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Model comparison

Claude Opus 4.7 (Adaptive) vs MiniMax M2.7

Data verified

Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
2.2pts
← winning
64.11/100
2 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and MiniMax M2.7 share 19 comparable benchmark results. 2 of 8 categories are comparable. 19 results are unique to Claude Opus 4.7 (Adaptive); 16 to MiniMax M2.7.

Updated July 23, 2026
Shared results
19
Claude Opus 4.7 (Adaptive) only
19
MiniMax M2.7 only
16
Comparable categories
2 / 8

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. MiniMax M2.7 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 19 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) has the cleaner BenchAlign overall profile here, landing at 66.27 versus 64.11. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

Claude Opus 4.7 (Adaptive)'s sharpest advantage is in coding, where it averages 78.6 against 53.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 69.4% to 57%.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 20.8x on output cost alone. Claude Opus 4.7 (Adaptive) is the reasoning model in the pair, while MiniMax M2.7 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. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 200K for MiniMax M2.7.

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 MiniMax M2.7
CategoryClaude Opus 4.7 (Adaptive)ΔMiniMax M2.7
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 25.3MiniMax M2.753.3
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 18.1MiniMax M2.757.0
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapMiniMax M2.7Not measured
KnowledgeClaude Opus 4.7 (Adaptive)60.0MarginNo overlapMiniMax M2.7Not measured
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapMiniMax M2.7Not measured

Decisive benchmark drivers

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

More
A · Claude Opus 4.7 (Adaptive)B · MiniMax M2.7

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 57%
    Winner: Claude Opus 4.7 (Adaptive)Δ 12.4
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; MiniMax M2.7 scored 57%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 56.2%
    Winner: Claude Opus 4.7 (Adaptive)Δ 8.1
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; MiniMax M2.7 scored 56.2%. 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)MiniMax M2.7Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputMiniMax M2.7$0.3 input / $1.2 outputMiniMax M2.7 has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableMiniMax M2.745 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableMiniMax M2.72.53 sA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MMiniMax M2.7200KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
Terminal-Bench 2.0Source 69.4%57%Claude Opus 4.7 (Adaptive) leads
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%25.6%Claude Opus 4.7 (Adaptive) leads
τ²-bench resultsSource 88.6%84.8%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 49.8%32.9%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 14951158Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
ToolathlonSource —46.3%Not comparable
MLE-Bench LiteSource —66.6%Not comparable
MM-ClawBenchSource —62.7%Not comparable
Claw-EvalSource —48.7%Not comparable
APEX-Agents-AASource —10.6%Not comparable
Gert LabsSource —40.40%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
SWE-bench VerifiedSource 87.6%—Not comparable
SWE-bench ProSource 64.3%56.2%Claude Opus 4.7 (Adaptive) leads
Terminal-Bench 2.0Source 69.4%—Not comparable
AA Coding IndexSource 73.6%52.6%Claude Opus 4.7 (Adaptive) leads
AA-SciCodeSource 54.5%47.0%Claude Opus 4.7 (Adaptive) leads
SWE-bench Verified*Source —75.4%Not comparable
SWE-RebenchSource —51.9%Not comparable
SWE MultilingualSource —76.5%Not comparable
Multi-SWE BenchSource —52.7%Not comparable
VIBE-ProSource —55.6%Not comparable
NL2RepoSource —39.8%Not comparable
Vibe Code BenchSource —27.04%Not comparable
React Native EvalsSource —71.4%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%68.7%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%0.6%Claude Opus 4.7 (Adaptive) leads
Knowledge
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
GPQASource 94.2%—Not comparable
GPQA-DSource 94.2%87.0%Claude Opus 4.7 (Adaptive) leads
HLESource 54.7%—Not comparable
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%38.1%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%87.4%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%28.1%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%0.7%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%26.1%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%34.4%MiniMax M2.7 leads
MMLU-Pro (Arcee)Source —80.8%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
FrontierMath (legacy)Source 43.8%—Not comparable
AIME25 (Arcee)Source —80.0%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%—Not comparable
Design Arena WebsiteSource 13251275Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)MiniMax M2.7Result
AA-IFBenchSource 58.6%75.7%MiniMax M2.7 leads
Frequently Asked Questions (3)

Which is better, Claude Opus 4.7 (Adaptive) or MiniMax M2.7?

Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 64.11. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 69.4% and 57%.

Which is better for coding, Claude Opus 4.7 (Adaptive) or MiniMax M2.7?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 53.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or MiniMax M2.7?

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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