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Model comparison

Claude Opus 4.6 vs GLM-4.7

Data verified

Head-to-head evidence from 22 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

68.59/100
Margin
7.4pts
← winning
61.16/100
3 category wins1 category wins

Public leaderboard positions: Claude Opus 4.6 #16 (Supported); GLM-4.7 #42 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.6 and GLM-4.7 share 22 comparable benchmark results. 4 of 8 categories are comparable. 24 results are unique to Claude Opus 4.6; 8 to GLM-4.7.

Updated July 20, 2026
Shared results
22
Claude Opus 4.6 only
24
GLM-4.7 only
8
Comparable categories
4 / 8

Pick Claude Opus 4.6 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 22 shared benchmark results across 7 evidence categories; 4 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 4.6 is clearly ahead on the BenchAlign aggregate, 68.59 to 61.16. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Claude Opus 4.6's sharpest advantage is in mathematics, where it averages 36.3 against 1.8. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 40.700% to 2.439%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.

Claude Opus 4.6 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. GLM-4.7 is the reasoning model in the pair, while Claude Opus 4.6 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 4.6 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 4.6 and GLM-4.7
CategoryClaude Opus 4.6ΔGLM-4.7
MathClaude Opus 4.636.3Margin← 34.5GLM-4.71.8
AgenticClaude Opus 4.673.0Margin← 27.3GLM-4.745.7
KnowledgeClaude Opus 4.669.1Margin← 17.3GLM-4.751.8
CodingClaude Opus 4.668.1Margin→ 7.3GLM-4.775.4
MultimodalClaude Opus 4.677.3MarginNo overlapGLM-4.7Not measured

Decisive benchmark drivers

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

More
A · Claude Opus 4.6B · GLM-4.7

  1. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 40.700%B 2.439%
    Winner: Claude Opus 4.6Δ 38.3
    FrontierMath v2 (Tiers 1-3): Claude Opus 4.6 scored 40.700%; GLM-4.7 scored 2.439%. Claude Opus 4.6 wins this benchmark.

  2. BrowseComp

    Agentic
    Source ↗
    A 83.7%B 52%
    Winner: Claude Opus 4.6Δ 31.7
    BrowseComp: Claude Opus 4.6 scored 83.7%; GLM-4.7 scored 52%. Claude Opus 4.6 wins this benchmark.

  3. HLE

    Knowledge
    Source ↗
    A 53%B 24.8%
    Winner: Claude Opus 4.6Δ 28.2
    HLE: Claude Opus 4.6 scored 53%; GLM-4.7 scored 24.8%. Claude Opus 4.6 wins this benchmark.

  4. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 65.4%B 41%
    Winner: Claude Opus 4.6Δ 24.4
    Terminal-Bench 2.0: Claude Opus 4.6 scored 65.4%; GLM-4.7 scored 41%. Claude Opus 4.6 wins this benchmark.

  5. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 22.900%B 0.000%
    Winner: Claude Opus 4.6Δ 22.9
    FrontierMath v2 (Tier 4): Claude Opus 4.6 scored 22.900%; GLM-4.7 scored 0.000%. Claude Opus 4.6 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.6GLM-4.7Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.6$5 input / $25 outputGLM-4.7$0 input / $0 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.640 tok/sGLM-4.782 tok/sGLM-4.7 has the higher measured throughput.
First-answer latencyseconds to first tokenClaude Opus 4.61.78 sGLM-4.71.10 sGLM-4.7 reaches the first token sooner.
Context windowmaximum listed tokensClaude Opus 4.61MGLM-4.7200KClaude Opus 4.6 lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.6 wins
BenchmarkClaude Opus 4.6GLM-4.7Result
Terminal-Bench 2.0Source 65.4%41%Claude Opus 4.6 leads
BrowseCompSource 83.7%52%Claude Opus 4.6 leads
OSWorld-VerifiedSource 72.7%—Not comparable
τ²-bench resultsSource 84.8%95.9%GLM-4.7 leads
Claw-EvalSource 70.4%—Not comparable
DeepSearchQASource 73.7%—Not comparable
CyberGymSource 66.6%—Not comparable
Gert LabsSource 61.85%39.95%Claude Opus 4.6 leads
ResearchClawBenchSource 19.9%—Not comparable
JobBenchSource 36.7%—Not comparable
VITA-BenchSource —15.5%Not comparable
AA Agentic IndexSource —25.4%Not comparable
GDPval-AASource —33.3%Not comparable
GDPval-AASource —1165Not comparable
CodingGLM-4.7 wins
BenchmarkClaude Opus 4.6GLM-4.7Result
SWE-bench VerifiedSource 80.8%73.8%Claude Opus 4.6 leads
SWE-bench Verified*Source 75.6%—Not comparable
LiveCodeBench ProSource 70.7%—Not comparable
SWE-bench ProSource 53.4%—Not comparable
SWE-RebenchSource 65.3%58.7%Claude Opus 4.6 leads
React Native EvalsSource 84.1%—Not comparable
Vibe Code BenchSource 57.57%—Not comparable
AA-SciCodeSource 45.7%45.1%Claude Opus 4.6 leads
FrontierCode 1.1 MainSource 26.9%—Not comparable
LiveCodeBenchSource —84.9%Not comparable
AA Coding IndexSource —45.3%Not comparable
AA LiveCodeBenchSource —89.4%Not comparable
Reasoning
BenchmarkClaude Opus 4.6GLM-4.7Result
AA-LCRSource 58.3%64.0%GLM-4.7 leads
CritPtSource 2.8%1.7%Claude Opus 4.6 leads
KnowledgeClaude Opus 4.6 wins
BenchmarkClaude Opus 4.6GLM-4.7Result
GPQASource 91.3%85.7%Claude Opus 4.6 leads
GPQA-DSource 89.2%—Not comparable
SuperGPQASource 95%—Not comparable
MMLU-ProSource 82%84.3%GLM-4.7 leads
MMLU-Pro (Arcee)Source 89.1%—Not comparable
HLESource 53%24.8%Claude Opus 4.6 leads
HLE w/o toolsSource 40%—Not comparable
HealthBench HardSource 14.8%—Not comparable
MedXpertQA (Text)Source 52.1%—Not comparable
Artificial Analysis Intelligence IndexSource 37.8%33.7%Claude Opus 4.6 leads
AA-GPQA DiamondSource 84.0%85.9%GLM-4.7 leads
AA-HLESource 18.6%25.1%GLM-4.7 leads
AA-Omniscience IndexSource 3.5%-34.6%Claude Opus 4.6 leads
AA-Omniscience AccuracySource 45.2%29.3%Claude Opus 4.6 leads
AA-Omniscience Hallucination RateSource 76.0%90.3%Claude Opus 4.6 leads
MathClaude Opus 4.6 wins
BenchmarkClaude Opus 4.6GLM-4.7Result
AIME25 (Arcee)Source 99.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 40.700%2.439%Claude Opus 4.6 leads
FrontierMath v2 (Tier 4)Source 22.900%0.000%Claude Opus 4.6 leads
AIME 2025Source —95.7%Not comparable
Multimodal
BenchmarkClaude Opus 4.6GLM-4.7Result
MMMU-ProSource 77.3%—Not comparable
ERQASource 51.6%—Not comparable
ScreenSpot ProSource 83.1%—Not comparable
MedXpertQA (MM)Source 64.8%—Not comparable
AA-MMMU-ProSource 72.5%—Not comparable
Design Arena WebsiteSource 13281258Claude Opus 4.6 leads
Inst. Following
BenchmarkClaude Opus 4.6GLM-4.7Result
AA-IFBenchSource 44.6%67.9%GLM-4.7 leads
Frequently Asked Questions (5)

Which is better, Claude Opus 4.6 or GLM-4.7?

Claude Opus 4.6 is ahead on BenchLM's BenchAlign leaderboard, 68.59 to 61.16. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 40.700% and 2.439%.

Which is better for knowledge tasks, Claude Opus 4.6 or GLM-4.7?

Claude Opus 4.6 has the edge for knowledge tasks in this comparison, averaging 69.1 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 4.6 or GLM-4.7?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 68.1. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

Which is better for math, Claude Opus 4.6 or GLM-4.7?

Claude Opus 4.6 has the edge for math in this comparison, averaging 36.3 versus 1.8. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.6 or GLM-4.7?

Claude Opus 4.6 has the edge for agentic tasks in this comparison, averaging 73 versus 45.7. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.

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