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Model comparison

Claude Opus 4.7 (Adaptive) vs GPT-4.1 nano

Data verified

Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
24.2pts
← winning
42.06/100
1 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-4.1 nano #161 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and GPT-4.1 nano share 18 comparable benchmark results. 1 of 8 categories are comparable. 20 results are unique to Claude Opus 4.7 (Adaptive); 3 to GPT-4.1 nano.

Updated July 23, 2026
Shared results
18
Claude Opus 4.7 (Adaptive) only
20
GPT-4.1 nano only
3
Comparable categories
1 / 8

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Claude Opus 4.7 (Adaptive) is clearly ahead on the BenchAlign aggregate, 66.27 to 42.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Claude Opus 4.7 (Adaptive)'s sharpest advantage is in knowledge, where it averages 60 against 50.3. The single biggest benchmark swing on the page is GPQA, 94.2% to 50.3%.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 62.5x on output cost alone. Claude Opus 4.7 (Adaptive) is the reasoning model in the pair, while GPT-4.1 nano is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.

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-4.1 nano
CategoryClaude Opus 4.7 (Adaptive)ΔGPT-4.1 nano
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 9.7GPT-4.1 nano50.3
AgenticClaude Opus 4.7 (Adaptive)75.1MarginNo overlapGPT-4.1 nanoNot measured
CodingClaude Opus 4.7 (Adaptive)78.6MarginNo overlapGPT-4.1 nanoNot measured
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGPT-4.1 nanoNot measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-4.1 nano1.0
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapGPT-4.1 nanoNot measured
Inst. FollowingClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-4.1 nano83.2

Decisive benchmark drivers

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

More
A · Claude Opus 4.7 (Adaptive)B · GPT-4.1 nano

  1. GPQA

    Knowledge
    Source ↗
    A 94.2%B 50.3%
    Winner: Claude Opus 4.7 (Adaptive)Δ 43.9
    GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GPT-4.1 nano scored 50.3%. 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-4.1 nanoComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGPT-4.1 nano$0.1 input / $0.4 outputGPT-4.1 nano has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGPT-4.1 nano181 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGPT-4.1 nano0.63 sA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGPT-4.1 nano1MListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 nanoResult
Terminal-Bench 2.0Source 69.4%—Not comparable
BrowseCompSource 79.3%—Not comparable
MCP AtlasSource 77.3%—Not comparable
OSWorld-VerifiedSource 78%—Not comparable
CyberGymSource 73.1%—Not comparable
AA Agentic IndexSource 44.4%1.2%Claude Opus 4.7 (Adaptive) leads
τ²-bench resultsSource 88.6%17.3%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 49.8%0.0%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 149541Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
Coding
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 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%11.1%Claude Opus 4.7 (Adaptive) leads
AA-SciCodeSource 54.5%25.9%Claude Opus 4.7 (Adaptive) leads
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 nanoResult
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%17.0%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%0.0%Claude Opus 4.7 (Adaptive) leads
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 nanoResult
GPQASource 94.2%50.3%Claude Opus 4.7 (Adaptive) leads
GPQA-DSource 94.2%—Not comparable
HLESource 54.7%—Not comparable
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%9.6%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%51.2%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%3.9%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%-56.4%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%13.3%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%80.4%Claude Opus 4.7 (Adaptive) leads
MMLUSource —80.1%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 nanoResult
FrontierMath (legacy)Source 43.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source —1.034%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 nanoResult
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%40.1%Claude Opus 4.7 (Adaptive) leads
Design Arena WebsiteSource 13251003Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1 nanoResult
AA-IFBenchSource 58.6%32.0%Claude Opus 4.7 (Adaptive) leads
IFEvalSource —83.2%Not comparable
Frequently Asked Questions (2)

Which is better, Claude Opus 4.7 (Adaptive) or GPT-4.1 nano?

Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 42.06. The biggest single separator in this matchup is GPQA, where the scores are 94.2% and 50.3%.

Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or GPT-4.1 nano?

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 50.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.

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