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GLM 4.6 vs Qwen3 32B

GLM 4.6 leads Qwen3 32B on the Artificial Analysis Intelligence Index, 18.5 to 7.3, and Qwen3 32B costs 5.5x less per output token ($0.40 vs $2.20 per million). Updated October 21, 2026; 37 benchmarks have a result for both.

Replace GLM 4.6 Replace Qwen3 32B
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
36
Qwen3 32BQwen
Coding
No data
Overall
40.8
Output $/M
$0.40
1
vs boards won of 37 shared

GLM 4.6 or Qwen3 32B: which to pick

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding.

Pick Qwen3 32B for output tokens 5.5x cheaper ($0.40 vs $2.20 per million) and work measured by Kagi LLM Benchmark, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BQwen3 32BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
37
GLM 4.6 only
32
Qwen3 32B only
8
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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 4.6 8.8 from 2 benchmarks, Qwen3 32B not benchmarked.
Widest category
Agentic work, 36.1 points apart on average over 3 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.6 54.0% against Qwen3 32B 0.0%, 54.0 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Qwen3 32B $0.40 per million output tokens, 5.5x apart.
Context window
GLM 4.6 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 4.6BarsQwen3 32BTies
Coding40
Intelligence10
Agentic work30
Reasoning51
Math40
Knowledge50
General140
All shared boards361
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
Aider polyglotNot measured40.0%Only Qwen3 32B measured
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev1341Not measuredOnly GLM 4.6 measured
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%54.6%GLM 4.6
SciCodeNot measured36.0%Only Qwen3 32B measured
Terminal-Bench Hard (Artificial Analysis run)28.8%3.0%GLM 4.6
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8Not measuredOnly GLM 4.6 measured
LMArena Text: coding14491357GLM 4.6
LMArena Text: software and IT14551366GLM 4.6
LMArena WebDev: HTML1350Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
AA Intelligence Index18.57.3GLM 4.6
Epoch Capabilities IndexNot measured138.4Only Qwen3 32B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
Agentic work benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1084Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%29.8%GLM 4.6
Berkeley Function Calling Leaderboard (BFCL)72.4%48.7%GLM 4.6
BrowseComp-Plus48.2%10.7%GLM 4.6
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%66.8%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)14.5%7.4%GLM 4.6
GPQA Diamond (Epoch AI run)Not measured65.7%Only Qwen3 32B measured
LMArena Text: hard prompts14401334GLM 4.6
LMArena Text: expert14321362GLM 4.6
LMArena Text: hard prompts in English14451350GLM 4.6
Kagi LLM Benchmark45.7%48.7%Qwen3 32B
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.073.0GLM 4.6
AIME 2025 (Artificial Analysis run)86.0%73.0%GLM 4.6
OTIS Mock AIMENot measured66.9%Only Qwen3 32B measured
LMArena Text: math14321399GLM 4.6
LMArena Text: mathematical work14471420GLM 4.6
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%79.8%GLM 4.6
LMArena Text: science14561372GLM 4.6
LMArena Text: medicine and healthcare14551368GLM 4.6
LMArena Text: legal and government14481324GLM 4.6
LMArena Text: business and finance14161318GLM 4.6
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1148Not measuredOnly GLM 4.6 measured
Design Arena: game dev1151Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1129Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1166Not measuredOnly GLM 4.6 measured
Design Arena: SVG1109Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 32BLeader
IFBench43.4%36.3%GLM 4.6
Long context reasoning (AA-LCR)54.0%0.0%GLM 4.6
DTBenchNot measured67.5%Only Qwen3 32B measured
LMCANot measured17.3%Only Qwen3 32B measured
Fiction.LiveBenchNot measured74.2%Only Qwen3 32B measured
LMArena Text14401340GLM 4.6
LMArena Text: instruction following14101305GLM 4.6
LMArena Text: creative writing14121297GLM 4.6
LMArena Text: English14491359GLM 4.6
LMArena Text: non-English14261317GLM 4.6
LMArena Text: Chinese14991357GLM 4.6
LMArena Text: German14471341GLM 4.6
LMArena Text: Japanese1393Not measuredOnly GLM 4.6 measured
LMArena Text: Korean1400Not measuredOnly GLM 4.6 measured
LMArena Text: French1459Not measuredOnly GLM 4.6 measured
LMArena Text: Spanish1436Not measuredOnly GLM 4.6 measured
LMArena Text: Russian14181311GLM 4.6
LMArena Text: Polish1447Not measuredOnly GLM 4.6 measured
LMArena Text: multi-turn14271331GLM 4.6
LMArena Text: longer queries14221327GLM 4.6
LMArena Text: writing and language14151298GLM 4.6
LMArena Text: entertainment and media14091279GLM 4.6
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Qwen3 32B
BenchmarkGLM 4.6Qwen3 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 4.6$0.0017Fits in one request
Qwen3 32B$0.0003Fits in one request

Qwen3 32B costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
Qwen3 32B$0.0082Fits in one request

Qwen3 32B costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
Qwen3 32B$0.032Exceeds 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 4.6 and Qwen3 32B
SpecificationZ.ai GLM 4.6Qwen3 32B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025April 28, 2025
Context window205K131K
Input $/Mtok$0.60$0.14
Output $/Mtok$2.20$0.40
Coding index8.8Not benchmarked
Independent measurements behind it20
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Qwen3 32B for coding?

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding. Pick Qwen3 32B for output tokens 5.5x cheaper ($0.40 vs $2.20 per million) and work measured by Kagi LLM Benchmark, where it leads.

Which is cheaper, GLM 4.6 or Qwen3 32B?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6 or Qwen3 32B?

GLM 4.6, with 205K tokens against 131K.

How many benchmarks is the GLM 4.6 vs Qwen3 32B comparison based on?

37 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen3 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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