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Model comparison

Claude Opus 4.7 (Adaptive) vs GLM-4.7

Data verified

Head-to-head evidence from 21 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
5.1pts
← winning
61.16/100
3 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GLM-4.7 #42 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and GLM-4.7 share 21 comparable benchmark results. 3 of 8 categories are comparable. 17 results are unique to Claude Opus 4.7 (Adaptive); 9 to GLM-4.7.

Updated July 23, 2026
Shared results
21
Claude Opus 4.7 (Adaptive) only
17
GLM-4.7 only
9
Comparable categories
3 / 8

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 21 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

Claude Opus 4.7 (Adaptive) is clearly ahead on the BenchAlign aggregate, 66.27 to 61.16. 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 agentic, where it averages 75.1 against 45.7. The single biggest benchmark swing on the page is HLE, 54.7% to 24.8%.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 200K for GLM-4.7.

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 GLM-4.7
CategoryClaude Opus 4.7 (Adaptive)ΔGLM-4.7
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 29.4GLM-4.745.7
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 8.2GLM-4.751.8
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 3.2GLM-4.775.4
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGLM-4.7Not measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGLM-4.71.8
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapGLM-4.7Not measured

Decisive benchmark drivers

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

More
A · Claude Opus 4.7 (Adaptive)B · GLM-4.7

  1. HLE

    Knowledge
    Source ↗
    A 54.7%B 24.8%
    Winner: Claude Opus 4.7 (Adaptive)Δ 29.9
    HLE: Claude Opus 4.7 (Adaptive) scored 54.7%; GLM-4.7 scored 24.8%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 41%
    Winner: Claude Opus 4.7 (Adaptive)Δ 28.4
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GLM-4.7 scored 41%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  3. BrowseComp

    Agentic
    Source ↗
    A 79.3%B 52%
    Winner: Claude Opus 4.7 (Adaptive)Δ 27.3
    BrowseComp: Claude Opus 4.7 (Adaptive) scored 79.3%; GLM-4.7 scored 52%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  4. SWE-bench Verified

    Coding
    Source ↗
    A 87.6%B 73.8%
    Winner: Claude Opus 4.7 (Adaptive)Δ 13.8
    SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; GLM-4.7 scored 73.8%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  5. GPQA

    Knowledge
    Source ↗
    A 94.2%B 85.7%
    Winner: Claude Opus 4.7 (Adaptive)Δ 8.5
    GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GLM-4.7 scored 85.7%. 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)GLM-4.7Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGLM-4.7$0 input / $0 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGLM-4.782 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGLM-4.71.10 sA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGLM-4.7200KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
Terminal-Bench 2.0Source 69.4%41%Claude Opus 4.7 (Adaptive) leads
BrowseCompSource 79.3%52%Claude Opus 4.7 (Adaptive) leads
MCP AtlasSource 77.3%—Not comparable
OSWorld-VerifiedSource 78%—Not comparable
CyberGymSource 73.1%—Not comparable
AA Agentic IndexSource 44.4%25.4%Claude Opus 4.7 (Adaptive) leads
τ²-bench resultsSource 88.6%95.9%GLM-4.7 leads
GDPval-AASource 49.8%33.3%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 14951165Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
VITA-BenchSource —15.5%Not comparable
Gert LabsSource —39.95%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
SWE-bench VerifiedSource 87.6%73.8%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%45.3%Claude Opus 4.7 (Adaptive) leads
AA-SciCodeSource 54.5%45.1%Claude Opus 4.7 (Adaptive) leads
LiveCodeBenchSource —84.9%Not comparable
SWE-RebenchSource —58.7%Not comparable
AA LiveCodeBenchSource —89.4%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%64.0%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%1.7%Claude Opus 4.7 (Adaptive) leads
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
GPQASource 94.2%85.7%Claude Opus 4.7 (Adaptive) leads
GPQA-DSource 94.2%—Not comparable
HLESource 54.7%24.8%Claude Opus 4.7 (Adaptive) leads
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%33.7%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%85.9%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%25.1%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%-34.6%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%29.3%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%90.3%Claude Opus 4.7 (Adaptive) leads
MMLU-ProSource —84.3%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
FrontierMath (legacy)Source 43.8%—Not comparable
AIME 2025Source —95.7%Not comparable
FrontierMath v2 (Tiers 1-3)Source —2.439%Not comparable
FrontierMath v2 (Tier 4)Source —0.000%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
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 13251255Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GLM-4.7Result
AA-IFBenchSource 58.6%67.9%GLM-4.7 leads
Frequently Asked Questions (4)

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

Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 61.16. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 24.8%.

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

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 51.8. 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 GLM-4.7?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 75.4. 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 GLM-4.7?

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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