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GLM 4.7 vs Qwen3.7 Plus

Updated October 21, 2026. 47 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace Qwen3.7 Plus
GLM 4.7Z.ai
Coding
55.1
Overall
22.2
Output $/M
$2.20
4
Qwen3.7 PlusQwen
Coding
33.2
Overall
25.2
Output $/M
$1.28
43
vs boards won of 47 shared

Decision reading

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus.

Pick Qwen3.7 Plus when output tokens cost 1.7x less, or when the context window is larger, 1M against 205K tokens.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BQwen3.7 PlusQwen33.2/1002 independent benchmarksNo cost per solved task
Shared results
47
GLM 4.7 only
33
Qwen3.7 Plus only
28
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 4.7 55.1 from 2 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
Widest category
Math, 9.0 points apart on average over 3 shared benchmarks.
Widest single gap
Terminal-Bench Hard (Artificial Analysis run): GLM 4.7 31.8% against Qwen3.7 Plus 47.0%, 15.2 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Qwen3.7 Plus $1.28 per million output tokens, 1.7x apart.
Context window
GLM 4.7 takes 205K tokens, Qwen3.7 Plus 1M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 4.7BarsQwen3.7 PlusTies
Coding04
Intelligence02
Agentic work10
Reasoning06
Math03
Knowledge13
Web design17
General118
All shared boards443
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.355.9Qwen3.7 Plus
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
Vibe Code Bench (Vals AI)Not measured46.4%Only Qwen3.7 Plus measured
Code migration (Vals AI)Not measured12.9%Only Qwen3.7 Plus measured
Terminal-Bench 2.1 (Vals AI)Not measured52.8%Only Qwen3.7 Plus measured
SkillsBench (Vals AI)Not measured54.3%Only Qwen3.7 Plus measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured46.1%Only Qwen3.7 Plus measured
Terminal-Bench Hard (Artificial Analysis run)31.8%47.0%Qwen3.7 Plus
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
FrontierCodeNot measured10.2%Only Qwen3.7 Plus measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541473Qwen3.7 Plus
LMArena Text: software and IT14531473Qwen3.7 Plus
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML1447Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
AA Intelligence Index22.225.2Qwen3.7 Plus
Epoch Capabilities Index143.6147.0Qwen3.7 Plus
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
AA Agentic IndexNot measured17.5Only Qwen3.7 Plus measured
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1119Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
Finance Agent (Vals AI)Not measured38.2%Only Qwen3.7 Plus measured
Tax Agent Bench (Vals AI)Not measured10.5%Only Qwen3.7 Plus measured
Legal Research Bench (Vals AI)Not measured16.4%Only Qwen3.7 Plus measured
Excel Modeling Benchmark (Vals AI)Not measured49.3%Only Qwen3.7 Plus measured
τ²-Bench Telecom95.9%93.0%GLM 4.7
OSWorld 2.0Not measured2.8%Only Qwen3.7 Plus measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%90.0%Qwen3.7 Plus
Humanity's Last Exam (Artificial Analysis run)27.4%35.6%Qwen3.7 Plus
GPQA Diamond (Epoch AI run)83.3%87.9%Qwen3.7 Plus
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
Mystery game puzzlesNot measured17.0%Only Qwen3.7 Plus measured
LMArena Text: hard prompts14431460Qwen3.7 Plus
LMArena Text: expert14241466Qwen3.7 Plus
LMArena Text: hard prompts in English14521460Qwen3.7 Plus
LMArena Vision: diagramsNot measured1292Only Qwen3.7 Plus measured
LMArena Vision: homeworkNot measured1295Only Qwen3.7 Plus measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%93.3%Qwen3.7 Plus
FrontierMath, tiers 1 to 3Not measured34.4%Only Qwen3.7 Plus measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241467Qwen3.7 Plus
LMArena Text: mathematical work14111470Qwen3.7 Plus
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MortgageTax (Vals AI)Not measured66.2%Only Qwen3.7 Plus measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571464Qwen3.7 Plus
LMArena Text: medicine and healthcare14691464GLM 4.7
LMArena Text: legal and government14441451Qwen3.7 Plus
LMArena Text: business and finance14291435Qwen3.7 Plus
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index17
Web design benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
Design Arena: websites12261269Qwen3.7 Plus
Design Arena: UI components11991261Qwen3.7 Plus
Design Arena: game dev11881262Qwen3.7 Plus
Design Arena: data viz11951275Qwen3.7 Plus
Design Arena: 3D11911239Qwen3.7 Plus
Design Arena: all code categories12171269Qwen3.7 Plus
Design Arena: SVG11431208Qwen3.7 Plus
Design Arena: ASCII art11651144GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
General benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
SAGE (Vals AI)Not measured39.3%Only Qwen3.7 Plus measured
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
Vals Multimodal IndexNot measured53.9%Only Qwen3.7 Plus measured
IFBench67.9%78.0%Qwen3.7 Plus
Long context reasoning (AA-LCR)71.0%73.0%Qwen3.7 Plus
DTBenchNot measured84.0%Only Qwen3.7 Plus measured
LMCANot measured37.6%Only Qwen3.7 Plus measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351454Qwen3.7 Plus
LMArena Text: instruction following14121439Qwen3.7 Plus
LMArena Text: creative writing14011438Qwen3.7 Plus
LMArena VisionNot measured1278Only Qwen3.7 Plus measured
LMArena Text: English14531458Qwen3.7 Plus
LMArena Text: non-English14171445Qwen3.7 Plus
LMArena Text: Chinese14951509Qwen3.7 Plus
LMArena Text: German14261471Qwen3.7 Plus
LMArena Text: Japanese14391412GLM 4.7
LMArena Text: Korean13991413Qwen3.7 Plus
LMArena Text: French14321472Qwen3.7 Plus
LMArena Text: Spanish14341457Qwen3.7 Plus
LMArena Text: Russian14231457Qwen3.7 Plus
LMArena Text: Polish14351449Qwen3.7 Plus
LMArena Text: multi-turn14461459Qwen3.7 Plus
LMArena Text: longer queries14321454Qwen3.7 Plus
LMArena Text: writing and language14111437Qwen3.7 Plus
LMArena Text: entertainment and media14001422Qwen3.7 Plus
LMArena Vision: EnglishNot measured1280Only Qwen3.7 Plus measured
LMArena Vision: ChineseNot measured1332Only Qwen3.7 Plus measured
LMArena Vision: reading textNot measured1288Only Qwen3.7 Plus measured
LMArena Vision: humorNot measured1261Only Qwen3.7 Plus measured
LMArena Vision: creative writingNot measured1258Only Qwen3.7 Plus measured
LMArena DocumentNot measured1444Only Qwen3.7 Plus measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Qwen3.7 Plus
BenchmarkGLM 4.7Qwen3.7 PlusLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 4.7$0.0017Fits in one request
Qwen3.7 Plus$0.0010Fits in one request

Qwen3.7 Plus costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Qwen3.7 Plus$0.020Fits in one request

Qwen3.7 Plus costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
Qwen3.7 Plus$0.077Fits in one request

Qwen3.7 Plus costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 4.7 and Qwen3.7 Plus
SpecificationZ.ai GLM 4.7Qwen3.7 Plus
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025June 3, 2026
Context window205K1M
Input $/Mtok$0.60$0.32
Output $/Mtok$2.20$1.28
Coding index55.133.2
Independent measurements behind it22
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Qwen3.7 Plus for coding?

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus. Pick Qwen3.7 Plus when output tokens cost 1.7x less, or when the context window is larger, 1M against 205K tokens.

Which is cheaper, GLM 4.7 or Qwen3.7 Plus?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Qwen3.7 Plus?

Qwen3.7 Plus, with 1M tokens against 205K.

How many benchmarks is the GLM 4.7 vs Qwen3.7 Plus comparison based on?

47 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-7-vs-qwen3-7-plus">LLMBenchmark.ai, GLM 4.7 vs Qwen3.7 Plus</a></p>