GLM 4.6V vs Qwen-Plus
Updated October 21, 2026. 20 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 20
- GLM 4.6V only
- 18
- Qwen-Plus only
- 8
- Like-for-like categories
- 4 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.6V has a AA Intelligence Index result, 11.2.
Directional only
Long documents
Listed context window
Qwen-Plus
Qwen-Plus takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen-Plus
GLM 4.6V $0.0008, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Qwen-Plus not benchmarked.
- Widest category
- Coding, 9.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.6V 1425 against Qwen-Plus 1347, 12.9 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Qwen-Plus $0.78 per million output tokens, 1.2x apart.
- Context window
- GLM 4.6V takes 131K tokens, Qwen-Plus 1M.
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 | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 20 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | Not measured | Only GLM 4.6V measured |
| LMArena Text: coding | 1389 | 1328 | GLM 4.6V |
| LMArena Text: software and IT | 1391 | 1343 | GLM 4.6V |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | Not measured | Only GLM 4.6V measured |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | Not measured | Only GLM 4.6V measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.1% | Only Qwen-Plus measured |
| LMArena Text: hard prompts | 1367 | 1317 | GLM 4.6V |
| LMArena Text: expert | 1371 | 1328 | GLM 4.6V |
| LMArena Text: hard prompts in English | 1391 | 1333 | GLM 4.6V |
| 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 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 4.6V | Qwen-Plus | 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 | 17.8% | Only Qwen-Plus measured |
| MATH level 5 | Not measured | 65.3% | Only Qwen-Plus measured |
| LMArena Text: math | Not measured | 1326 | Only Qwen-Plus measured |
| LMArena Text: mathematical work | Not measured | 1332 | Only Qwen-Plus measured |
| 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 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| LMArena Text: science | 1389 | 1352 | GLM 4.6V |
| LMArena Text: medicine and healthcare | 1389 | 1350 | GLM 4.6V |
| LMArena Text: legal and government | 1393 | 1349 | GLM 4.6V |
| LMArena Text: business and finance | 1359 | 1303 | GLM 4.6V |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GLM 4.6V | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 30.1% | Not measured | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | 48.7% | Not measured | Only GLM 4.6V measured |
| DTBench | Not measured | 81.1% | Only Qwen-Plus measured |
| LMCA | Not measured | 24.0% | Only Qwen-Plus measured |
| LMArena Text | 1376 | 1326 | GLM 4.6V |
| LMArena Text: instruction following | 1350 | 1303 | GLM 4.6V |
| LMArena Text: creative writing | 1347 | 1292 | GLM 4.6V |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | 1339 | GLM 4.6V |
| LMArena Text: non-English | 1358 | 1310 | GLM 4.6V |
| LMArena Text: Chinese | 1425 | 1347 | GLM 4.6V |
| LMArena Text: Japanese | Not measured | 1251 | Only Qwen-Plus measured |
| LMArena Text: Russian | 1339 | 1323 | GLM 4.6V |
| LMArena Text: multi-turn | 1359 | 1336 | GLM 4.6V |
| LMArena Text: longer queries | 1357 | 1324 | GLM 4.6V |
| LMArena Text: writing and language | 1347 | 1296 | GLM 4.6V |
| LMArena Text: entertainment and media | 1343 | 1270 | GLM 4.6V |
| 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 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Qwen-Plus | 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
Qwen-Plus costs less
Repository review
50K input + 3K output tokens
Qwen-Plus costs less
Agent loop
200K input + 10K output tokens
Qwen-Plus 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 | Qwen-Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | December 8, 2025 | February 1, 2025 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.30 | $0.26 |
| Output $/Mtok | $0.90 | $0.78 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Qwen-Plus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GLM 4.6V or Qwen-Plus?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or Qwen-Plus?
Qwen-Plus, with 1M tokens against 131K.
How many benchmarks is the GLM 4.6V vs Qwen-Plus comparison based on?
20 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.