GLM 5 vs Qwen3.5 397B A17B
Updated October 21, 2026. 49 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 for coding: it scores 59.7 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B.
Pick Qwen3.5 397B A17B when output tokens cost 1.4x less, or when the context window is larger, 262K against 205K tokens.
- Shared results
- 49
- GLM 5 only
- 26
- Qwen3.5 397B A17B only
- 16
- Like-for-like categories
- 8 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
GLM 5 scores 59.7 against 28.6.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5
GLM 5 leads, 27.9 against 21.4.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 5 has a GPQA Diamond result, 83.3%.
Directional only
Math
AIME
No call
Only GLM 5 has a AIME result, 91.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5 has a MMLU Pro result, 86.0%.
Directional only
Web design
Design Arena: websites
GLM 5
GLM 5 leads, 1247 against 1191.
Same board, both measured
Long documents
Listed context window
Qwen3.5 397B A17B
Qwen3.5 397B A17B takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5 397B A17B
GLM 5 $0.0026, Qwen3.5 397B A17B $0.0016 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5 has the number, $0.63.
Directional only
At a glance
- Coding index
- GLM 5 59.7 from 2 benchmarks, Qwen3.5 397B A17B 28.6 from 1 benchmark.
- Widest category
- Web design, 7.7 points apart on average over 7 shared benchmarks.
- Widest single gap
- Design Arena: game dev: GLM 5 1234 against Qwen3.5 397B A17B 1141, 11.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.4x apart.
- Context window
- GLM 5 takes 205K tokens, Qwen3.5 397B A17B 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 | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 5 | 2 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 2 | ||
| Math | 0 | 3 | ||
| Knowledge | 3 | 1 | ||
| Web design | 7 | 0 | ||
| General | 14 | 5 | ||
| All shared boards | 35 | 14 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index52
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GLM 5 measured |
| SWE-bench Multilingual | 69.7% | Not measured | Only GLM 5 measured |
| LMArena WebDev | 1435 | 1400 | GLM 5 |
| LiveCodeBench (Vals AI) | 81.9% | Not measured | Only GLM 5 measured |
| SWE-bench Verified (Vals AI) | 71.4% | Not measured | Only GLM 5 measured |
| Vibe Code Bench (Vals AI) | 23.4% | Not measured | Only GLM 5 measured |
| SciCode | Not measured | 44.8% | Only Qwen3.5 397B A17B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 40.9% | GLM 5 |
| Terminal-Bench | 52.4% | Not measured | Only GLM 5 measured |
| WeirdML | 48.2% | Not measured | Only GLM 5 measured |
| ALE-Bench | 765.6 | 621.9 | GLM 5 |
| LMArena Text: coding | 1461 | 1464 | Qwen3.5 397B A17B |
| LMArena Text: software and IT | 1460 | 1463 | Qwen3.5 397B A17B |
| LMArena WebDev: React | 1424 | 1388 | GLM 5 |
| LMArena WebDev: HTML | 1450 | 1410 | GLM 5 |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 21.4 | GLM 5 |
| Epoch Capabilities Index | 145.8 | 146.8 | Qwen3.5 397B A17B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1123 | Not measured | Only GLM 5 measured |
| Design Arena: agents building mobile apps | 1136 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Android apps | 1152 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Godot games | 1144 | Not measured | Only GLM 5 measured |
| Design Arena: HTML slides | 1148 | Not measured | Only GLM 5 measured |
| τ²-Bench Telecom | 98.2% | 95.6% | GLM 5 |
| APEX-Agents | Not measured | 24.9% | Only Qwen3.5 397B A17B measured |
| Vending-Bench 2 | $4,432 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | Not measured | Only GLM 5 measured |
| GPQA Diamond (Artificial Analysis run) | 82.0% | 89.3% | Qwen3.5 397B A17B |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | 29.0% | GLM 5 |
| GPQA Diamond (Epoch AI run) | 87.8% | 86.4% | GLM 5 |
| ARC-AGI | 44.7% | Not measured | Only GLM 5 measured |
| ARC-AGI-2 | 4.9% | Not measured | Only GLM 5 measured |
| SimpleBench | 53.2% | Not measured | Only GLM 5 measured |
| Mystery game puzzles | Not measured | 18.0% | Only Qwen3.5 397B A17B measured |
| LMArena Text: hard prompts | 1452 | 1448 | GLM 5 |
| LMArena Text: expert | 1454 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: hard prompts in English | 1460 | 1450 | GLM 5 |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AIME | 91.7% | Not measured | Only GLM 5 measured |
| OTIS Mock AIME | 80.0% | 88.9% | Qwen3.5 397B A17B |
| FrontierMath, tiers 1 to 3 | Not measured | 31.2% | Only Qwen3.5 397B A17B measured |
| LMArena Text: math | 1440 | 1453 | Qwen3.5 397B A17B |
| LMArena Text: mathematical work | 1451 | 1454 | Qwen3.5 397B A17B |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | Not measured | Only GLM 5 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5 measured |
| CaseLaw (Vals AI) | 52.5% | Not measured | Only GLM 5 measured |
| CorpFin (Vals AI) | 62.9% | Not measured | Only GLM 5 measured |
| TaxEval (Vals AI) | 70.0% | Not measured | Only GLM 5 measured |
| MedQA (Vals AI) | 94.3% | Not measured | Only GLM 5 measured |
| LMArena Text: science | 1460 | 1457 | GLM 5 |
| LMArena Text: medicine and healthcare | 1455 | 1455 | GLM 5 |
| LMArena Text: legal and government | 1453 | 1441 | GLM 5 |
| LMArena Text: business and finance | 1430 | 1435 | Qwen3.5 397B A17B |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 1191 | GLM 5 |
| Design Arena: UI components | 1230 | 1163 | GLM 5 |
| Design Arena: game dev | 1234 | 1141 | GLM 5 |
| Design Arena: data viz | 1228 | 1175 | GLM 5 |
| Design Arena: 3D | 1229 | 1161 | GLM 5 |
| Design Arena: all code categories | 1246 | 1180 | GLM 5 |
| Design Arena: SVG | 1168 | 1139 | GLM 5 |
| Design Arena: ASCII art | 1146 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index145
| Benchmark | GLM 5 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| IFBench | 72.3% | 78.8% | Qwen3.5 397B A17B |
| Long context reasoning (AA-LCR) | 75.7% | 77.3% | Qwen3.5 397B A17B |
| DTBench | Not measured | 87.5% | Only Qwen3.5 397B A17B measured |
| LMCA | Not measured | 37.9% | Only Qwen3.5 397B A17B measured |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| LMArena Text | 1446 | 1437 | GLM 5 |
| LMArena Text: instruction following | 1428 | 1424 | GLM 5 |
| LMArena Text: creative writing | 1438 | 1402 | GLM 5 |
| LMArena Vision | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Text: English | 1458 | 1443 | GLM 5 |
| LMArena Text: non-English | 1430 | 1430 | GLM 5 |
| LMArena Text: Chinese | 1511 | 1498 | GLM 5 |
| LMArena Text: German | 1447 | 1445 | GLM 5 |
| LMArena Text: Japanese | 1416 | 1429 | Qwen3.5 397B A17B |
| LMArena Text: Korean | 1423 | 1384 | GLM 5 |
| LMArena Text: French | 1456 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: Spanish | 1454 | 1441 | GLM 5 |
| LMArena Text: Russian | 1436 | 1430 | GLM 5 |
| LMArena Text: Polish | 1434 | 1447 | Qwen3.5 397B A17B |
| LMArena Text: multi-turn | 1456 | 1446 | GLM 5 |
| LMArena Text: longer queries | 1446 | 1441 | GLM 5 |
| LMArena Text: writing and language | 1437 | 1415 | GLM 5 |
| LMArena Text: entertainment and media | 1426 | 1399 | GLM 5 |
| LMArena Vision: English | Not measured | 1268 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: Chinese | Not measured | 1314 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: reading text | Not measured | 1271 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: humor | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: captioning | Not measured | 1230 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: recognizing things | Not measured | 1265 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: creative writing | Not measured | 1260 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 | Qwen3.5 397B A17B | 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
Qwen3.5 397B A17B costs less
Repository review
50K input + 3K output tokens
Qwen3.5 397B A17B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5 397B A17B 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 | Qwen3.5 397B A17B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | February 11, 2026 | February 16, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $1.00 | $0.39 |
| Output $/Mtok | $3.20 | $2.34 |
| Coding index | 59.7 | 28.6 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.63 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than Qwen3.5 397B A17B for coding?
Pick GLM 5 for coding: it scores 59.7 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B. Pick Qwen3.5 397B A17B when output tokens cost 1.4x less, or when the context window is larger, 262K against 205K tokens.
Which is cheaper, GLM 5 or Qwen3.5 397B A17B?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for GLM 5 only.
Which has the bigger context window, GLM 5 or Qwen3.5 397B A17B?
Qwen3.5 397B A17B, with 262K tokens against 205K.
How many benchmarks is the GLM 5 vs Qwen3.5 397B A17B comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 2 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.