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Model comparison

GLM-4.7 vs Qwen3.7 Plus

Data verified

Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
Margin
6.1pts
winning →
67.22/100
0 category wins4 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); Qwen3.7 Plus #21 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and Qwen3.7 Plus share 23 comparable benchmark results. 4 of 8 categories are comparable. 7 results are unique to GLM-4.7; 46 to Qwen3.7 Plus.

Updated July 23, 2026
Shared results
23
GLM-4.7 only
7
Qwen3.7 Plus only
46
Comparable categories
4 / 8

Pick Qwen3.7 Plus 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 23 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 Plus is clearly ahead on the BenchAlign aggregate, 67.22 to 61.16. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.7 Plus's sharpest advantage is in mathematics, where it averages 92.9 against 1.8. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 70.3%.

Qwen3.7 Plus 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 Plus
CategoryGLM-4.7ΔQwen3.7 Plus
MathGLM-4.71.8Margin→ 91.1Qwen3.7 Plus92.9
AgenticGLM-4.745.7Margin→ 26.0Qwen3.7 Plus71.7
KnowledgeGLM-4.751.8Margin→ 8.3Qwen3.7 Plus60.1
CodingGLM-4.775.4Margin→ 0.2Qwen3.7 Plus75.6
ReasoningGLM-4.7Not measuredMarginNo overlapQwen3.7 Plus91.7
MultilingualGLM-4.7Not measuredMarginNo overlapQwen3.7 Plus85.4
MultimodalGLM-4.7Not measuredMarginNo overlapQwen3.7 Plus81.5
Inst. FollowingGLM-4.7Not measuredMarginNo overlapQwen3.7 Plus84.5

Decisive benchmark drivers

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

More
A · GLM-4.7B · Qwen3.7 Plus

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 70.3%
    Winner: Qwen3.7 PlusΔ 29.3
    Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.7 Plus scored 70.3%. Qwen3.7 Plus wins this benchmark.

  2. HLE

    Knowledge
    Source ↗
    A 24.8%B 34.7%
    Winner: Qwen3.7 PlusΔ 9.9
    HLE: GLM-4.7 scored 24.8%; Qwen3.7 Plus scored 34.7%. Qwen3.7 Plus wins this benchmark.

  3. LiveCodeBench

    Coding
    Source ↗
    A 84.9%B 89.6%
    Winner: Qwen3.7 PlusΔ 4.7
    LiveCodeBench: GLM-4.7 scored 84.9%; Qwen3.7 Plus scored 89.6%. Qwen3.7 Plus wins this benchmark.

  4. GPQA

    Knowledge
    Source ↗
    A 85.7%B 90.3%
    Winner: Qwen3.7 PlusΔ 4.6
    GPQA: GLM-4.7 scored 85.7%; Qwen3.7 Plus scored 90.3%. Qwen3.7 Plus wins this benchmark.

  5. MMLU-Pro

    Knowledge
    Source ↗
    A 84.3%B 88.5%
    Winner: Qwen3.7 PlusΔ 4.2
    MMLU-Pro: GLM-4.7 scored 84.3%; Qwen3.7 Plus scored 88.5%. Qwen3.7 Plus wins this benchmark.

Operational comparison

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

MetricGLM-4.7Qwen3.7 PlusComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputQwen3.7 PlusNot availableA complete price comparison is not available.
Generation speedtokens per secondGLM-4.782 tok/sQwen3.7 PlusNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sQwen3.7 PlusNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KQwen3.7 Plus1MQwen3.7 Plus lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.7 Plus wins
BenchmarkGLM-4.7Qwen3.7 PlusResult
Terminal-Bench 2.0Source 41%70.3%Qwen3.7 Plus leads
BrowseCompSource 52%—Not comparable
VITA-BenchSource 15.5%45.6%Qwen3.7 Plus leads
AA Agentic IndexSource 25.4%20.8%GLM-4.7 leads
τ²-bench resultsSource 95.9%93%GLM-4.7 leads
Gert LabsSource 39.95%—Not comparable
GDPval-AASource 33.3%21.8%GLM-4.7 leads
GDPval-AASource 1165936GLM-4.7 leads
QwenClawBenchSource —61.8%Not comparable
QwenWebBenchSource —1536Not comparable
Claw-EvalSource —62.7%Not comparable
BFCL v4Source —72.9%Not comparable
MCP AtlasSource —73.2%Not comparable
DeepPlanningSource —62.3%Not comparable
OSWorld-VerifiedSource —73.3%Not comparable
AndroidWorldSource —81.0%Not comparable
APEX-Agents-AASource —22.4%Not comparable
OSWorld 2.0Source —2.8%Not comparable
CodingQwen3.7 Plus wins
BenchmarkGLM-4.7Qwen3.7 PlusResult
SWE-bench VerifiedSource 73.8%77.7%Qwen3.7 Plus leads
LiveCodeBenchSource 84.9%89.6%Qwen3.7 Plus leads
SWE-RebenchSource 58.7%—Not comparable
AA Coding IndexSource 45.3%55.9%Qwen3.7 Plus leads
AA-SciCodeSource 45.1%45.5%Qwen3.7 Plus leads
AA LiveCodeBenchSource 89.4%—Not comparable
Terminal-Bench 2.0Source —70.3%Not comparable
SWE-bench ProSource —57.6%Not comparable
SWE MultilingualSource —75.8%Not comparable
NL2RepoSource —41.1%Not comparable
SciCodeSource —51.3%Not comparable
Reasoning
BenchmarkGLM-4.7Qwen3.7 PlusResult
AA-LCRSource 64.0%65.0%Qwen3.7 Plus leads
CritPtSource 1.7%9.1%Qwen3.7 Plus leads
MRCRv2Source —91.7%Not comparable
KnowledgeQwen3.7 Plus wins
BenchmarkGLM-4.7Qwen3.7 PlusResult
GPQASource 85.7%90.3%Qwen3.7 Plus leads
MMLU-ProSource 84.3%88.5%Qwen3.7 Plus leads
HLESource 24.8%34.7%Qwen3.7 Plus leads
Artificial Analysis Intelligence IndexSource 33.7%39.0%Qwen3.7 Plus leads
AA-GPQA DiamondSource 85.9%90.0%Qwen3.7 Plus leads
AA-HLESource 25.1%33.4%Qwen3.7 Plus leads
AA-Omniscience IndexSource -34.6%2.4%Qwen3.7 Plus leads
AA-Omniscience AccuracySource 29.3%22.2%GLM-4.7 leads
AA-Omniscience Hallucination RateSource 90.3%25.5%Qwen3.7 Plus leads
GPQA-DSource —90.3%Not comparable
MMLU-ReduxSource —94.5%Not comparable
SuperGPQASource —71.4%Not comparable
MMMLUSource —89.0%Not comparable
MathQwen3.7 Plus wins
BenchmarkGLM-4.7Qwen3.7 PlusResult
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 —92.9%Not comparable
IMOAnswerBenchSource —86.0%Not comparable
ApexSource —22.7%Not comparable
Multilingual
BenchmarkGLM-4.7Qwen3.7 PlusResult
MMLU-ProXSource —85.4%Not comparable
NOVA-63Source —58.8%Not comparable
INCLUDESource —83.0%Not comparable
MAXIFESource —88.8%Not comparable
PolyMathSource —84.0%Not comparable
Multimodal
BenchmarkGLM-4.7Qwen3.7 PlusResult
Design Arena WebsiteSource 12551288Qwen3.7 Plus leads
MMMU-ProSource —79%Not comparable
MathVisionSource —90.3%Not comparable
CharXivSource —85.9%Not comparable
ERQASource —69.8%Not comparable
MedXpertQA (MM)Source —71.0%Not comparable
ScreenSpot ProSource —79.0%Not comparable
SimpleVQASource —81.7%Not comparable
MMSearch-PlusSource —41.4%Not comparable
RealWorldQASource —86.9%Not comparable
OmniDocBench 1.5Source —91.4%Not comparable
OCRBench V2Source —70.7%Not comparable
ODINW13Source —51.1%Not comparable
Video-MME (with subtitle)Source —88.0%Not comparable
VideoMMMUSource —85.4%Not comparable
MLVU (M-Avg)Source —87.4%Not comparable
AA-MMMU-ProSource —80.5%Not comparable
Inst. Following
BenchmarkGLM-4.7Qwen3.7 PlusResult
AA-IFBenchSource 67.9%78.0%Qwen3.7 Plus leads
IFEvalSource —94.6%Not comparable
IFBenchSource —79.1%Not comparable
Frequently Asked Questions (5)

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

Qwen3.7 Plus is ahead on BenchLM's BenchAlign leaderboard, 67.22 to 61.16. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 70.3%.

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

Qwen3.7 Plus has the edge for knowledge tasks in this comparison, averaging 60.1 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 Plus?

Qwen3.7 Plus has the edge for coding in this comparison, averaging 75.6 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 Plus?

Qwen3.7 Plus has the edge for math in this comparison, averaging 92.9 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 Plus?

Qwen3.7 Plus has the edge for agentic tasks in this comparison, averaging 71.7 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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