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GLM 4.5 vs Qwen3 Coder 480B A35B

GLM 4.5 leads Qwen3 Coder 480B A35B on the Artificial Analysis Intelligence Index, 12.8 to 11.9, and Qwen3 Coder 480B A35B costs 1.5x less per output token ($1.50 vs $2.20 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace GLM 4.5 Replace Qwen3 Coder 480B A35B
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
44
Qwen3 Coder 480B A35BQwen
Coding
No data
Overall
No data
Output $/M
$1.50
4
vs boards won of 48 shared

GLM 4.5 or Qwen3 Coder 480B A35B: which to pick

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

Pick GLM 4.5 for work measured by SWE-bench Verified or WeirdML, where it leads.

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 Coder 480B A35BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
48
GLM 4.5 only
13
Qwen3 Coder 480B A35B only
5
Like-for-like categories
8 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, Qwen3 Coder 480B A35B not benchmarked.
Widest category
Math, 22.4 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: GLM 4.5 73.7 against Qwen3 Coder 480B A35B 39.3, 34.4 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen3 Coder 480B A35B $1.50 per million output tokens, 1.5x apart.
Context window
GLM 4.5 takes 131K tokens, Qwen3 Coder 480B A35B 262K.

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 4.5BarsQwen3 Coder 480B A35BTies
Coding43
Intelligence10
Agentic work01
Reasoning60
Math40
Knowledge50
Web design50
General190
All shared boards444
CodingLike-for-likeSix of these boards feed the Vibe Coding Index43
Coding benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
SWE-bench Verified64.2%69.6%Qwen3 Coder 480B A35B
LMArena WebDevNot measured1275Only Qwen3 Coder 480B A35B measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%58.5%GLM 4.5
Terminal-Bench Hard (Artificial Analysis run)22.0%18.9%GLM 4.5
Terminal-BenchNot measured27.2%Only Qwen3 Coder 480B A35B measured
GSO-BenchNot measured4.9%Only Qwen3 Coder 480B A35B measured
WeirdML40.6%41.2%Qwen3 Coder 480B A35B
ALE-Bench344.8461.4Qwen3 Coder 480B A35B
LMArena Text: coding14341412GLM 4.5
LMArena Text: software and IT14401393GLM 4.5
LMArena WebDev: ReactNot measured1257Only Qwen3 Coder 480B A35B measured
LMArena WebDev: HTMLNot measured1287Only Qwen3 Coder 480B A35B measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
AA Intelligence Index12.811.9GLM 4.5
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
τ²-Bench Telecom43.0%43.6%Qwen3 Coder 480B A35B
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%61.8%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%4.5%GLM 4.5
LMArena Text: hard prompts14291372GLM 4.5
LMArena Text: expert14331338GLM 4.5
LMArena Text: hard prompts in English14291380GLM 4.5
Kagi LLM Benchmark57.9%49.5%GLM 4.5
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
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 Index73.739.3GLM 4.5
AIME 2025 (Artificial Analysis run)73.7%39.3%GLM 4.5
LMArena Text: math14271365GLM 4.5
LMArena Text: mathematical work14301367GLM 4.5
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 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
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%78.8%GLM 4.5
LMArena Text: science14441347GLM 4.5
LMArena Text: medicine and healthcare14551347GLM 4.5
LMArena Text: legal and government14381358GLM 4.5
LMArena Text: business and finance14201343GLM 4.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
Design Arena: websites11691158GLM 4.5
Design Arena: UI components11411110GLM 4.5
Design Arena: game dev11441098GLM 4.5
Design Arena: data viz11591084GLM 4.5
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories11651142GLM 4.5
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 index190
General benchmarks for GLM 4.5 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
IFBench44.1%40.5%GLM 4.5
Long context reasoning (AA-LCR)52.7%45.7%GLM 4.5
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301357GLM 4.5
LMArena Text: instruction following14041355GLM 4.5
LMArena Text: creative writing13951333GLM 4.5
LMArena Text: English14371359GLM 4.5
LMArena Text: non-English14171346GLM 4.5
LMArena Text: Chinese14651357GLM 4.5
LMArena Text: German14071325GLM 4.5
LMArena Text: Japanese14151311GLM 4.5
LMArena Text: Korean13801305GLM 4.5
LMArena Text: French14181398GLM 4.5
LMArena Text: Spanish14541360GLM 4.5
LMArena Text: Russian14131366GLM 4.5
LMArena Text: Polish14221344GLM 4.5
LMArena Text: multi-turn14151366GLM 4.5
LMArena Text: longer queries14121378GLM 4.5
LMArena Text: writing and language14031340GLM 4.5
LMArena Text: entertainment and media14041317GLM 4.5
Neither model has a result on the other 26 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 Qwen3 Coder 480B A35B
BenchmarkGLM 4.5Qwen3 Coder 480B A35BLeader
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
Qwen3 Coder 480B A35B$0.0010Fits in one request

Qwen3 Coder 480B A35B costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen3 Coder 480B A35B$0.019Fits in one request

Qwen3 Coder 480B A35B costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Qwen3 Coder 480B A35B$0.075Fits in one request

Qwen3 Coder 480B A35B 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 and Qwen3 Coder 480B A35B
SpecificationZ.ai GLM 4.5Qwen3 Coder 480B A35B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025July 23, 2025
Context window131K262K
Input $/Mtok$0.60$0.30
Output $/Mtok$2.20$1.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen3 Coder 480B A35B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by SWE-bench Verified or WeirdML, where it leads.

Which is cheaper, GLM 4.5 or Qwen3 Coder 480B A35B?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 Coder 480B A35B costs $0.30 and $1.50. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5 or Qwen3 Coder 480B A35B?

Qwen3 Coder 480B A35B, with 262K tokens against 131K.

How many benchmarks is the GLM 4.5 vs Qwen3 Coder 480B A35B comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Qwen3 Coder 480B A35B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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