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GLM 4.7 vs Qwen3 32B

Updated October 21, 2026. 37 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 32B
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
36
Qwen3 32BQwen
Coding
40.0
Overall
7.3
Output $/M
$0.28
1
vs boards won of 37 shared

Decision reading

Pick Qwen3 32B for coding: it scores 40.0 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when the context window is larger, 205K against 131K tokens, or when what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved taskModel BQwen3 32BQwen40.0/1001 independent benchmarkNo cost per solved task
Shared results
37
GLM 4.7 only
43
Qwen3 32B only
5
Like-for-like categories
7 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 33.6 from 1 benchmark, Qwen3 32B 40.0 from 1 benchmark.
Widest category
Agentic work, 66.1 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 71.0% against Qwen3 32B 0.0%, 71.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against Qwen3 32B $0.28 per million output tokens, 7.1x apart.
Context window
GLM 4.7 takes 205K tokens, Qwen3 32B 131K.

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 32BTies
Coding40
Intelligence20
Agentic work10
Reasoning60
Math41
Knowledge50
General140
All shared boards361
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
Aider polyglotNot measured40.0%Only Qwen3 32B measured
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%54.6%GLM 4.7
SciCodeNot measured36.0%Only Qwen3 32B measured
Terminal-Bench Hard (Artificial Analysis run)31.8%3.0%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541357GLM 4.7
LMArena Text: software and IT14531366GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML1447Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
AA Intelligence Index22.27.3GLM 4.7
Epoch Capabilities Index143.6138.4GLM 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
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 apps1118Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%29.8%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%66.8%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%7.4%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%65.7%GLM 4.7
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431334GLM 4.7
LMArena Text: expert14241362GLM 4.7
LMArena Text: hard prompts in English14521350GLM 4.7
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index41
Math benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.073.0GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%73.0%GLM 4.7
OTIS Mock AIME83.3%66.9%GLM 4.7
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241399GLM 4.7
LMArena Text: mathematical work14111420Qwen3 32B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
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
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%79.8%GLM 4.7
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571372GLM 4.7
LMArena Text: medicine and healthcare14691368GLM 4.7
LMArena Text: legal and government14441324GLM 4.7
LMArena Text: business and finance14291318GLM 4.7
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
Design Arena: websites1226Not measuredOnly GLM 4.7 measured
Design Arena: UI components1199Not measuredOnly GLM 4.7 measured
Design Arena: game dev1189Not measuredOnly GLM 4.7 measured
Design Arena: data viz1195Not measuredOnly GLM 4.7 measured
Design Arena: 3D1191Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1217Not measuredOnly GLM 4.7 measured
Design Arena: SVG1142Not measuredOnly GLM 4.7 measured
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for GLM 4.7 and Qwen3 32B
BenchmarkGLM 4.7Qwen3 32BLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%36.3%GLM 4.7
Long context reasoning (AA-LCR)71.0%0.0%GLM 4.7
DTBenchNot measured67.5%Only Qwen3 32B measured
LMCANot measured17.3%Only Qwen3 32B measured
Fiction.LiveBenchNot measured74.2%Only Qwen3 32B measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351340GLM 4.7
LMArena Text: instruction following14121305GLM 4.7
LMArena Text: creative writing14011297GLM 4.7
LMArena Text: English14531359GLM 4.7
LMArena Text: non-English14171317GLM 4.7
LMArena Text: Chinese14951357GLM 4.7
LMArena Text: German14261341GLM 4.7
LMArena Text: Japanese1439Not measuredOnly GLM 4.7 measured
LMArena Text: Korean1399Not measuredOnly GLM 4.7 measured
LMArena Text: French1432Not measuredOnly GLM 4.7 measured
LMArena Text: Spanish1434Not measuredOnly GLM 4.7 measured
LMArena Text: Russian14231311GLM 4.7
LMArena Text: Polish1435Not measuredOnly GLM 4.7 measured
LMArena Text: multi-turn14461331GLM 4.7
LMArena Text: longer queries14321327GLM 4.7
LMArena Text: writing and language14111298GLM 4.7
LMArena Text: entertainment and media14001279GLM 4.7
Neither model has a result on the other 21 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 32B
BenchmarkGLM 4.7Qwen3 32BLeader
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$0.0015Fits in one request
Qwen3 32B$0.0002Fits in one request

Qwen3 32B costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
Qwen3 32B$0.0048Fits in one request

Qwen3 32B costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds the context window
Qwen3 32B$0.019Exceeds the context window

Qwen3 32B 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 32B
SpecificationZ.ai GLM 4.7Qwen3 32B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025April 28, 2025
Context window205K131K
Input $/Mtok$0.54$0.08
Output $/Mtok$1.98$0.28
Coding index33.640.0
Independent measurements behind it11
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Qwen3 32B for coding?

Pick Qwen3 32B for coding: it scores 40.0 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when the context window is larger, 205K against 131K tokens, or when what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.7 or Qwen3 32B?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Qwen3 32B costs $0.08 and $0.28. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Qwen3 32B?

GLM 4.7, with 205K tokens against 131K.

How many benchmarks is the GLM 4.7 vs Qwen3 32B comparison based on?

37 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and Qwen3 32B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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