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Model comparison

Claude Opus 4.6 vs GLM-5.1

Data verified

Head-to-head evidence from 25 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

68.59/100
Margin
0.9pts
← winning
67.74/100
3 category wins1 category wins

Public leaderboard positions: Claude Opus 4.6 #16 (Supported); GLM-5.1 #18 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.6 and GLM-5.1 share 25 comparable benchmark results. 4 of 8 categories are comparable. 21 results are unique to Claude Opus 4.6; 11 to GLM-5.1.

Updated July 23, 2026
Shared results
25
Claude Opus 4.6 only
21
GLM-5.1 only
11
Comparable categories
4 / 8

Pick Claude Opus 4.6 if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 25 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 has the cleaner BenchAlign overall profile here, landing at 68.59 versus 67.74. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

Claude Opus 4.6's sharpest advantage is in knowledge, where it averages 69.1 against 52.3. The single biggest benchmark swing on the page is BrowseComp, 83.7% to 68%. GLM-5.1 does hit back in mathematics, 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 $1.40 input / $4.40 output per 1M tokens for GLM-5.1. That is roughly 5.7x on output cost alone. GLM-5.1 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 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 4.6 and GLM-5.1
CategoryClaude Opus 4.6ΔGLM-5.1
MathClaude Opus 4.636.3Margin→ 25.7GLM-5.162.0
KnowledgeClaude Opus 4.669.1Margin← 16.8GLM-5.152.3
AgenticClaude Opus 4.673.0Margin← 7.6GLM-5.165.4
CodingClaude Opus 4.668.1Margin← 6.8GLM-5.161.3
MultimodalClaude Opus 4.677.3MarginNo overlapGLM-5.1Not measured

Decisive benchmark drivers

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

More
A · Claude Opus 4.6B · GLM-5.1

  1. BrowseComp

    Agentic
    Source ↗
    A 83.7%B 68%
    Winner: Claude Opus 4.6Δ 15.7
    BrowseComp: Claude Opus 4.6 scored 83.7%; GLM-5.1 scored 68%. Claude Opus 4.6 wins this benchmark.

  2. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 22.900%B 12.500%
    Winner: Claude Opus 4.6Δ 10.4
    FrontierMath v2 (Tier 4): Claude Opus 4.6 scored 22.900%; GLM-5.1 scored 12.500%. Claude Opus 4.6 wins this benchmark.

  3. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 40.700%B 33.448%
    Winner: Claude Opus 4.6Δ 7.3
    FrontierMath v2 (Tiers 1-3): Claude Opus 4.6 scored 40.700%; GLM-5.1 scored 33.448%. Claude Opus 4.6 wins this benchmark.

  4. SWE-bench Pro

    Coding
    Source ↗
    A 53.4%B 58.4%
    Winner: GLM-5.1Δ 5
    SWE-bench Pro: Claude Opus 4.6 scored 53.4%; GLM-5.1 scored 58.4%. GLM-5.1 wins this benchmark.

  5. SWE-Rebench

    Coding
    Source ↗
    A 65.3%B 62.7%
    Winner: Claude Opus 4.6Δ 2.6
    SWE-Rebench: Claude Opus 4.6 scored 65.3%; GLM-5.1 scored 62.7%. 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-5.1Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.6$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 4.640 tok/sGLM-5.1Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.61.78 sGLM-5.1Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.61MGLM-5.1203KClaude Opus 4.6 lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.6 wins
BenchmarkClaude Opus 4.6GLM-5.1Result
Terminal-Bench 2.0Source 65.4%63.5%Claude Opus 4.6 leads
BrowseCompSource 83.7%68%Claude Opus 4.6 leads
OSWorld-VerifiedSource 72.7%—Not comparable
τ²-bench resultsSource 84.8%97.7%GLM-5.1 leads
Claw-EvalSource 70.4%62.3%Claude Opus 4.6 leads
DeepSearchQASource 73.7%—Not comparable
CyberGymSource 66.6%68.7%GLM-5.1 leads
Gert LabsSource 61.85%60.11%Claude Opus 4.6 leads
ResearchClawBenchSource 19.9%18.2%Claude Opus 4.6 leads
JobBenchSource 36.7%—Not comparable
τ³-bench resultsSource —70.6%Not comparable
MCP AtlasSource —71.8%Not comparable
AA Agentic IndexSource —29.9%Not comparable
GDPval-AASource —37.8%Not comparable
GDPval-AASource —1257Not comparable
CodingClaude Opus 4.6 wins
BenchmarkClaude Opus 4.6GLM-5.1Result
SWE-bench VerifiedSource 80.8%—Not comparable
SWE-bench Verified*Source 75.6%—Not comparable
LiveCodeBench ProSource 70.7%—Not comparable
SWE-bench ProSource 53.4%58.4%GLM-5.1 leads
SWE-RebenchSource 65.3%62.7%Claude Opus 4.6 leads
React Native EvalsSource 84.1%—Not comparable
Vibe Code BenchSource 57.57%31.46%Claude Opus 4.6 leads
AA-SciCodeSource 45.7%43.8%Claude Opus 4.6 leads
FrontierCode 1.1 MainSource 26.9%—Not comparable
NL2RepoSource —42.7%Not comparable
AA Coding IndexSource —55.8%Not comparable
Reasoning
BenchmarkClaude Opus 4.6GLM-5.1Result
AA-LCRSource 58.3%62.3%GLM-5.1 leads
CritPtSource 2.8%4.6%GLM-5.1 leads
KnowledgeClaude Opus 4.6 wins
BenchmarkClaude Opus 4.6GLM-5.1Result
GPQASource 91.3%—Not comparable
GPQA-DSource 89.2%86.2%Claude Opus 4.6 leads
SuperGPQASource 95%—Not comparable
MMLU-ProSource 82%—Not comparable
MMLU-Pro (Arcee)Source 89.1%—Not comparable
HLESource 53%52.3%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%40.2%GLM-5.1 leads
AA-GPQA DiamondSource 84.0%86.8%GLM-5.1 leads
AA-HLESource 18.6%28.0%GLM-5.1 leads
AA-Omniscience IndexSource 3.5%1.9%Claude Opus 4.6 leads
AA-Omniscience AccuracySource 45.2%24.2%Claude Opus 4.6 leads
AA-Omniscience Hallucination RateSource 76.0%29.4%GLM-5.1 leads
MathGLM-5.1 wins
BenchmarkClaude Opus 4.6GLM-5.1Result
AIME25 (Arcee)Source 99.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 40.700%33.448%Claude Opus 4.6 leads
FrontierMath v2 (Tier 4)Source 22.900%12.500%Claude Opus 4.6 leads
AIME26Source —95.3%Not comparable
HMMT Nov 2025Source —94.0%Not comparable
HMMT Feb 2026Source —82.6%Not comparable
MMAnswerBenchSource —83.8%Not comparable
Multimodal
BenchmarkClaude Opus 4.6GLM-5.1Result
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 13251305Claude Opus 4.6 leads
Inst. Following
BenchmarkClaude Opus 4.6GLM-5.1Result
AA-IFBenchSource 44.6%76.3%GLM-5.1 leads
Frequently Asked Questions (5)

Which is better, Claude Opus 4.6 or GLM-5.1?

Claude Opus 4.6 is ahead on BenchLM's BenchAlign leaderboard, 68.59 to 67.74. The biggest single separator in this matchup is BrowseComp, where the scores are 83.7% and 68%.

Which is better for knowledge tasks, Claude Opus 4.6 or GLM-5.1?

Claude Opus 4.6 has the edge for knowledge tasks in this comparison, averaging 69.1 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, Claude Opus 4.6 or GLM-5.1?

Claude Opus 4.6 has the edge for coding in this comparison, averaging 68.1 versus 61.3. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.

Which is better for math, Claude Opus 4.6 or GLM-5.1?

GLM-5.1 has the edge for math in this comparison, averaging 62 versus 36.3. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.6 or GLM-5.1?

Claude Opus 4.6 has the edge for agentic tasks in this comparison, averaging 73 versus 65.4. Inside this category, BrowseComp 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 4.6
API / mo$22,500
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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