GLM 4.6V vs Qwen3.5-35B-A3B
Qwen3.5-35B-A3B leads GLM 4.6V on the Artificial Analysis Intelligence Index, 19.3 to 11.2, and GLM 4.6V costs 1.4x less per output token ($0.90 vs $1.30 per million). Updated October 22, 2026; 27 benchmarks have a result for both.
GLM 4.6V or Qwen3.5-35B-A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for output tokens 1.4x cheaper ($0.90 vs $1.30 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 27
- GLM 4.6V only
- 11
- Qwen3.5-35B-A3B only
- 18
- Like-for-like categories
- 6 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B leads, 19.3 against 11.2.
Same board, both measured
Long documents
Listed context window
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
GLM 4.6V $0.0008, Qwen3.5-35B-A3B $0.0008 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Qwen3.5-35B-A3B not benchmarked.
- Widest category
- Agentic work, 57.6 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.6V 31.6% against Qwen3.5-35B-A3B 89.2%, 57.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Qwen3.5-35B-A3B $1.30 per million output tokens, 1.4x apart.
- Context window
- GLM 4.6V takes 131K tokens, Qwen3.5-35B-A3B 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 4.6V | Bars | Qwen3.5-35B-A3B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Knowledge | 0 | 4 | ||
| General | 1 | 12 | ||
| All shared boards | 1 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1254 | Only Qwen3.5-35B-A3B measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 26.5% | Qwen3.5-35B-A3B |
| ALE-Bench | Not measured | 221.8 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: coding | 1389 | 1410 | Qwen3.5-35B-A3B |
| LMArena Text: software and IT | 1391 | 1415 | Qwen3.5-35B-A3B |
| LMArena WebDev: React | Not measured | 1238 | Only Qwen3.5-35B-A3B measured |
| LMArena WebDev: HTML | Not measured | 1301 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 19.3 | Qwen3.5-35B-A3B |
| Epoch Capabilities Index | Not measured | 142.5 | Only Qwen3.5-35B-A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | 89.2% | Qwen3.5-35B-A3B |
| Vending-Bench 2 | Not measured | $463 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 84.5% | Qwen3.5-35B-A3B |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 21.0% | Qwen3.5-35B-A3B |
| GPQA Diamond (Epoch AI run) | Not measured | 83.5% | Only Qwen3.5-35B-A3B measured |
| LMArena Text: hard prompts | 1367 | 1400 | Qwen3.5-35B-A3B |
| LMArena Text: expert | 1371 | 1408 | Qwen3.5-35B-A3B |
| LMArena Text: hard prompts in English | 1391 | 1411 | Qwen3.5-35B-A3B |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| OTIS Mock AIME | Not measured | 70.0% | Only Qwen3.5-35B-A3B measured |
| LMArena Text: math | Not measured | 1404 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: mathematical work | Not measured | 1410 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| LMArena Text: science | 1389 | 1413 | Qwen3.5-35B-A3B |
| LMArena Text: medicine and healthcare | 1389 | 1421 | Qwen3.5-35B-A3B |
| LMArena Text: legal and government | 1393 | 1399 | Qwen3.5-35B-A3B |
| LMArena Text: business and finance | 1359 | 1394 | Qwen3.5-35B-A3B |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| IFBench | 30.1% | 72.5% | Qwen3.5-35B-A3B |
| Long context reasoning (AA-LCR) | 48.7% | 72.0% | Qwen3.5-35B-A3B |
| DTBench | Not measured | 80.0% | Only Qwen3.5-35B-A3B measured |
| LMCA | Not measured | 29.5% | Only Qwen3.5-35B-A3B measured |
| LMArena Text | 1376 | 1395 | Qwen3.5-35B-A3B |
| LMArena Text: instruction following | 1350 | 1379 | Qwen3.5-35B-A3B |
| LMArena Text: creative writing | 1347 | 1347 | GLM 4.6V |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | 1407 | Qwen3.5-35B-A3B |
| LMArena Text: non-English | 1358 | 1378 | Qwen3.5-35B-A3B |
| LMArena Text: Chinese | 1425 | 1457 | Qwen3.5-35B-A3B |
| LMArena Text: German | Not measured | 1367 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Japanese | Not measured | 1326 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Korean | Not measured | 1355 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: French | Not measured | 1412 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Spanish | Not measured | 1390 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Russian | 1339 | 1376 | Qwen3.5-35B-A3B |
| LMArena Text: Polish | Not measured | 1385 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: multi-turn | 1359 | 1390 | Qwen3.5-35B-A3B |
| LMArena Text: longer queries | 1357 | 1389 | Qwen3.5-35B-A3B |
| LMArena Text: writing and language | 1347 | 1365 | Qwen3.5-35B-A3B |
| LMArena Text: entertainment and media | 1343 | 1347 | Qwen3.5-35B-A3B |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Qwen3.5-35B-A3B | 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 4.6V costs less
Repository review
50K input + 3K output tokens
Qwen3.5-35B-A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-35B-A3B 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 4.6V | Qwen3.5-35B-A3B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | February 25, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.30 | $0.1625 |
| Output $/Mtok | $0.90 | $1.30 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Qwen3.5-35B-A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 1.4x cheaper ($0.90 vs $1.30 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, GLM 4.6V or Qwen3.5-35B-A3B?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.5-35B-A3B costs $0.1625 and $1.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B, with 262K tokens against 131K.
How many benchmarks is the GLM 4.6V vs Qwen3.5-35B-A3B comparison based on?
27 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.