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GLM 4.5 Air vs Qwen2.5 Coder 32B Instruct

GLM 4.5 Air leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 11.1 to 6.7, at about the same output price ($1.10 vs $1.00 per million). Updated October 22, 2026; 27 benchmarks have a result for both.

Replace GLM 4.5 Air Replace Qwen2.5 Coder 32B Instruct
GLM 4.5 AirZ.ai
Coding
No data
Overall
No data
Output $/M
$1.10
27
Qwen2.5 Coder 32B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
0
vs boards won of 27 shared

GLM 4.5 Air or Qwen2.5 Coder 32B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GLM 4.5 Air for a larger context window (131K against 33K tokens).

Model AGLM 4.5 AirZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 Coder 32B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
27
GLM 4.5 Air only
21
Qwen2.5 Coder 32B Instruct only
8
Like-for-like categories
6 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 4.5 Air not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
Widest category
Coding, 27.5 points apart on average over 3 shared benchmarks.
Widest single gap
LiveCodeBench (Artificial Analysis run): GLM 4.5 Air 68.4% against Qwen2.5 Coder 32B Instruct 29.5%, 38.9 points apart on a 0 to 100 scale.
Output price
GLM 4.5 Air $1.10 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 1.1x apart.
Context window
GLM 4.5 Air takes 131K tokens, Qwen2.5 Coder 32B Instruct 33K.

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 4.5 AirBarsQwen2.5 Coder 32B InstructTies
Coding30
Intelligence10
Reasoning50
Math20
Knowledge50
General110
All shared boards270
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
Aider polyglotNot measured16.4%Only Qwen2.5 Coder 32B Instruct measured
SWE-bench VerifiedNot measured47.0%Only Qwen2.5 Coder 32B Instruct measured
LiveCodeBench (Artificial Analysis run)68.4%29.5%GLM 4.5 Air
Terminal-Bench Hard (Artificial Analysis run)20.5%Not measuredOnly GLM 4.5 Air measured
GSO-Bench2.9%Not measuredOnly GLM 4.5 Air measured
LMArena Text: coding13971276GLM 4.5 Air
LMArena Text: software and IT14011261GLM 4.5 Air
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
AA Intelligence Index11.16.7GLM 4.5 Air
Epoch Capabilities IndexNot measured119.5Only Qwen2.5 Coder 32B Instruct measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
τ²-Bench Telecom46.5%Not measuredOnly GLM 4.5 Air measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
GPQA Diamond (Artificial Analysis run)73.3%41.7%GLM 4.5 Air
Humanity's Last Exam (Artificial Analysis run)7.0%3.5%GLM 4.5 Air
LMArena Text: hard prompts13791251GLM 4.5 Air
LMArena Text: expert13701221GLM 4.5 Air
LMArena Text: hard prompts in English13881269GLM 4.5 Air
Kagi LLM Benchmark43.0%Not measuredOnly GLM 4.5 Air measured
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
AA Math Index80.7Not measuredOnly GLM 4.5 Air measured
AIME 2025 (Artificial Analysis run)80.7%Not measuredOnly GLM 4.5 Air measured
GSM8KNot measured93.0%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: math13961251GLM 4.5 Air
LMArena Text: mathematical work13961243GLM 4.5 Air
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
MMLU Pro (Artificial Analysis run)81.5%63.5%GLM 4.5 Air
MMLUNot measured79.1%Only Qwen2.5 Coder 32B Instruct measured
AI2 Reasoning ChallengeNot measured70.5%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: science14071221GLM 4.5 Air
LMArena Text: medicine and healthcare14021187GLM 4.5 Air
LMArena Text: legal and government13821257GLM 4.5 Air
LMArena Text: business and finance13711224GLM 4.5 Air
Neither model has a result on the other 11 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
Design Arena: websites1146Not measuredOnly GLM 4.5 Air measured
Design Arena: UI components1120Not measuredOnly GLM 4.5 Air measured
Design Arena: game dev1094Not measuredOnly GLM 4.5 Air measured
Design Arena: data viz1186Not measuredOnly GLM 4.5 Air measured
Design Arena: 3D1122Not measuredOnly GLM 4.5 Air measured
Design Arena: all code categories1137Not measuredOnly GLM 4.5 Air measured
Design Arena: SVG1071Not measuredOnly GLM 4.5 Air measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
General benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
IFBench37.6%Not measuredOnly GLM 4.5 Air measured
Long context reasoning (AA-LCR)46.7%Not measuredOnly GLM 4.5 Air measured
HellaSwagNot measured83.0%Only Qwen2.5 Coder 32B Instruct measured
WinoGrandeNot measured80.8%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text13841230GLM 4.5 Air
LMArena Text: instruction following13541223GLM 4.5 Air
LMArena Text: creative writing13431174GLM 4.5 Air
LMArena Text: English13971248GLM 4.5 Air
LMArena Text: non-English13661205GLM 4.5 Air
LMArena Text: Chinese14261222GLM 4.5 Air
LMArena Text: German1377Not measuredOnly GLM 4.5 Air measured
LMArena Text: Japanese1348Not measuredOnly GLM 4.5 Air measured
LMArena Text: Korean1308Not measuredOnly GLM 4.5 Air measured
LMArena Text: French1400Not measuredOnly GLM 4.5 Air measured
LMArena Text: Spanish1385Not measuredOnly GLM 4.5 Air measured
LMArena Text: Russian13731228GLM 4.5 Air
LMArena Text: Polish1368Not measuredOnly GLM 4.5 Air measured
LMArena Text: multi-turn13711222GLM 4.5 Air
LMArena Text: longer queries13661251GLM 4.5 Air
LMArena Text: writing and language13541191GLM 4.5 Air
LMArena Text: entertainment and media13391150GLM 4.5 Air
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5 Air and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5 AirQwen2.5 Coder 32B InstructLeader
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 4.5 Air$0.0008Fits in one request
Qwen2.5 Coder 32B Instruct$0.0012Fits in one request

GLM 4.5 Air costs less

Repository review

50K input + 3K output tokens

GLM 4.5 Air$0.013Fits in one request
Qwen2.5 Coder 32B Instruct$0.036Exceeds the context window

GLM 4.5 Air costs less

Agent loop

200K input + 10K output tokens

GLM 4.5 Air$0.051Exceeds the context window
Qwen2.5 Coder 32B Instruct$0.142Exceeds the context window

GLM 4.5 Air 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 4.5 Air and Qwen2.5 Coder 32B Instruct
SpecificationZ.ai GLM 4.5 AirQwen2.5 Coder 32B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025November 11, 2024
Context window131K33K
Input $/Mtok$0.20$0.66
Output $/Mtok$1.10$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 Air better than Qwen2.5 Coder 32B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 Air for a larger context window (131K against 33K tokens).

Which is cheaper, GLM 4.5 Air or Qwen2.5 Coder 32B Instruct?

GLM 4.5 Air costs $0.20 per million input tokens and $1.10 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5 Air or Qwen2.5 Coder 32B Instruct?

GLM 4.5 Air, with 131K tokens against 33K.

How many benchmarks is the GLM 4.5 Air vs Qwen2.5 Coder 32B Instruct comparison based on?

27 benchmarks have a result for both models as of October 22, 2026. GLM 4.5 Air's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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