GLM 5.3 vs Qwen3 Coder 480B A35B
Updated October 21, 2026. 42 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 50.0 for Qwen3 Coder 480B A35B.
Pick Qwen3 Coder 480B A35B when output tokens cost 1.4x less.
- Shared results
- 42
- GLM 5.3 only
- 58
- Qwen3 Coder 480B A35B only
- 10
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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.
Coding
Vibe Coding Index, four public coding benchmarks
GLM 5.3
GLM 5.3 scores 73.1 against 50.0.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 11.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1159.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0008, Qwen3 Coder 480B A35B $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 73.1 from 2 benchmarks, Qwen3 Coder 480B A35B 50.0 from 2 benchmarks.
- Widest category
- Coding, 35.9 points apart on average over 7 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GLM 5.3 1641 against Qwen3 Coder 480B A35B 1257, 57.1 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $1.40 against Qwen3 Coder 480B A35B $1.00 per million output tokens, 1.4x apart.
- Context window
- GLM 5.3 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.
| Category | GLM 5.3 | Bars | Qwen3 Coder 480B A35B | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 5 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 42 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 69.6% | Only Qwen3 Coder 480B A35B measured |
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | 1275 | GLM 5.3 |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | Not measured | Only GLM 5.3 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 58.5% | Only Qwen3 Coder 480B A35B measured |
| SciCode | 59.0% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 18.9% | Only Qwen3 Coder 480B A35B measured |
| Terminal-Bench | Not measured | 27.2% | Only Qwen3 Coder 480B A35B measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| GSO-Bench | Not measured | 4.9% | Only Qwen3 Coder 480B A35B measured |
| WeirdML | 75.4% | 41.2% | GLM 5.3 |
| ALE-Bench | 1317.4 | 461.4 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1412 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1393 | GLM 5.3 |
| LMArena WebDev: React | 1641 | 1257 | GLM 5.3 |
| LMArena WebDev: HTML | 1595 | 1287 | GLM 5.3 |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 11.9 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1255 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | Not measured | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 43.6% | Only Qwen3 Coder 480B A35B measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 61.8% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 4.5% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | 1372 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1338 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1380 | GLM 5.3 |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 39.3 | Only Qwen3 Coder 480B A35B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 39.3% | Only Qwen3 Coder 480B A35B measured |
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1495 | 1365 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1367 | GLM 5.3 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.8% | Only Qwen3 Coder 480B A35B measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1347 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1347 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1358 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1343 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1159 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1112 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1099 | GLM 5.3 |
| Design Arena: data viz | 1260 | 1084 | GLM 5.3 |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | 1143 | GLM 5.3 |
| Design Arena: SVG | 1292 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| IFBench | Not measured | 40.5% | Only Qwen3 Coder 480B A35B measured |
| Long context reasoning (AA-LCR) | 79.7% | 45.7% | GLM 5.3 |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| LMArena Text | 1474 | 1357 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1355 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1333 | GLM 5.3 |
| LMArena Text: English | 1482 | 1359 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1346 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1357 | GLM 5.3 |
| LMArena Text: German | 1488 | 1325 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1311 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1305 | GLM 5.3 |
| LMArena Text: French | 1503 | 1398 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1360 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1366 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1344 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1366 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1378 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1340 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1317 | GLM 5.3 |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| 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 5.3 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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.
| Specification | Z.ai GLM 5.3 | Qwen3 Coder 480B A35B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | July 23, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.079 | $0.30 |
| Output $/Mtok | $1.40 | $1.00 |
| Coding index | 73.1 | 50.0 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Qwen3 Coder 480B A35B for coding?
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 50.0 for Qwen3 Coder 480B A35B. Pick Qwen3 Coder 480B A35B when output tokens cost 1.4x less.
Which is cheaper, GLM 5.3 or Qwen3 Coder 480B A35B?
GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Qwen3 Coder 480B A35B costs $0.30 and $1.00. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or Qwen3 Coder 480B A35B?
GLM 5.3, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.3 vs Qwen3 Coder 480B A35B comparison based on?
42 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Qwen3 Coder 480B A35B's on 2. Where only one model has a result, the page says so instead of naming a winner.