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Model comparison

Claude Opus 4.6 (Adaptive) vs GLM-5.2

Data verified

Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

64.18/100
Margin
0.2pts
← winning
63.96/100
0 category wins0 category wins

Public leaderboard positions: Claude Opus 4.6 (Adaptive) #35 (Estimated); GLM-5.2 #37 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.6 (Adaptive) and GLM-5.2 share 13 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to Claude Opus 4.6 (Adaptive); 30 to GLM-5.2.

Updated July 23, 2026
Shared results
13
Claude Opus 4.6 (Adaptive) only
3
GLM-5.2 only
30
Comparable categories
0 / 8

Benchmark data for Claude Opus 4.6 (Adaptive) and GLM-5.2 is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

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 (Adaptive) and GLM-5.2
CategoryClaude Opus 4.6 (Adaptive)ΔGLM-5.2
AgenticClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapGLM-5.281.0
CodingClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapGLM-5.262.1
KnowledgeClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapGLM-5.259.6
MathClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapGLM-5.295.9

Operational comparison

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

MetricClaude Opus 4.6 (Adaptive)GLM-5.2Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.6 (Adaptive)Not availableGLM-5.2$1.4 input / $4.4 outputA complete price comparison is not available.
Generation speedtokens per secondClaude Opus 4.6 (Adaptive)Not availableGLM-5.2Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.6 (Adaptive)Not availableGLM-5.2Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.6 (Adaptive)1MGLM-5.21MListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
APEX-Agents-AASource 33.0%33.7%GLM-5.2 leads
τ²-bench resultsSource 92.1%99.1%GLM-5.2 leads
Terminal-Bench 2.0Source 81%Not comparable
MCP AtlasSource 76.8%Not comparable
ToolathlonSource 48.2%Not comparable
AA Agentic IndexSource 43.1%Not comparable
GDPval-AASource 50.7%Not comparable
GDPval-AASource 1514Not comparable
ResearchClawBenchSource 20.7%Not comparable
AA BriefcaseSource 1260Not comparable
AA AutomationBenchSource 27.8%Not comparable
AA EnterpriseOps-GymSource 42.7%Not comparable
AA Harvey LABSource 91.0%Not comparable
AA ITBenchSource 42.7%Not comparable
AA Tau3 BankingSource 26.8%Not comparable
terminalBenchHardSource 50.8%Not comparable
aaTerminalBench21Source 77.9%Not comparable
Coding
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
Vibe Code BenchSource 53.50%Not comparable
AA-SciCodeSource 51.9%50.5%Claude Opus 4.6 (Adaptive) leads
SWE-bench ProSource 62.1%Not comparable
NL2RepoSource 48.9%Not comparable
Terminal-Bench 2.0Source 81.0%Not comparable
ProgramBenchSource 63.7%Not comparable
cursorBench32Source 55.0%Not comparable
AA Coding IndexSource 68.8%Not comparable
Reasoning
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
AA-LCRSource 70.7%71.3%GLM-5.2 leads
CritPtSource 12.6%20.9%GLM-5.2 leads
Knowledge
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
Artificial Analysis Intelligence IndexSource 43.7%51.1%GLM-5.2 leads
AA-GPQA DiamondSource 89.6%89.5%Claude Opus 4.6 (Adaptive) leads
AA-HLESource 36.7%40.1%GLM-5.2 leads
AA-Omniscience IndexSource 13.5%4.0%Claude Opus 4.6 (Adaptive) leads
AA-Omniscience AccuracySource 46.4%25.1%Claude Opus 4.6 (Adaptive) leads
AA-Omniscience Hallucination RateSource 61.3%28.1%GLM-5.2 leads
GPQASource 91.2%Not comparable
GPQA-DSource 91.2%Not comparable
HLESource 54.7%Not comparable
HLE w/o toolsSource 40.5%Not comparable
AA Openness IndexSource 44.4%Not comparable
Math
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
AIME26Source 99.2%Not comparable
HMMT Nov 2025Source 94.4%Not comparable
HMMT Feb 2026Source 92.5%Not comparable
MMAnswerBenchSource 91.0%Not comparable
Multilingual
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
AA Global-MMLU-LiteSource 92.2%Not comparable
Multimodal
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
AA-MMMU-ProSource 75.4%Not comparable
Design Arena WebsiteSource 13251340GLM-5.2 leads
Inst. Following
BenchmarkClaude Opus 4.6 (Adaptive)GLM-5.2Result
AA-IFBenchSource 53.1%73.3%GLM-5.2 leads
Frequently Asked Questions (3)

Can I compare Claude Opus 4.6 (Adaptive) and GLM-5.2 on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for Claude Opus 4.6 (Adaptive) and GLM-5.2 today?

GLM-5.2: $1.40 input / $4.40 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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Last updated: July 23, 2026

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