Enter opens the highlighted result. Esc closes.

GLM 4.6 vs Qwen3 Coder 480B A35B

GLM 4.6 leads Qwen3 Coder 480B A35B on the Artificial Analysis Intelligence Index, 18.5 to 11.9, and Qwen3 Coder 480B A35B costs 1.5x less per output token ($1.50 vs $2.20 per million). Updated October 21, 2026; 50 benchmarks have a result for both.

Replace GLM 4.6 Replace Qwen3 Coder 480B A35B
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
46
Qwen3 Coder 480B A35BQwen
Coding
No data
Overall
No data
Output $/M
$1.50
4
vs boards won of 50 shared

GLM 4.6 or Qwen3 Coder 480B A35B: which to pick

Only GLM 4.6 has a coding score, 8.8 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 1.5x cheaper ($1.50 vs $2.20 per million) and a larger context window (262K against 205K tokens).

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BQwen3 Coder 480B A35BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
50
GLM 4.6 only
19
Qwen3 Coder 480B A35B only
3
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 4.6 8.8 from 2 benchmarks, Qwen3 Coder 480B A35B not benchmarked.
Widest category
Agentic work, 33.3 points apart on average over 1 shared benchmark.
Widest single gap
AA Math Index: GLM 4.6 86.0 against Qwen3 Coder 480B A35B 39.3, 46.7 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Qwen3 Coder 480B A35B $1.50 per million output tokens, 1.5x apart.
Context window
GLM 4.6 takes 205K 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 4.6BarsQwen3 Coder 480B A35BTies
Coding63
Intelligence10
Agentic work10
Reasoning51
Math40
Knowledge50
Web design50
General190
All shared boards464
CodingLike-for-likeSix of these boards feed the Vibe Coding Index63
Coding benchmarks for GLM 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
SWE-bench Verified68.2%69.6%Qwen3 Coder 480B A35B
LMArena WebDev13411275GLM 4.6
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%58.5%GLM 4.6
Terminal-Bench Hard (Artificial Analysis run)28.8%18.9%GLM 4.6
Terminal-Bench24.5%27.2%Qwen3 Coder 480B A35B
GSO-BenchNot measured4.9%Only Qwen3 Coder 480B A35B measured
WeirdMLNot measured41.2%Only Qwen3 Coder 480B A35B measured
ALE-Bench340.8461.4Qwen3 Coder 480B A35B
LMArena Text: coding14491412GLM 4.6
LMArena Text: software and IT14551393GLM 4.6
LMArena WebDev: ReactNot measured1257Only Qwen3 Coder 480B A35B measured
LMArena WebDev: HTML13501287GLM 4.6
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
AA Intelligence Index18.511.9GLM 4.6
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
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%43.6%GLM 4.6
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
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 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%61.8%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)14.5%4.5%GLM 4.6
LMArena Text: hard prompts14401372GLM 4.6
LMArena Text: expert14321338GLM 4.6
LMArena Text: hard prompts in English14451380GLM 4.6
Kagi LLM Benchmark45.7%49.5%Qwen3 Coder 480B A35B
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 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.039.3GLM 4.6
AIME 2025 (Artificial Analysis run)86.0%39.3%GLM 4.6
LMArena Text: math14321365GLM 4.6
LMArena Text: mathematical work14471367GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
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%78.8%GLM 4.6
LMArena Text: science14561347GLM 4.6
LMArena Text: medicine and healthcare14551347GLM 4.6
LMArena Text: legal and government14481358GLM 4.6
LMArena Text: business and finance14161343GLM 4.6
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 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
Design Arena: websites11741159GLM 4.6
Design Arena: UI components11481113GLM 4.6
Design Arena: game dev11511099GLM 4.6
Design Arena: data viz11611084GLM 4.6
Design Arena: 3D1129Not measuredOnly GLM 4.6 measured
Design Arena: all code categories11661143GLM 4.6
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 index190
General benchmarks for GLM 4.6 and Qwen3 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 Coder 480B A35BLeader
IFBench43.4%40.5%GLM 4.6
Long context reasoning (AA-LCR)54.0%45.7%GLM 4.6
LMArena Text14401357GLM 4.6
LMArena Text: instruction following14101355GLM 4.6
LMArena Text: creative writing14121333GLM 4.6
LMArena Text: English14491359GLM 4.6
LMArena Text: non-English14261346GLM 4.6
LMArena Text: Chinese14991357GLM 4.6
LMArena Text: German14471325GLM 4.6
LMArena Text: Japanese13931311GLM 4.6
LMArena Text: Korean14001305GLM 4.6
LMArena Text: French14591398GLM 4.6
LMArena Text: Spanish14361360GLM 4.6
LMArena Text: Russian14181366GLM 4.6
LMArena Text: Polish14471344GLM 4.6
LMArena Text: multi-turn14271366GLM 4.6
LMArena Text: longer queries14221378GLM 4.6
LMArena Text: writing and language14151340GLM 4.6
LMArena Text: entertainment and media14091317GLM 4.6
Neither model has a result on the other 27 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 Coder 480B A35B
BenchmarkGLM 4.6Qwen3 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 4.6$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 4.6$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 4.6$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 4.6 and Qwen3 Coder 480B A35B
SpecificationZ.ai GLM 4.6Qwen3 Coder 480B A35B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025July 23, 2025
Context window205K262K
Input $/Mtok$0.60$0.30
Output $/Mtok$2.20$1.50
Coding index8.8Not benchmarked
Independent measurements behind it21
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Qwen3 Coder 480B A35B for coding?

Only GLM 4.6 has a coding score, 8.8 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 1.5x cheaper ($1.50 vs $2.20 per million) and a larger context window (262K against 205K tokens).

Which is cheaper, GLM 4.6 or Qwen3 Coder 480B A35B?

GLM 4.6 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 4.6 or Qwen3 Coder 480B A35B?

Qwen3 Coder 480B A35B, with 262K tokens against 205K.

How many benchmarks is the GLM 4.6 vs Qwen3 Coder 480B A35B comparison based on?

50 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 Coder 480B A35B's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next