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Model comparison

Claude Opus 4.7 (Adaptive) vs GPT-5.4 nano

Data verified

Head-to-head evidence from 22 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
0.5pts
winning →
66.79/100
2 category wins1 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-5.4 nano #25 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and GPT-5.4 nano share 22 comparable benchmark results. 3 of 8 categories are comparable. 16 results are unique to Claude Opus 4.7 (Adaptive); 7 to GPT-5.4 nano.

Updated July 23, 2026
Shared results
22
Claude Opus 4.7 (Adaptive) only
16
GPT-5.4 nano only
7
Comparable categories
3 / 8

Pick GPT-5.4 nano if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if agentic is the priority or you need the larger 1M context window.

Confidence note. This is a partial-evidence comparison with 22 shared benchmark results across 6 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.4 nano has the cleaner BenchAlign overall profile here, landing at 66.79 versus 66.27. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

GPT-5.4 nano's sharpest advantage is in multimodal & grounded, where it averages 66.1 against 65.1. The single biggest benchmark swing on the page is OSWorld-Verified, 78% to 39%. Claude Opus 4.7 (Adaptive) does hit back in agentic, so the answer changes if that is the part of the workload you care about most.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. That is roughly 20.0x on output cost alone. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 400K for GPT-5.4 nano.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Claude Opus 4.7 (Adaptive) and GPT-5.4 nano
CategoryClaude Opus 4.7 (Adaptive)ΔGPT-5.4 nano
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 32.2GPT-5.4 nano42.9
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 16.2GPT-5.4 nano43.8
MultimodalClaude Opus 4.7 (Adaptive)65.1Margin→ 1.0GPT-5.4 nano66.1
CodingClaude Opus 4.7 (Adaptive)78.6MarginNo overlapGPT-5.4 nanoNot measured
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGPT-5.4 nanoNot measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-5.4 nano21.0

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Claude Opus 4.7 (Adaptive)B · GPT-5.4 nano

  1. OSWorld-Verified

    Agentic
    Source ↗
    A 78%B 39%
    Winner: Claude Opus 4.7 (Adaptive)Δ 39
    OSWorld-Verified: Claude Opus 4.7 (Adaptive) scored 78%; GPT-5.4 nano scored 39%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 46.3%
    Winner: Claude Opus 4.7 (Adaptive)Δ 23.1
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GPT-5.4 nano scored 46.3%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  3. HLE

    Knowledge
    Source ↗
    A 54.7%B 37.7%
    Winner: Claude Opus 4.7 (Adaptive)Δ 17
    HLE: Claude Opus 4.7 (Adaptive) scored 54.7%; GPT-5.4 nano scored 37.7%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  4. GPQA

    Knowledge
    Source ↗
    A 94.2%B 82.8%
    Winner: Claude Opus 4.7 (Adaptive)Δ 11.4
    GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GPT-5.4 nano scored 82.8%. Claude Opus 4.7 (Adaptive) wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.7 (Adaptive)GPT-5.4 nanoComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGPT-5.4 nano$0.2 input / $1.25 outputGPT-5.4 nano has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGPT-5.4 nano191 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGPT-5.4 nano3.64 sA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGPT-5.4 nano400KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
Terminal-Bench 2.0Source 69.4%46.3%Claude Opus 4.7 (Adaptive) leads
BrowseCompSource 79.3%—Not comparable
MCP AtlasSource 77.3%56.1%Claude Opus 4.7 (Adaptive) leads
OSWorld-VerifiedSource 78%39%Claude Opus 4.7 (Adaptive) leads
CyberGymSource 73.1%—Not comparable
AA Agentic IndexSource 44.4%27.5%Claude Opus 4.7 (Adaptive) leads
τ²-bench resultsSource 88.6%76%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 49.8%30.0%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 14951100Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
ToolathlonSource —35.5%Not comparable
APEX-Agents-AASource —24.9%Not comparable
Coding
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
SWE-bench VerifiedSource 87.6%—Not comparable
SWE-bench ProSource 64.3%—Not comparable
Terminal-Bench 2.0Source 69.4%—Not comparable
AA Coding IndexSource 73.6%56.1%Claude Opus 4.7 (Adaptive) leads
AA-SciCodeSource 54.5%46.9%Claude Opus 4.7 (Adaptive) leads
Vibe Code BenchSource —26.10%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%66.0%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%9.3%Claude Opus 4.7 (Adaptive) leads
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
GPQASource 94.2%82.8%Claude Opus 4.7 (Adaptive) leads
GPQA-DSource 94.2%—Not comparable
HLESource 54.7%37.7%Claude Opus 4.7 (Adaptive) leads
HLE w/o toolsSource 46.9%24.3%Claude Opus 4.7 (Adaptive) leads
Artificial Analysis Intelligence IndexSource 53.5%38.2%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%81.7%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%26.5%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%-29.5%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%25.4%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%73.6%Claude Opus 4.7 (Adaptive) leads
Math
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
FrontierMath (legacy)Source 43.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source —25.860%Not comparable
FrontierMath v2 (Tier 4)Source —6.250%Not comparable
MultimodalGPT-5.4 nano wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%65.4%Claude Opus 4.7 (Adaptive) leads
Design Arena WebsiteSource 1325—Not comparable
MMMU-ProSource —66.1%Not comparable
MMMU-Pro w/ PythonSource —69.5%Not comparable
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.4 nanoResult
AA-IFBenchSource 58.6%75.9%GPT-5.4 nano leads
Frequently Asked Questions (4)

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

GPT-5.4 nano is ahead on BenchLM's BenchAlign leaderboard, 66.79 to 66.27. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 78% and 39%.

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

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 43.8. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.

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

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 42.9. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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

GPT-5.4 nano has the edge for multimodal and grounded tasks in this comparison, averaging 66.1 versus 65.1. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.

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