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GLM 5.2 vs Qwen3 Coder 480B A35B

GLM 5.2 leads Qwen3 Coder 480B A35B on the Artificial Analysis Intelligence Index, 33.7 to 11.9, and Qwen3 Coder 480B A35B costs 2.9x less per output token ($1.50 vs $4.40 per million). Updated October 22, 2026; 46 benchmarks have a result for both.

Replace GLM 5.2 Replace Qwen3 Coder 480B A35B
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
46
Qwen3 Coder 480B A35BQwen
Coding
No data
Overall
No data
Output $/M
$1.50
0
vs boards won of 46 shared

GLM 5.2 or Qwen3 Coder 480B A35B: which to pick

Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; Qwen3 Coder 480B A35B has none, so the two cannot be ranked on coding.

Pick Qwen3 Coder 480B A35B for output tokens 2.9x cheaper ($1.50 vs $4.40 per million).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BQwen3 Coder 480B A35BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
46
GLM 5.2 only
52
Qwen3 Coder 480B A35B only
7
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 5.2 61.1 from 4 benchmarks, Qwen3 Coder 480B A35B not benchmarked.
Widest category
Agentic work, 55.6 points apart on average over 1 shared benchmark.
Widest single gap
LMArena WebDev: React: GLM 5.2 1617 against Qwen3 Coder 480B A35B 1257, 57.1 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against Qwen3 Coder 480B A35B $1.50 per million output tokens, 2.9x apart.
Context window
GLM 5.2 takes 1.05M 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 5.2BarsQwen3 Coder 480B A35BTies
Coding80
Intelligence10
Agentic work10
Reasoning60
Math20
Knowledge40
Web design50
General190
All shared boards460
CodingLike-for-likeSix of these boards feed the Vibe Coding Index80
Coding benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
SWE-bench VerifiedNot measured69.6%Only Qwen3 Coder 480B A35B measured
LMArena WebDev16041275GLM 5.2
AA Coding Index68.8Not measuredOnly GLM 5.2 measured
LiveCodeBench (Vals AI)69.5%Not measuredOnly GLM 5.2 measured
SWE-bench Verified (Vals AI)82.8%Not measuredOnly GLM 5.2 measured
Vibe Code Bench (Vals AI)64.0%Not measuredOnly GLM 5.2 measured
Code migration (Vals AI)37.9%Not measuredOnly GLM 5.2 measured
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)67.8%Not measuredOnly GLM 5.2 measured
SkillsBench (Vals AI)45.1%Not measuredOnly GLM 5.2 measured
LiveCodeBench (Artificial Analysis run)Not measured58.5%Only Qwen3 Coder 480B A35B measured
SciCode51.2%Not measuredOnly GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)50.8%18.9%GLM 5.2
Terminal-BenchNot measured27.2%Only Qwen3 Coder 480B A35B measured
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
GSO-BenchNot measured4.9%Only Qwen3 Coder 480B A35B measured
WeirdML70.1%41.2%GLM 5.2
ALE-Bench1047.0461.4GLM 5.2
LMArena Text: coding14871412GLM 5.2
LMArena Text: software and IT14861393GLM 5.2
LMArena WebDev: React16171257GLM 5.2
LMArena WebDev: HTML15651287GLM 5.2
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
AA Intelligence Index33.711.9GLM 5.2
Epoch Capabilities Index151.9Not measuredOnly GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
AA Agentic Index38.4Not measuredOnly GLM 5.2 measured
Design Arena: agents building web apps1222Not measuredOnly GLM 5.2 measured
Design Arena: agents building full-stack apps1241Not measuredOnly GLM 5.2 measured
Design Arena: agents building mobile apps1159Not measuredOnly GLM 5.2 measured
Design Arena: agents building Android apps1178Not measuredOnly GLM 5.2 measured
Design Arena: agents building Godot games1142Not measuredOnly GLM 5.2 measured
Design Arena: agents building games1206Not measuredOnly GLM 5.2 measured
Design Arena: HTML slides1125Not measuredOnly GLM 5.2 measured
Design Arena: PowerPoint slides1170Not measuredOnly GLM 5.2 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%Not measuredOnly GLM 5.2 measured
Legal Research Bench (Vals AI)31.3%Not measuredOnly GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)61.5%Not measuredOnly GLM 5.2 measured
τ²-Bench Telecom99.1%43.6%GLM 5.2
APEX-Agents45.2%Not measuredOnly GLM 5.2 measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
GPQA Diamond85.6%Not measuredOnly GLM 5.2 measured
GPQA Diamond (Artificial Analysis run)89.5%61.8%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)41.1%4.5%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%Not measuredOnly GLM 5.2 measured
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%Not measuredOnly GLM 5.2 measured
Mystery game puzzles19.0%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811372GLM 5.2
LMArena Text: expert14871338GLM 5.2
LMArena Text: hard prompts in English14811380GLM 5.2
Kagi LLM Benchmark62.6%49.5%GLM 5.2
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
AA Math IndexNot measured39.3Only Qwen3 Coder 480B A35B measured
AIME 2025 (Artificial Analysis run)Not measured39.3%Only Qwen3 Coder 480B A35B measured
OTIS Mock AIME86.4%Not measuredOnly GLM 5.2 measured
FrontierMath, tiers 1 to 359.2%Not measuredOnly GLM 5.2 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%Not measuredOnly GLM 5.2 measured
LMArena Text: math14831365GLM 5.2
LMArena Text: mathematical work14891367GLM 5.2
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
MMLU Pro86.7%Not measuredOnly GLM 5.2 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5.2 measured
CorpFin (Vals AI)66.1%Not measuredOnly GLM 5.2 measured
TaxEval (Vals AI)73.3%Not measuredOnly GLM 5.2 measured
MedCode (Vals AI)40.8%Not measuredOnly GLM 5.2 measured
MMLU Pro (Artificial Analysis run)Not measured78.8%Only Qwen3 Coder 480B A35B measured
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891347GLM 5.2
LMArena Text: medicine and healthcare14751347GLM 5.2
LMArena Text: legal and government14861358GLM 5.2
LMArena Text: business and finance14511343GLM 5.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
Design Arena: websites12851158GLM 5.2
Design Arena: UI components12941110GLM 5.2
Design Arena: game dev12681098GLM 5.2
Design Arena: data viz12851084GLM 5.2
Design Arena: 3D1287Not measuredOnly GLM 5.2 measured
Design Arena: all code categories12871142GLM 5.2
Design Arena: SVG1213Not measuredOnly GLM 5.2 measured
Design Arena: ASCII art1191Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 Coder 480B A35BLeader
MedScribe (Vals AI)83.5%Not measuredOnly GLM 5.2 measured
IFBench73.3%40.5%GLM 5.2
Long context reasoning (AA-LCR)78.3%45.7%GLM 5.2
DTBench93.6%Not measuredOnly GLM 5.2 measured
LMCA45.8%Not measuredOnly GLM 5.2 measured
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
LMArena Text14701357GLM 5.2
LMArena Text: instruction following14661355GLM 5.2
LMArena Text: creative writing14631333GLM 5.2
LMArena Text: English14751359GLM 5.2
LMArena Text: non-English14591346GLM 5.2
LMArena Text: Chinese15221357GLM 5.2
LMArena Text: German14701325GLM 5.2
LMArena Text: Japanese14501311GLM 5.2
LMArena Text: Korean14401305GLM 5.2
LMArena Text: French14791398GLM 5.2
LMArena Text: Spanish14781360GLM 5.2
LMArena Text: Russian14681366GLM 5.2
LMArena Text: Polish14561344GLM 5.2
LMArena Text: multi-turn14691366GLM 5.2
LMArena Text: longer queries14801378GLM 5.2
LMArena Text: writing and language14631340GLM 5.2
LMArena Text: entertainment and media14501317GLM 5.2
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.2 and Qwen3 Coder 480B A35B
BenchmarkGLM 5.2Qwen3 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 5.2$0.0036Fits 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 5.2$0.083Fits 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 5.2$0.324Fits in one request
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 5.2 and Qwen3 Coder 480B A35B
SpecificationZ.ai GLM 5.2Qwen3 Coder 480B A35B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026July 23, 2025
Context window1.05M262K
Input $/Mtok$1.40$0.30
Output $/Mtok$4.40$1.50
Coding index61.1Not benchmarked
Independent measurements behind it41
Cost per solved task$0.86Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than Qwen3 Coder 480B A35B for coding?

Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; Qwen3 Coder 480B A35B has none, so the two cannot be ranked on coding. Pick Qwen3 Coder 480B A35B for output tokens 2.9x cheaper ($1.50 vs $4.40 per million).

Which is cheaper, GLM 5.2 or Qwen3 Coder 480B A35B?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3 Coder 480B A35B costs $0.30 and $1.50. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, GLM 5.2 or Qwen3 Coder 480B A35B?

GLM 5.2, with 1.05M tokens against 262K.

How many benchmarks is the GLM 5.2 vs Qwen3 Coder 480B A35B comparison based on?

46 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 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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