GLM 5.2 vs Qwen3.5 397B A17B
GLM 5.2 leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 33.7 to 21.4, and Qwen3.5 397B A17B costs 1.9x less per output token ($2.34 vs $4.40 per million). Updated October 22, 2026; 55 benchmarks have a result for both.
GLM 5.2 or Qwen3.5 397B A17B: which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding.
Pick Qwen3.5 397B A17B for output tokens 1.9x cheaper ($2.34 vs $4.40 per million) and work measured by OTIS Mock AIME or IFBench, where it leads.
- Shared results
- 55
- GLM 5.2 only
- 43
- Qwen3.5 397B A17B only
- 10
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 21.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1190.
Same board, both measured
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5 397B A17B
GLM 5.2 $0.0036, Qwen3.5 397B A17B $0.0016 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Coding, 16.1 points apart on average over 8 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GLM 5.2 1617 against Qwen3.5 397B A17B 1388, 35.4 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.9x apart.
- Context window
- GLM 5.2 takes 1.05M 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 5.2 | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 8 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 3 | 1 | ||
| Knowledge | 4 | 0 | ||
| Web design | 7 | 0 | ||
| General | 20 | 1 | ||
| All shared boards | 53 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index80
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena WebDev | 1604 | 1400 | GLM 5.2 |
| AA Coding Index | 68.8 | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | 69.5% | Not measured | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | 82.8% | Not measured | Only GLM 5.2 measured |
| Vibe Code Bench (Vals AI) | 64.0% | Not measured | Only GLM 5.2 measured |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | 44.8% | GLM 5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 40.9% | GLM 5.2 |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | 621.9 | GLM 5.2 |
| LMArena Text: coding | 1487 | 1464 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1463 | GLM 5.2 |
| LMArena WebDev: React | 1617 | 1388 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1410 | GLM 5.2 |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 21.4 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | 146.8 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1159 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1178 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1125 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1170 | Not measured | Only GLM 5.2 measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | 31.3% | Not measured | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| τ²-Bench Telecom | 99.1% | 95.6% | GLM 5.2 |
| APEX-Agents | 45.2% | 24.9% | GLM 5.2 |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only 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 index70
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GLM 5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 89.3% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 29.0% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 86.4% | GLM 5.2 |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | 18.0% | GLM 5.2 |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1448 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1462 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1450 | GLM 5.2 |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | 88.9% | Qwen3.5 397B A17B |
| FrontierMath, tiers 1 to 3 | 59.2% | 31.2% | GLM 5.2 |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| LMArena Text: math | 1483 | 1453 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1454 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GLM 5.2 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5.2 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only GLM 5.2 measured |
| TaxEval (Vals AI) | 73.3% | Not measured | Only GLM 5.2 measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1489 | 1457 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1455 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1441 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1435 | GLM 5.2 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1190 | GLM 5.2 |
| Design Arena: UI components | 1294 | 1160 | GLM 5.2 |
| Design Arena: game dev | 1268 | 1140 | GLM 5.2 |
| Design Arena: data viz | 1285 | 1175 | GLM 5.2 |
| Design Arena: 3D | 1287 | 1160 | GLM 5.2 |
| Design Arena: all code categories | 1287 | 1179 | GLM 5.2 |
| Design Arena: SVG | 1213 | 1139 | GLM 5.2 |
| Design Arena: ASCII art | 1191 | Not measured | Only 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 index201
| Benchmark | GLM 5.2 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | 78.8% | Qwen3.5 397B A17B |
| Long context reasoning (AA-LCR) | 78.3% | 77.3% | GLM 5.2 |
| DTBench | 93.6% | 87.5% | GLM 5.2 |
| LMCA | 45.8% | 37.9% | GLM 5.2 |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | 1437 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1424 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1402 | GLM 5.2 |
| LMArena Vision | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Text: English | 1475 | 1443 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1430 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1498 | GLM 5.2 |
| LMArena Text: German | 1470 | 1445 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1429 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1384 | GLM 5.2 |
| LMArena Text: French | 1479 | 1462 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1441 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1430 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1447 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1446 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1441 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1415 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1399 | GLM 5.2 |
| 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 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | 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 5.2 | Qwen3.5 397B A17B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | June 16, 2026 | February 16, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.40 | $0.39 |
| Output $/Mtok | $4.40 | $2.34 |
| Coding index | 61.1 | Not benchmarked |
| Independent measurements behind it | 4 | 1 |
| Cost per solved task | $0.86 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Qwen3.5 397B A17B for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding. Pick Qwen3.5 397B A17B for output tokens 1.9x cheaper ($2.34 vs $4.40 per million) and work measured by OTIS Mock AIME or IFBench, where it leads.
Which is cheaper, GLM 5.2 or Qwen3.5 397B A17B?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 5.2 or Qwen3.5 397B A17B?
GLM 5.2, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.2 vs Qwen3.5 397B A17B comparison based on?
55 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.