GLM 4.7 vs Qwen3.6 Plus
GLM 4.7 leads Qwen3.6 Plus on coding, 55.1 to 44.1 on the Vibe Coding Index, at about the same output price ($2.20 vs $1.95 per million). Updated October 22, 2026; 66 benchmarks have a result for both.
GLM 4.7 or Qwen3.6 Plus: which to pick
Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 44.1 for Qwen3.6 Plus, though on 2 benchmarks against 3.
Pick Qwen3.6 Plus for output tokens 1.1x cheaper ($1.95 vs $2.20 per million) and a larger context window (1M against 205K tokens).
- Shared results
- 66
- GLM 4.7 only
- 15
- Qwen3.6 Plus only
- 17
- 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
GLM 4.7
GLM 4.7 scores 55.1 against 44.1.
2 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.6 Plus
Qwen3.6 Plus leads, 27.0 against 22.2.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.4% against 80.0%.
Same board, both measured
Math
AIME
Qwen3.6 Plus
Qwen3.6 Plus leads, 94.6% against 93.3%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.7% against 82.7%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.6 Plus
Qwen3.6 Plus leads, 1243 against 1225.
Same board, both measured
Long documents
Listed context window
Qwen3.6 Plus
Qwen3.6 Plus takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 Plus
GLM 4.7 $0.0017, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, Qwen3.6 Plus $1.19.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Qwen3.6 Plus 44.1 from 3 benchmarks.
- Widest category
- Agentic work, 9.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vending-Bench 2: GLM 4.7 $2,377 against Qwen3.6 Plus $5,115, 17.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Qwen3.6 Plus $1.95 per million output tokens, 1.1x apart.
- Context window
- GLM 4.7 takes 205K tokens, Qwen3.6 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 4.7 | Bars | Qwen3.6 Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 10 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 1 | 7 | ||
| Math | 0 | 4 | ||
| Knowledge | 4 | 7 | ||
| Web design | 1 | 7 | ||
| General | 7 | 14 | ||
| All shared boards | 13 | 53 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index010
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1461 | Qwen3.6 Plus |
| AA Coding Index | 45.3 | 54.5 | Qwen3.6 Plus |
| LiveCodeBench (Vals AI) | 82.2% | 86.0% | Qwen3.6 Plus |
| SWE-bench Verified (Vals AI) | 69.4% | 73.4% | Qwen3.6 Plus |
| Vibe Code Bench (Vals AI) | Not measured | 25.6% | Only Qwen3.6 Plus measured |
| Code migration (Vals AI) | Not measured | 11.1% | Only Qwen3.6 Plus measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.6 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 53.2% | Only Qwen3.6 Plus measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 43.9% | Qwen3.6 Plus |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | 670.1 | Qwen3.6 Plus |
| LMArena Text: coding | 1454 | 1467 | Qwen3.6 Plus |
| LMArena Text: software and IT | 1453 | 1462 | Qwen3.6 Plus |
| LMArena WebDev: React | 1420 | 1454 | Qwen3.6 Plus |
| LMArena WebDev: HTML | 1447 | 1463 | Qwen3.6 Plus |
| LMArena WebDev: from an image | Not measured | 1472 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 27.0 | Qwen3.6 Plus |
| Epoch Capabilities Index | 143.6 | 147.3 | Qwen3.6 Plus |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| Finance Agent (Vals AI) | Not measured | 40.9% | Only Qwen3.6 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 7.7% | Only Qwen3.6 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 14.9% | Only Qwen3.6 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 32.9% | Only Qwen3.6 Plus measured |
| τ²-Bench Telecom | 95.9% | 97.7% | Qwen3.6 Plus |
| Vending-Bench 2 | $2,377 | $5,115 | Qwen3.6 Plus |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 87.4% | Qwen3.6 Plus |
| MMMU Pro (Vals AI) | Not measured | 84.2% | Only Qwen3.6 Plus measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 88.2% | Qwen3.6 Plus |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 27.8% | Qwen3.6 Plus |
| GPQA Diamond (Epoch AI run) | 83.3% | 88.4% | Qwen3.6 Plus |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | 14.0% | GLM 4.7 |
| Mystery game puzzles | Not measured | 12.0% | Only Qwen3.6 Plus measured |
| LMArena Text: hard prompts | 1443 | 1448 | Qwen3.6 Plus |
| LMArena Text: expert | 1424 | 1454 | Qwen3.6 Plus |
| LMArena Text: hard prompts in English | 1452 | 1455 | Qwen3.6 Plus |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AIME | 93.3% | 94.6% | Qwen3.6 Plus |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 93.3% | Qwen3.6 Plus |
| FrontierMath, tiers 1 to 3 | Not measured | 38.2% | Only Qwen3.6 Plus measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | 1449 | Qwen3.6 Plus |
| LMArena Text: mathematical work | 1411 | 1448 | Qwen3.6 Plus |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index47
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 87.7% | Qwen3.6 Plus |
| LegalBench (Vals AI) | 83.4% | 84.2% | Qwen3.6 Plus |
| CaseLaw (Vals AI) | 54.9% | 51.5% | GLM 4.7 |
| CorpFin (Vals AI) | 46.4% | 61.9% | Qwen3.6 Plus |
| TaxEval (Vals AI) | 68.8% | 74.7% | Qwen3.6 Plus |
| MortgageTax (Vals AI) | Not measured | 68.0% | Only Qwen3.6 Plus measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 36.9% | Qwen3.6 Plus |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 44.1% | Qwen3.6 Plus |
| LMArena Text: science | 1457 | 1439 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1442 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1437 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1430 | Qwen3.6 Plus |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | 1243 | Qwen3.6 Plus |
| Design Arena: UI components | 1196 | 1230 | Qwen3.6 Plus |
| Design Arena: game dev | 1188 | 1211 | Qwen3.6 Plus |
| Design Arena: data viz | 1195 | 1228 | Qwen3.6 Plus |
| Design Arena: 3D | 1190 | 1202 | Qwen3.6 Plus |
| Design Arena: all code categories | 1216 | 1236 | Qwen3.6 Plus |
| Design Arena: SVG | 1142 | 1161 | Qwen3.6 Plus |
| Design Arena: ASCII art | 1165 | 1113 | GLM 4.7 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index714
| Benchmark | GLM 4.7 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 44.9% | Only Qwen3.6 Plus measured |
| MedScribe (Vals AI) | 68.6% | 77.0% | Qwen3.6 Plus |
| Vals Multimodal Index | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| IFBench | 67.9% | 75.2% | Qwen3.6 Plus |
| Long context reasoning (AA-LCR) | 71.0% | 78.3% | Qwen3.6 Plus |
| DTBench | Not measured | 81.9% | Only Qwen3.6 Plus measured |
| LMCA | Not measured | 33.1% | Only Qwen3.6 Plus measured |
| CL-bench | 15.9% | 20.3% | Qwen3.6 Plus |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1436 | Qwen3.6 Plus |
| LMArena Text: instruction following | 1412 | 1424 | Qwen3.6 Plus |
| LMArena Text: creative writing | 1401 | 1404 | Qwen3.6 Plus |
| LMArena Text: English | 1453 | 1445 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1424 | Qwen3.6 Plus |
| LMArena Text: Chinese | 1495 | 1477 | GLM 4.7 |
| LMArena Text: German | 1426 | 1451 | Qwen3.6 Plus |
| LMArena Text: Japanese | 1439 | 1390 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1379 | GLM 4.7 |
| LMArena Text: French | 1432 | 1455 | Qwen3.6 Plus |
| LMArena Text: Spanish | 1434 | 1432 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1434 | Qwen3.6 Plus |
| LMArena Text: Polish | 1435 | 1433 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1438 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1439 | Qwen3.6 Plus |
| LMArena Text: writing and language | 1411 | 1418 | Qwen3.6 Plus |
| LMArena Text: entertainment and media | 1400 | 1402 | Qwen3.6 Plus |
| 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 4.7 | Qwen3.6 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
Qwen3.6 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.6 Plus costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 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 4.7 | Qwen3.6 Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | April 2, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.60 | $0.325 |
| Output $/Mtok | $2.20 | $1.95 |
| Coding index | 55.1 | 44.1 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.29 | $1.19 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Qwen3.6 Plus for coding?
Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 44.1 for Qwen3.6 Plus, though on 2 benchmarks against 3. Pick Qwen3.6 Plus for output tokens 1.1x cheaper ($1.95 vs $2.20 per million) and a larger context window (1M against 205K tokens).
Which is cheaper, GLM 4.7 or Qwen3.6 Plus?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Qwen3.6 Plus $1.19.
Which has the bigger context window, GLM 4.7 or Qwen3.6 Plus?
Qwen3.6 Plus, with 1M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Qwen3.6 Plus comparison based on?
66 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Qwen3.6 Plus's on 3. Where only one model has a result, the page says so instead of naming a winner.