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Model comparison

Claude Opus 4.7 (Adaptive) vs GLM-5.1

Data verified

Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
1.5pts
winning →
67.74/100
3 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GLM-5.1 #18 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and GLM-5.1 share 23 comparable benchmark results. 3 of 8 categories are comparable. 15 results are unique to Claude Opus 4.7 (Adaptive); 13 to GLM-5.1.

Updated July 23, 2026
Shared results
23
Claude Opus 4.7 (Adaptive) only
15
GLM-5.1 only
13
Comparable categories
3 / 8

Pick GLM-5.1 if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if coding is the priority or you need the larger 1M context window.

Confidence note. This is a partial-evidence comparison with 23 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

GLM-5.1 has the cleaner BenchAlign overall profile here, landing at 67.74 versus 66.27. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.1. That is roughly 5.7x on output cost alone. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 203K for GLM-5.1.

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-5.1
CategoryClaude Opus 4.7 (Adaptive)ΔGLM-5.1
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 17.3GLM-5.161.3
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 9.7GLM-5.165.4
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 7.7GLM-5.152.3
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGLM-5.1Not measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGLM-5.162.0
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapGLM-5.1Not 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-5.1

  1. BrowseComp

    Agentic
    Source ↗
    A 79.3%B 68%
    Winner: Claude Opus 4.7 (Adaptive)Δ 11.3
    BrowseComp: Claude Opus 4.7 (Adaptive) scored 79.3%; GLM-5.1 scored 68%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 63.5%
    Winner: Claude Opus 4.7 (Adaptive)Δ 5.9
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GLM-5.1 scored 63.5%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 58.4%
    Winner: Claude Opus 4.7 (Adaptive)Δ 5.9
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; GLM-5.1 scored 58.4%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  4. HLE

    Knowledge
    Source ↗
    A 54.7%B 52.3%
    Winner: Claude Opus 4.7 (Adaptive)Δ 2.4
    HLE: Claude Opus 4.7 (Adaptive) scored 54.7%; GLM-5.1 scored 52.3%. 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)GLM-5.1Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGLM-5.1$1.4 input / $4.4 outputGLM-5.1 has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGLM-5.1Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGLM-5.1Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGLM-5.1203KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
Terminal-Bench 2.0Source 69.4%63.5%Claude Opus 4.7 (Adaptive) leads
BrowseCompSource 79.3%68%Claude Opus 4.7 (Adaptive) leads
MCP AtlasSource 77.3%71.8%Claude Opus 4.7 (Adaptive) leads
OSWorld-VerifiedSource 78%—Not comparable
CyberGymSource 73.1%68.7%Claude Opus 4.7 (Adaptive) leads
AA Agentic IndexSource 44.4%29.9%Claude Opus 4.7 (Adaptive) leads
τ²-bench resultsSource 88.6%97.7%GLM-5.1 leads
GDPval-AASource 49.8%37.8%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 14951257Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
τ³-bench resultsSource —70.6%Not comparable
Claw-EvalSource —62.3%Not comparable
Gert LabsSource —60.11%Not comparable
ResearchClawBenchSource —18.2%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
SWE-bench VerifiedSource 87.6%—Not comparable
SWE-bench ProSource 64.3%58.4%Claude Opus 4.7 (Adaptive) leads
Terminal-Bench 2.0Source 69.4%—Not comparable
AA Coding IndexSource 73.6%55.8%Claude Opus 4.7 (Adaptive) leads
AA-SciCodeSource 54.5%43.8%Claude Opus 4.7 (Adaptive) leads
NL2RepoSource —42.7%Not comparable
SWE-RebenchSource —62.7%Not comparable
Vibe Code BenchSource —31.46%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%62.3%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%4.6%Claude Opus 4.7 (Adaptive) leads
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
GPQASource 94.2%—Not comparable
GPQA-DSource 94.2%86.2%Claude Opus 4.7 (Adaptive) leads
HLESource 54.7%52.3%Claude Opus 4.7 (Adaptive) leads
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%40.2%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%86.8%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%28.0%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%1.9%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%29.4%GLM-5.1 leads
Math
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
FrontierMath (legacy)Source 43.8%—Not comparable
AIME26Source —95.3%Not comparable
HMMT Nov 2025Source —94.0%Not comparable
HMMT Feb 2026Source —82.6%Not comparable
MMAnswerBenchSource —83.8%Not comparable
FrontierMath v2 (Tiers 1-3)Source —33.448%Not comparable
FrontierMath v2 (Tier 4)Source —12.500%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
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 13251305Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GLM-5.1Result
AA-IFBenchSource 58.6%76.3%GLM-5.1 leads
Frequently Asked Questions (4)

Which is better, Claude Opus 4.7 (Adaptive) or GLM-5.1?

GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 66.27. The biggest single separator in this matchup is BrowseComp, where the scores are 79.3% and 68%.

Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or GLM-5.1?

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 52.3. 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-5.1?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 61.3. 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 GLM-5.1?

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 65.4. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Opus 4.7 (Adaptive)
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Model the full break-even

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