GLM 4.6 vs Qwen3.5-122B-A10B
GLM 4.6 leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 18.5 to 17.7, at about the same output price ($2.20 vs $2.08 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
GLM 4.6 or Qwen3.5-122B-A10B: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding.
Pick Qwen3.5-122B-A10B for output tokens 1.1x cheaper ($2.08 vs $2.20 per million) and a larger context window (262K against 205K tokens).
- Shared results
- 38
- GLM 4.6 only
- 31
- Qwen3.5-122B-A10B only
- 13
- Like-for-like categories
- 7 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 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 17.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.6 has a Design Arena: websites result, 1174.
Directional only
Long documents
Listed context window
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-122B-A10B
GLM 4.6 $0.0017, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Qwen3.5-122B-A10B not benchmarked.
- Widest category
- Agentic work, 16.7 points apart on average over 1 shared benchmark.
- Widest single gap
- IFBench: GLM 4.6 43.4% against Qwen3.5-122B-A10B 75.7%, 32.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Qwen3.5-122B-A10B $2.08 per million output tokens, 1.1x apart.
- Context window
- GLM 4.6 takes 205K tokens, Qwen3.5-122B-A10B 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 4.6 | Bars | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 3 | 3 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 2 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 2 | ||
| All shared boards | 30 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index33
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | 1361 | Qwen3.5-122B-A10B |
| AA Coding Index | 45.8 | 43.3 | GLM 4.6 |
| LiveCodeBench (Vals AI) | 81.0% | Not measured | Only GLM 4.6 measured |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only GLM 4.6 measured |
| SciCode | Not measured | 39.7% | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 31.1% | Qwen3.5-122B-A10B |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | 1436 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1436 | GLM 4.6 |
| LMArena WebDev: React | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | 1350 | 1351 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 17.7 | GLM 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 index01
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | 93.6% | Qwen3.5-122B-A10B |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | Not measured | Only GLM 4.6 measured |
| BrowseComp-Plus | 48.2% | Not measured | Only 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 index32
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 85.7% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 25.2% | Qwen3.5-122B-A10B |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts | 1440 | 1421 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1431 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1431 | GLM 4.6 |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: homework | Not measured | 1261 | Only Qwen3.5-122B-A10B measured |
| Kagi LLM Benchmark | 45.7% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| LMArena Text: math | 1432 | 1431 | GLM 4.6 |
| LMArena Text: mathematical work | 1447 | 1431 | GLM 4.6 |
| 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 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | Not measured | Only GLM 4.6 measured |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| LMArena Text: science | 1456 | 1432 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1423 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1422 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1414 | GLM 4.6 |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1145 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1150 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1128 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1108 | Not measured | Only 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 index172
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| IFBench | 43.4% | 75.7% | Qwen3.5-122B-A10B |
| Long context reasoning (AA-LCR) | 54.0% | 76.3% | Qwen3.5-122B-A10B |
| DTBench | Not measured | 84.3% | Only Qwen3.5-122B-A10B measured |
| LMCA | Not measured | 32.2% | Only Qwen3.5-122B-A10B measured |
| LMArena Text | 1440 | 1417 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1399 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1368 | GLM 4.6 |
| LMArena Vision | Not measured | 1244 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | 1449 | 1428 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1401 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1463 | GLM 4.6 |
| LMArena Text: German | 1447 | 1425 | GLM 4.6 |
| LMArena Text: Japanese | 1393 | 1367 | GLM 4.6 |
| LMArena Text: Korean | 1400 | 1353 | GLM 4.6 |
| LMArena Text: French | 1459 | 1443 | GLM 4.6 |
| LMArena Text: Spanish | 1436 | 1425 | GLM 4.6 |
| LMArena Text: Russian | 1418 | 1400 | GLM 4.6 |
| LMArena Text: Polish | 1447 | 1398 | GLM 4.6 |
| LMArena Text: multi-turn | 1427 | 1416 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1410 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1387 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1366 | GLM 4.6 |
| LMArena Vision: English | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: reading text | Not measured | 1251 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: humor | Not measured | 1243 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | Qwen3.5-122B-A10B | 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
Qwen3.5-122B-A10B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-122B-A10B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-122B-A10B 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 4.6 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | February 25, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.60 | $0.26 |
| Output $/Mtok | $2.20 | $2.08 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Qwen3.5-122B-A10B for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding. Pick Qwen3.5-122B-A10B for output tokens 1.1x cheaper ($2.08 vs $2.20 per million) and a larger context window (262K against 205K tokens).
Which is cheaper, GLM 4.6 or Qwen3.5-122B-A10B?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B, with 262K tokens against 205K.
How many benchmarks is the GLM 4.6 vs Qwen3.5-122B-A10B comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.