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Model comparison

GLM-5.2 vs Qwen3.7 Max

Data verified

Head-to-head evidence from 33 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

63.96/100
Margin
8.9pts
winning →
72.84/100
1 category wins3 category wins

Public leaderboard positions: GLM-5.2 #37 (Estimated); Qwen3.7 Max #10 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5.2 and Qwen3.7 Max share 33 comparable benchmark results. 4 of 8 categories are comparable. 10 results are unique to GLM-5.2; 25 to Qwen3.7 Max.

Updated July 23, 2026
Shared results
33
GLM-5.2 only
10
Qwen3.7 Max only
25
Comparable categories
4 / 8

Pick Qwen3.7 Max if you want the stronger benchmark profile. GLM-5.2 only becomes the better choice if agentic is the priority.

Confidence note. This is a partial-evidence comparison with 33 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

Qwen3.7 Max is clearly ahead on the BenchAlign aggregate, 72.84 to 63.96. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.7 Max's sharpest advantage is in coding, where it averages 77.9 against 62.1. The single biggest benchmark swing on the page is HLE, 54.7% to 41.4%. GLM-5.2 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.

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 GLM-5.2 and Qwen3.7 Max
CategoryGLM-5.2ΔQwen3.7 Max
CodingGLM-5.262.1Margin→ 15.8Qwen3.7 Max77.9
AgenticGLM-5.281.0Margin← 11.3Qwen3.7 Max69.7
KnowledgeGLM-5.259.6Margin→ 4.6Qwen3.7 Max64.2
MathGLM-5.295.9Margin→ 1.2Qwen3.7 Max97.1
ReasoningGLM-5.2Not measuredMarginNo overlapQwen3.7 Max90.4
MultilingualGLM-5.2Not measuredMarginNo overlapQwen3.7 Max87.0
Inst. FollowingGLM-5.2Not measuredMarginNo overlapQwen3.7 Max84.4

Decisive benchmark drivers

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

More
A · GLM-5.2B · Qwen3.7 Max

  1. HLE

    Knowledge
    Source ↗
    A 54.7%B 41.4%
    Winner: GLM-5.2Δ 13.3
    HLE: GLM-5.2 scored 54.7%; Qwen3.7 Max scored 41.4%. GLM-5.2 wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 81%B 69.7%
    Winner: GLM-5.2Δ 11.3
    Terminal-Bench 2.0: GLM-5.2 scored 81%; Qwen3.7 Max scored 69.7%. GLM-5.2 wins this benchmark.

  3. HMMT Feb 2026

    Math
    Source ↗
    A 92.5%B 97.1%
    Winner: Qwen3.7 MaxΔ 4.6
    HMMT Feb 2026: GLM-5.2 scored 92.5%; Qwen3.7 Max scored 97.1%. Qwen3.7 Max wins this benchmark.

  4. SWE-bench Pro

    Coding
    Source ↗
    A 62.1%B 60.6%
    Winner: GLM-5.2Δ 1.5
    SWE-bench Pro: GLM-5.2 scored 62.1%; Qwen3.7 Max scored 60.6%. GLM-5.2 wins this benchmark.

  5. GPQA

    Knowledge
    Source ↗
    A 91.2%B 92.4%
    Winner: Qwen3.7 MaxΔ 1.2
    GPQA: GLM-5.2 scored 91.2%; Qwen3.7 Max scored 92.4%. Qwen3.7 Max wins this benchmark.

Operational comparison

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

MetricGLM-5.2Qwen3.7 MaxComparison
Input / output priceUSD per 1M tokensGLM-5.2$1.4 input / $4.4 outputQwen3.7 MaxNot availableA complete price comparison is not available.
Generation speedtokens per secondGLM-5.2Not availableQwen3.7 MaxNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-5.2Not availableQwen3.7 MaxNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-5.21MQwen3.7 Max1MListed context windows are equal.

Benchmark Deep Dive

AgenticGLM-5.2 wins
BenchmarkGLM-5.2Qwen3.7 MaxResult
Terminal-Bench 2.0Source 81%69.7%GLM-5.2 leads
MCP AtlasSource 76.8%76.4%GLM-5.2 leads
ToolathlonSource 48.2%—Not comparable
AA Agentic IndexSource 43.1%30.6%GLM-5.2 leads
τ²-bench resultsSource 99.1%94.7%GLM-5.2 leads
GDPval-AASource 50.7%38.7%GLM-5.2 leads
GDPval-AASource 15141273GLM-5.2 leads
APEX-Agents-AASource 33.7%—Not comparable
ResearchClawBenchSource 20.7%18.7%GLM-5.2 leads
AA BriefcaseSource 1260908GLM-5.2 leads
AA AutomationBenchSource 27.8%25.6%GLM-5.2 leads
AA EnterpriseOps-GymSource 42.7%45.0%Qwen3.7 Max leads
AA Harvey LABSource 91.0%83.4%GLM-5.2 leads
AA ITBenchSource 42.7%42.5%GLM-5.2 leads
AA Tau3 BankingSource 26.8%—Not comparable
terminalBenchHardSource 50.8%50.8%Tie
aaTerminalBench21Source 77.9%74.5%GLM-5.2 leads
QwenClawBenchSource —64.3%Not comparable
QwenWebBenchSource —1568Not comparable
Claw-EvalSource —65.2%Not comparable
BFCL v4Source —75.0%Not comparable
VITA-BenchSource —47.9%Not comparable
HLE w/ toolsSource —53.5%Not comparable
Gert LabsSource —64.27%Not comparable
CodingQwen3.7 Max wins
BenchmarkGLM-5.2Qwen3.7 MaxResult
SWE-bench ProSource 62.1%60.6%GLM-5.2 leads
NL2RepoSource 48.9%47.2%GLM-5.2 leads
Terminal-Bench 2.0Source 81.0%69.7%GLM-5.2 leads
ProgramBenchSource 63.7%—Not comparable
cursorBench32Source 55.0%—Not comparable
AA Coding IndexSource 68.8%66.0%GLM-5.2 leads
AA-SciCodeSource 50.5%48.8%GLM-5.2 leads
SWE-bench VerifiedSource —80.4%Not comparable
SWE MultilingualSource —78.3%Not comparable
SciCodeSource —53.5%Not comparable
LiveCodeBenchSource —91.6%Not comparable
Reasoning
BenchmarkGLM-5.2Qwen3.7 MaxResult
CritPtSource 20.9%13.4%GLM-5.2 leads
AA-LCRSource 71.3%69.0%GLM-5.2 leads
MRCRv2Source —90.4%Not comparable
KnowledgeQwen3.7 Max wins
BenchmarkGLM-5.2Qwen3.7 MaxResult
GPQASource 91.2%92.4%Qwen3.7 Max leads
GPQA-DSource 91.2%92.4%Qwen3.7 Max leads
HLESource 54.7%41.4%GLM-5.2 leads
HLE w/o toolsSource 40.5%—Not comparable
Artificial Analysis Intelligence IndexSource 51.1%46.0%GLM-5.2 leads
AA-GPQA DiamondSource 89.5%92.3%Qwen3.7 Max leads
AA-HLESource 40.1%38.1%GLM-5.2 leads
AA-Omniscience IndexSource 4.0%14.1%Qwen3.7 Max leads
AA-Omniscience AccuracySource 25.1%30.1%Qwen3.7 Max leads
AA-Omniscience Hallucination RateSource 28.1%22.9%Qwen3.7 Max leads
AA Openness IndexSource 44.4%—Not comparable
MMLU-ProSource —89.6%Not comparable
MMLU-ReduxSource —95%Not comparable
SuperGPQASource —73.6%Not comparable
MMMLUSource —90.3%Not comparable
MathQwen3.7 Max wins
BenchmarkGLM-5.2Qwen3.7 MaxResult
AIME26Source 99.2%—Not comparable
HMMT Nov 2025Source 94.4%—Not comparable
HMMT Feb 2026Source 92.5%97.1%Qwen3.7 Max leads
MMAnswerBenchSource 91.0%—Not comparable
IMOAnswerBenchSource —90.0%Not comparable
ApexSource —44.5%Not comparable
Multilingual
BenchmarkGLM-5.2Qwen3.7 MaxResult
MMLU-ProXSource —87%Not comparable
NOVA-63Source —59.0%Not comparable
INCLUDESource —86.2%Not comparable
MAXIFESource —89.2%Not comparable
PolyMathSource —86.5%Not comparable
Multimodal
BenchmarkGLM-5.2Qwen3.7 MaxResult
Design Arena WebsiteSource 13401293GLM-5.2 leads
Inst. Following
BenchmarkGLM-5.2Qwen3.7 MaxResult
AA-IFBenchSource 73.3%80.5%Qwen3.7 Max leads
IFEvalSource —94.3%Not comparable
IFBenchSource —79.1%Not comparable
Frequently Asked Questions (5)

Which is better, GLM-5.2 or Qwen3.7 Max?

Qwen3.7 Max is ahead on BenchLM's BenchAlign leaderboard, 72.84 to 63.96. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 41.4%.

Which is better for knowledge tasks, GLM-5.2 or Qwen3.7 Max?

Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 64.2 versus 59.6. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5.2 or Qwen3.7 Max?

Qwen3.7 Max has the edge for coding in this comparison, averaging 77.9 versus 62.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5.2 or Qwen3.7 Max?

Qwen3.7 Max has the edge for math in this comparison, averaging 97.1 versus 95.9. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5.2 or Qwen3.7 Max?

GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 69.7. Inside this category, AA Briefcase is the benchmark that creates the most daylight between them.

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