-5.4 nano: $0.00082; Fits in one request

GPT-5.4 nano 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-5.4 nano: $0.01375; Fits in one request

GPT-5.4 nano 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-5.4 nano: $0.0205; Fits in one request

GPT-5.4 nano 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.

SpecificationClaude Opus 4.7 (Adaptive)GPT-5.4 nano

Context window

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

Claude Opus 4.7 (Adaptive)

GPT-5.4 nano

400K

OpenAI GPT-5.4 nano model documentation

API model ID

Claude Opus 4.7 (Adaptive)

Not sourced

GPT-5.4 nano

gpt-5.4-nano

OpenAI GPT-5.4 nano 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 4.7 (Adaptive)

Not published

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Documented inputs

Claude Opus 4.7 (Adaptive)

Not sourced

GPT-5.4 nano

text, image

OpenAI model catalog

Documented outputs

Claude Opus 4.7 (Adaptive)

Not sourced

GPT-5.4 nano

text

OpenAI model catalog

Provider availability

Claude Opus 4.7 (Adaptive)

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Claude Opus 4.7 (Adaptive)

Reasoning

GPT-5.4 nano

Reasoning

Weight access

Claude Opus 4.7 (Adaptive)

Proprietary

GPT-5.4 nano

Proprietary

License

Claude Opus 4.7 (Adaptive)

Proprietary

GPT-5.4 nano

Proprietary

Release date

Claude Opus 4.7 (Adaptive)

2026-04-16

GPT-5.4 nano

2026-03-17

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 (Adaptive) has the higher public score estimate, 69.94 versus 62.19, but the 90% score intervals overlap.
Workload cost: Repository review: $0.325 vs $0.01375. Cache-heavy agent loop: $1.35 vs $0.0205.
Context tradeoff: Claude Opus 4.7 (Adaptive) has the larger documented window (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 evidence33 rows

Agentic

Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%
Source
GPT-5.4 nano46.3%
Source
Claude Opus 4.7 (Adaptive) leads this result
BrowseComp
Claude Opus 4.7 (Adaptive)79.3%
Source
GPT-5.4 nano—
Not directly comparable
MCP Atlas
Claude Opus 4.7 (Adaptive)77.3%
Source
GPT-5.4 nano56.1%
Source
Claude Opus 4.7 (Adaptive) leads this result
OSWorld-Verified
Claude Opus 4.7 (Adaptive)78%
Source
GPT-5.4 nano39%
Source
Claude Opus 4.7 (Adaptive) leads this result
CyberGym
Claude Opus 4.7 (Adaptive)73.1%
Source
GPT-5.4 nano—
Not directly comparable
OSWorld 2.0
Claude Opus 4.7 (Adaptive)18.2%
Source
GPT-5.4 nano—
Not directly comparable
JobBench
Claude Opus 4.7 (Adaptive)45.9%
Source
GPT-5.4 nano—
Not directly comparable
Toolathlon
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano35.5%
Source
Not directly comparable
τ²-bench results
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano92.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano41.6%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.7 (Adaptive)87.6%
Source
GPT-5.4 nano—
Not directly comparable
SWE-bench Pro
Claude Opus 4.7 (Adaptive)64.3%
Source
GPT-5.4 nano—
Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%
Source
GPT-5.4 nano—
Not directly comparable
Vibe Code Bench
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano26.10%
Source
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano84.0%
Source
Not directly comparable
SWE-bench (Vals)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano69.8%
Source
Not directly comparable

Reasoning

MRCR v2 128K-256K
Claude Opus 4.7 (Adaptive)59.2%
Source
GPT-5.4 nano—
Not directly comparable
ARC-AGI-2
Claude Opus 4.7 (Adaptive)75.8%
Source
GPT-5.4 nano—
Not directly comparable
ARC-AGI-3
Claude Opus 4.7 (Adaptive)0.2%
Source
GPT-5.4 nano—
Not directly comparable

Knowledge

GPQA
Claude Opus 4.7 (Adaptive)94.2%
Source
GPT-5.4 nano82.8%
Source
Claude Opus 4.7 (Adaptive) leads this result
GPQA-D
Claude Opus 4.7 (Adaptive)94.2%
Source
GPT-5.4 nano—
Not directly comparable
HLE
Claude Opus 4.7 (Adaptive)54.7%
Source
GPT-5.4 nano37.7%
Source
Claude Opus 4.7 (Adaptive) leads this result
HLE w/o tools
Claude Opus 4.7 (Adaptive)46.9%
Source
GPT-5.4 nano24.3%
Source
Claude Opus 4.7 (Adaptive) leads this result
GPQA Diamond (Vals)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano77.5%
Source
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano77.2%
Source
Not directly comparable

Math

FrontierMath (legacy)
Claude Opus 4.7 (Adaptive)43.8%
Source
GPT-5.4 nano—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano25.860%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano6.250%
Source
Not directly comparable

Multimodal

OfficeQA Pro
Claude Opus 4.7 (Adaptive)43.6%
Source
GPT-5.4 nano—
Not directly comparable
CharXiv
Claude Opus 4.7 (Adaptive)91%
Source
GPT-5.4 nano—
Not directly comparable
CharXiv w/o tools
Claude Opus 4.7 (Adaptive)82.1%
Source
GPT-5.4 nano—
Not directly comparable
MMMU-Pro
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano66.1%
Source
Not directly comparable
MMMU-Pro w/ Python
Claude Opus 4.7 (Adaptive)—
GPT-5.4 nano69.5%
Source
Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

Claude Opus 4.7 (Adaptive) has the higher public score estimate, 69.94 versus 62.19, 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-5.4 nano?

Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 59.6 to 37.4. Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

Claude Opus 4.7 (Adaptive) leads the public agentic tasks lane, 61 to 37.3, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.00082 on GPT-5.4 nano; repository review costs $0.325 and $0.01375; the cache-heavy agent loop costs $1.35 and $0.0205. 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-5.4 nano?

Claude Opus 4.7 (Adaptive) has the larger documented context window: 1M, compared with 400K.

Related comparisons

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Last updated September 4, 2026

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Claude Opus 4.7 (Adaptive) vs GPT-5.4 nano

Which one for your work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Multimodal

Reasoning

Math

Multilingual

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 4.7 (Adaptive) or GPT-5.4 nano?

Which is better for coding, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

Which costs less, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

Which has the larger context window, Claude Opus 4.7 (Adaptive) or GPT-5.4 nano?

Related comparisons

Watch Claude Opus 4.7 (Adaptive) vs GPT-5.4 nano