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Model comparison

Claude Opus 4.7 (Adaptive) vs GPT-4.1

Data verified

Head-to-head evidence from 15 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
15.2pts
← winning
GPT-4.1
OpenAI
51.11/100
1 category wins1 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-4.1 #108 (Supported). 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 share 15 comparable benchmark results. 2 of 8 categories are comparable. 23 results are unique to Claude Opus 4.7 (Adaptive); 5 to GPT-4.1.

Updated July 23, 2026
Shared results
15
Claude Opus 4.7 (Adaptive) only
23
GPT-4.1 only
5
Comparable categories
2 / 8

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 evidence categories; 2 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 51.11. 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 coding, where it averages 78.6 against 54.6. The single biggest benchmark swing on the page is SWE-bench Verified, 87.6% to 54.6%. GPT-4.1 does hit back in knowledge, 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 $2.00 input / $8.00 output per 1M tokens for GPT-4.1. That is roughly 3.1x on output cost alone. Claude Opus 4.7 (Adaptive) is the reasoning model in the pair, while GPT-4.1 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
CategoryClaude Opus 4.7 (Adaptive)ΔGPT-4.1
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 24.0GPT-4.154.6
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin→ 6.3GPT-4.166.3
AgenticClaude Opus 4.7 (Adaptive)75.1MarginNo overlapGPT-4.1Not measured
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGPT-4.1Not measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-4.14.1
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapGPT-4.1Not measured
Inst. FollowingClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-4.187.4

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

  1. SWE-bench Verified

    Coding
    Source ↗
    A 87.6%B 54.6%
    Winner: Claude Opus 4.7 (Adaptive)Δ 33
    SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; GPT-4.1 scored 54.6%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. GPQA

    Knowledge
    Source ↗
    A 94.2%B 66.3%
    Winner: Claude Opus 4.7 (Adaptive)Δ 27.9
    GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GPT-4.1 scored 66.3%. 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-4.1Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGPT-4.1$2 input / $8 outputGPT-4.1 has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGPT-4.1108 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGPT-4.11.02 sA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGPT-4.11MListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
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%—Not comparable
τ²-bench resultsSource 88.6%47.1%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 49.8%—Not comparable
GDPval-AASource 1495—Not comparable
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
Gert LabsSource —25.65%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
SWE-bench VerifiedSource 87.6%54.6%Claude Opus 4.7 (Adaptive) leads
SWE-bench ProSource 64.3%—Not comparable
Terminal-Bench 2.0Source 69.4%—Not comparable
AA Coding IndexSource 73.6%—Not comparable
AA-SciCodeSource 54.5%38.1%Claude Opus 4.7 (Adaptive) leads
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%61.0%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%0.0%Claude Opus 4.7 (Adaptive) leads
KnowledgeGPT-4.1 wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
GPQASource 94.2%66.3%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%19.4%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%66.6%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%4.6%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%-36.2%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%24.2%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%79.6%Claude Opus 4.7 (Adaptive) leads
MMLUSource —90.2%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
FrontierMath (legacy)Source 43.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source —5.517%Not comparable
FrontierMath v2 (Tier 4)Source —0.000%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%61.2%Claude Opus 4.7 (Adaptive) leads
Design Arena WebsiteSource 13251068Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GPT-4.1Result
AA-IFBenchSource 58.6%43.0%Claude Opus 4.7 (Adaptive) leads
IFEvalSource —87.4%Not comparable
Frequently Asked Questions (3)

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

Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 51.11. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 87.6% and 54.6%.

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

GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 66.3 versus 60. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.

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

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 54.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

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