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Model comparison

GLM-4.7 vs Qwen3.7 Max

Data verified

Head-to-head evidence from 24 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
Margin
11.7pts
winning →
72.84/100
0 category wins4 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); Qwen3.7 Max #10 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and Qwen3.7 Max share 24 comparable benchmark results. 4 of 8 categories are comparable. 6 results are unique to GLM-4.7; 34 to Qwen3.7 Max.

Updated July 23, 2026
Shared results
24
GLM-4.7 only
6
Qwen3.7 Max only
34
Comparable categories
4 / 8

Pick Qwen3.7 Max if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.

Confidence note. This is a partial-evidence comparison with 24 shared benchmark results across 6 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 61.16. 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 mathematics, where it averages 97.1 against 1.8. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 69.7%.

Qwen3.7 Max 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 GLM-4.7 and Qwen3.7 Max
CategoryGLM-4.7ΔQwen3.7 Max
MathGLM-4.71.8Margin→ 95.3Qwen3.7 Max97.1
AgenticGLM-4.745.7Margin→ 24.0Qwen3.7 Max69.7
KnowledgeGLM-4.751.8Margin→ 12.4Qwen3.7 Max64.2
CodingGLM-4.775.4Margin→ 2.5Qwen3.7 Max77.9
ReasoningGLM-4.7Not measuredMarginNo overlapQwen3.7 Max90.4
MultilingualGLM-4.7Not measuredMarginNo overlapQwen3.7 Max87.0
Inst. FollowingGLM-4.7Not measuredMarginNo overlapQwen3.7 Max84.4

Decisive benchmark drivers

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

More
A · GLM-4.7B · Qwen3.7 Max

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 69.7%
    Winner: Qwen3.7 MaxΔ 28.7
    Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.7 Max scored 69.7%. Qwen3.7 Max wins this benchmark.

  2. HLE

    Knowledge
    Source ↗
    A 24.8%B 41.4%
    Winner: Qwen3.7 MaxΔ 16.6
    HLE: GLM-4.7 scored 24.8%; Qwen3.7 Max scored 41.4%. Qwen3.7 Max wins this benchmark.

  3. GPQA

    Knowledge
    Source ↗
    A 85.7%B 92.4%
    Winner: Qwen3.7 MaxΔ 6.7
    GPQA: GLM-4.7 scored 85.7%; Qwen3.7 Max scored 92.4%. Qwen3.7 Max wins this benchmark.

  4. LiveCodeBench

    Coding
    Source ↗
    A 84.9%B 91.6%
    Winner: Qwen3.7 MaxΔ 6.7
    LiveCodeBench: GLM-4.7 scored 84.9%; Qwen3.7 Max scored 91.6%. Qwen3.7 Max wins this benchmark.

  5. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 80.4%
    Winner: Qwen3.7 MaxΔ 6.6
    SWE-bench Verified: GLM-4.7 scored 73.8%; Qwen3.7 Max scored 80.4%. Qwen3.7 Max wins this benchmark.

Operational comparison

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

MetricGLM-4.7Qwen3.7 MaxComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputQwen3.7 MaxNot availableA complete price comparison is not available.
Generation speedtokens per secondGLM-4.782 tok/sQwen3.7 MaxNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sQwen3.7 MaxNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KQwen3.7 Max1MQwen3.7 Max lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.7 Max wins
BenchmarkGLM-4.7Qwen3.7 MaxResult
Terminal-Bench 2.0Source 41%69.7%Qwen3.7 Max leads
BrowseCompSource 52%—Not comparable
VITA-BenchSource 15.5%47.9%Qwen3.7 Max leads
AA Agentic IndexSource 25.4%30.6%Qwen3.7 Max leads
τ²-bench resultsSource 95.9%94.7%GLM-4.7 leads
Gert LabsSource 39.95%64.27%Qwen3.7 Max leads
GDPval-AASource 33.3%38.7%Qwen3.7 Max leads
GDPval-AASource 11651273Qwen3.7 Max leads
QwenClawBenchSource —64.3%Not comparable
QwenWebBenchSource —1568Not comparable
Claw-EvalSource —65.2%Not comparable
BFCL v4Source —75.0%Not comparable
MCP AtlasSource —76.4%Not comparable
HLE w/ toolsSource —53.5%Not comparable
ResearchClawBenchSource —18.7%Not comparable
AA BriefcaseSource —908Not comparable
AA AutomationBenchSource —25.6%Not comparable
AA EnterpriseOps-GymSource —45.0%Not comparable
AA ITBenchSource —42.5%Not comparable
terminalBenchHardSource —50.8%Not comparable
aaTerminalBench21Source —74.5%Not comparable
AA Harvey LABSource —83.4%Not comparable
CodingQwen3.7 Max wins
BenchmarkGLM-4.7Qwen3.7 MaxResult
SWE-bench VerifiedSource 73.8%80.4%Qwen3.7 Max leads
LiveCodeBenchSource 84.9%91.6%Qwen3.7 Max leads
SWE-RebenchSource 58.7%—Not comparable
AA Coding IndexSource 45.3%66.0%Qwen3.7 Max leads
AA-SciCodeSource 45.1%48.8%Qwen3.7 Max leads
AA LiveCodeBenchSource 89.4%—Not comparable
SWE-bench ProSource —60.6%Not comparable
SWE MultilingualSource —78.3%Not comparable
NL2RepoSource —47.2%Not comparable
SciCodeSource —53.5%Not comparable
Terminal-Bench 2.0Source —69.7%Not comparable
Reasoning
BenchmarkGLM-4.7Qwen3.7 MaxResult
AA-LCRSource 64.0%69.0%Qwen3.7 Max leads
CritPtSource 1.7%13.4%Qwen3.7 Max leads
MRCRv2Source —90.4%Not comparable
KnowledgeQwen3.7 Max wins
BenchmarkGLM-4.7Qwen3.7 MaxResult
GPQASource 85.7%92.4%Qwen3.7 Max leads
MMLU-ProSource 84.3%89.6%Qwen3.7 Max leads
HLESource 24.8%41.4%Qwen3.7 Max leads
Artificial Analysis Intelligence IndexSource 33.7%46.0%Qwen3.7 Max leads
AA-GPQA DiamondSource 85.9%92.3%Qwen3.7 Max leads
AA-HLESource 25.1%38.1%Qwen3.7 Max leads
AA-Omniscience IndexSource -34.6%14.1%Qwen3.7 Max leads
AA-Omniscience AccuracySource 29.3%30.1%Qwen3.7 Max leads
AA-Omniscience Hallucination RateSource 90.3%22.9%Qwen3.7 Max leads
GPQA-DSource —92.4%Not comparable
MMLU-ReduxSource —95%Not comparable
SuperGPQASource —73.6%Not comparable
MMMLUSource —90.3%Not comparable
MathQwen3.7 Max wins
BenchmarkGLM-4.7Qwen3.7 MaxResult
AIME 2025Source 95.7%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 2.439%—Not comparable
FrontierMath v2 (Tier 4)Source 0.000%—Not comparable
HMMT Feb 2026Source —97.1%Not comparable
IMOAnswerBenchSource —90.0%Not comparable
ApexSource —44.5%Not comparable
Multilingual
BenchmarkGLM-4.7Qwen3.7 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-4.7Qwen3.7 MaxResult
Design Arena WebsiteSource 12551293Qwen3.7 Max leads
Inst. Following
BenchmarkGLM-4.7Qwen3.7 MaxResult
AA-IFBenchSource 67.9%80.5%Qwen3.7 Max leads
IFEvalSource —94.3%Not comparable
IFBenchSource —79.1%Not comparable
Frequently Asked Questions (5)

Which is better, GLM-4.7 or Qwen3.7 Max?

Qwen3.7 Max is ahead on BenchLM's BenchAlign leaderboard, 72.84 to 61.16. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 69.7%.

Which is better for knowledge tasks, GLM-4.7 or Qwen3.7 Max?

Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 64.2 versus 51.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-4.7 or Qwen3.7 Max?

Qwen3.7 Max has the edge for coding in this comparison, averaging 77.9 versus 75.4. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for math, GLM-4.7 or Qwen3.7 Max?

Qwen3.7 Max has the edge for math in this comparison, averaging 97.1 versus 1.8. GLM-4.7 stays close enough that the answer can still flip depending on your workload.

Which is better for agentic tasks, GLM-4.7 or Qwen3.7 Max?

Qwen3.7 Max has the edge for agentic tasks in this comparison, averaging 69.7 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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