GLM 4.6 vs Qwen-Plus
Updated October 21, 2026. 23 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen-Plus has none, so the two cannot be ranked on coding.
Qwen-Plus has the edge where output tokens cost 2.8x less, and where the context window is larger, 1M against 205K tokens.
- Shared results
- 23
- GLM 4.6 only
- 43
- Qwen-Plus only
- 5
- Like-for-like categories
- 5 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
Only GLM 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.6 has a AA Intelligence Index result, 18.5.
Directional only
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
Qwen-Plus
Qwen-Plus takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen-Plus
GLM 4.6 $0.0017, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Qwen-Plus not benchmarked.
- Widest category
- General, 19.5 points apart on average over 12 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.6 1499 against Qwen-Plus 1347, 25.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Qwen-Plus $0.78 per million output tokens, 2.8x apart.
- Context window
- GLM 4.6 takes 205K 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 4.6 | Bars | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 12 | 0 | ||
| All shared boards | 23 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.6 | Qwen-Plus | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| 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 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only GLM 4.6 measured |
| 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 | 1328 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1343 | GLM 4.6 |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6 | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6 | Qwen-Plus | 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 | 1091 | 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% | 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 index30
| Benchmark | GLM 4.6 | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.1% | Only Qwen-Plus measured |
| LMArena Text: hard prompts | 1440 | 1317 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1328 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1333 | GLM 4.6 |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6 | Qwen-Plus | 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 |
| 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 | 1432 | 1326 | GLM 4.6 |
| LMArena Text: mathematical work | 1447 | 1332 | GLM 4.6 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.6 | Qwen-Plus | 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 | 1352 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1350 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1349 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1303 | GLM 4.6 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Qwen-Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1148 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1151 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1129 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1166 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1109 | 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 index120
| Benchmark | GLM 4.6 | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 43.4% | Not measured | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 54.0% | Not measured | Only GLM 4.6 measured |
| DTBench | Not measured | 81.1% | Only Qwen-Plus measured |
| LMCA | Not measured | 24.0% | Only Qwen-Plus measured |
| LMArena Text | 1440 | 1326 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1303 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1292 | GLM 4.6 |
| LMArena Text: English | 1449 | 1339 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1310 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1347 | GLM 4.6 |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | 1251 | GLM 4.6 |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | 1323 | GLM 4.6 |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | 1336 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1324 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1296 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1270 | GLM 4.6 |
| 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 4.6 | 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 4.6 | Qwen-Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | September 30, 2025 | February 1, 2025 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.60 | $0.26 |
| Output $/Mtok | $2.20 | $0.78 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Qwen-Plus for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen-Plus has none, so the two cannot be ranked on coding. Qwen-Plus has the edge where output tokens cost 2.8x less, and where the context window is larger, 1M against 205K tokens.
Which is cheaper, GLM 4.6 or Qwen-Plus?
GLM 4.6 costs $0.60 per million input tokens and $2.20 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 4.6 or Qwen-Plus?
Qwen-Plus, with 1M tokens against 205K.
How many benchmarks is the GLM 4.6 vs Qwen-Plus comparison based on?
23 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.