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GLM 4.5 vs Qwen2.5 Coder 32B Instruct

GLM 4.5 leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 12.8 to 6.7, and Qwen2.5 Coder 32B Instruct costs 2.2x less per output token ($1.00 vs $2.20 per million). Updated October 22, 2026; 28 benchmarks have a result for both.

Replace GLM 4.5 Replace Qwen2.5 Coder 32B Instruct
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
28
Qwen2.5 Coder 32B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
0
vs boards won of 28 shared

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

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 Coder 32B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
28
GLM 4.5 only
33
Qwen2.5 Coder 32B Instruct only
7
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 not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
Widest category
General, 34.1 points apart on average over 11 shared benchmarks.
Widest single gap
LMArena Text: medicine and healthcare: GLM 4.5 1455 against Qwen2.5 Coder 32B Instruct 1187, 44.7 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 2.2x apart.
Context window
GLM 4.5 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 4.5BarsQwen2.5 Coder 32B InstructTies
Coding40
Intelligence10
Reasoning50
Math20
Knowledge50
General110
All shared boards280
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.5 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
Aider polyglotNot measured16.4%Only Qwen2.5 Coder 32B Instruct measured
SWE-bench Verified64.2%47.0%GLM 4.5
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%29.5%GLM 4.5
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.5 measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341276GLM 4.5
LMArena Text: software and IT14401261GLM 4.5
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
AA Intelligence Index12.86.7GLM 4.5
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 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
τ²-Bench Telecom43.0%Not measuredOnly GLM 4.5 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 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%41.7%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%3.5%GLM 4.5
LMArena Text: hard prompts14291251GLM 4.5
LMArena Text: expert14331221GLM 4.5
LMArena Text: hard prompts in English14291269GLM 4.5
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.5 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.7Not measuredOnly GLM 4.5 measured
AIME 2025 (Artificial Analysis run)73.7%Not measuredOnly GLM 4.5 measured
GSM8KNot measured93.0%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: math14271251GLM 4.5
LMArena Text: mathematical work14301243GLM 4.5
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 4.5 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%63.5%GLM 4.5
MMLUNot measured79.1%Only Qwen2.5 Coder 32B Instruct measured
AI2 Reasoning ChallengeNot measured70.5%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: science14441221GLM 4.5
LMArena Text: medicine and healthcare14551187GLM 4.5
LMArena Text: legal and government14381257GLM 4.5
LMArena Text: business and finance14201224GLM 4.5
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.5 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
Design Arena: websites1169Not measuredOnly GLM 4.5 measured
Design Arena: UI components1141Not measuredOnly GLM 4.5 measured
Design Arena: game dev1144Not measuredOnly GLM 4.5 measured
Design Arena: data viz1159Not measuredOnly GLM 4.5 measured
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.5 measured
Design Arena: SVG1096Not measuredOnly GLM 4.5 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 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 Coder 32B InstructLeader
IFBench44.1%Not measuredOnly GLM 4.5 measured
Long context reasoning (AA-LCR)52.7%Not measuredOnly GLM 4.5 measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
HellaSwagNot measured83.0%Only Qwen2.5 Coder 32B Instruct measured
WinoGrandeNot measured80.8%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text14301230GLM 4.5
LMArena Text: instruction following14041223GLM 4.5
LMArena Text: creative writing13951174GLM 4.5
LMArena Text: English14371248GLM 4.5
LMArena Text: non-English14171205GLM 4.5
LMArena Text: Chinese14651222GLM 4.5
LMArena Text: German1407Not measuredOnly GLM 4.5 measured
LMArena Text: Japanese1415Not measuredOnly GLM 4.5 measured
LMArena Text: Korean1380Not measuredOnly GLM 4.5 measured
LMArena Text: French1418Not measuredOnly GLM 4.5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 4.5 measured
LMArena Text: Russian14131228GLM 4.5
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn14151222GLM 4.5
LMArena Text: longer queries14121251GLM 4.5
LMArena Text: writing and language14031191GLM 4.5
LMArena Text: entertainment and media14041150GLM 4.5
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 4.5 and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.5Qwen2.5 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$0.0017Fits in one request
Qwen2.5 Coder 32B Instruct$0.0012Fits in one request

Qwen2.5 Coder 32B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen2.5 Coder 32B Instruct$0.036Exceeds the context window

Qwen2.5 Coder 32B Instruct costs less

Agent loop

200K input + 10K output tokens

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

Same cost

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 and Qwen2.5 Coder 32B Instruct
SpecificationZ.ai GLM 4.5Qwen2.5 Coder 32B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025November 11, 2024
Context window131K33K
Input $/Mtok$0.60$0.66
Output $/Mtok$2.20$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

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

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

GLM 4.5 costs $0.60 per million input tokens and $2.20 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 or Qwen2.5 Coder 32B Instruct?

GLM 4.5, with 131K tokens against 33K.

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

28 benchmarks have a result for both models as of October 22, 2026. GLM 4.5'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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