GLM 4.7 vs Qwen3.7 Max
GLM 4.7 leads Qwen3.7 Max on coding, 55.1 to 54.1 on the Vibe Coding Index, and GLM 4.7 costs 2.0x less per output token ($2.20 vs $4.425 per million). Updated October 22, 2026; 40 benchmarks have a result for both.
GLM 4.7 or Qwen3.7 Max: which to pick
GLM 4.7 and Qwen3.7 Max are level for coding, 55.1 against 54.1 on the Vibe Coding Index, so price and context decide.
Pick Qwen3.7 Max for a larger context window (1M against 205K tokens) and work measured by LMArena WebDev or AA Coding Index, where it leads. Pick GLM 4.7 for output tokens 2.0x cheaper ($2.20 vs $4.425 per million) and a lower cost per solved task ($0.29 against $4.69).
- Shared results
- 40
- GLM 4.7 only
- 41
- Qwen3.7 Max only
- 19
- 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
No call
Level: 55.1 against 54.1, inside 2 points.
2 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 22.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 80.0%.
Same board, both measured
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 82.7%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1225.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0017, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Math, 16.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- ALE-Bench: GLM 4.7 399.5 against Qwen3.7 Max 1189.4, 26.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Qwen3.7 Max $4.425 per million output tokens, 2.0x apart.
- Context window
- GLM 4.7 takes 205K tokens, Qwen3.7 Max 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 | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 1 | 7 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 4 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 6 | ||
| Web design | 0 | 8 | ||
| General | 0 | 3 | ||
| All shared boards | 2 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index17
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1515 | Qwen3.7 Max |
| AA Coding Index | 45.3 | 66.0 | Qwen3.7 Max |
| LiveCodeBench (Vals AI) | 82.2% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 69.4% | 68.8% | GLM 4.7 |
| Vibe Code Bench (Vals AI) | Not measured | 47.7% | Only Qwen3.7 Max measured |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 50.8% | Qwen3.7 Max |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | 1189.4 | Qwen3.7 Max |
| LMArena Text: coding | 1454 | Not measured | Only GLM 4.7 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: React | 1420 | 1514 | Qwen3.7 Max |
| LMArena WebDev: HTML | 1447 | 1512 | Qwen3.7 Max |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 143.6 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | 1050 | 1191 | Qwen3.7 Max |
| Design Arena: agents building mobile apps | 1105 | 1155 | Qwen3.7 Max |
| Design Arena: agents building Android apps | 1112 | 1159 | Qwen3.7 Max |
| Design Arena: agents building Godot games | 1058 | 1187 | Qwen3.7 Max |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1108 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1171 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 95.9% | 94.7% | GLM 4.7 |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 90.2% | Qwen3.7 Max |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 83.3% | 90.9% | Qwen3.7 Max |
| SimpleBench | 47.7% | 70.4% | Qwen3.7 Max |
| Chess puzzles | 21.0% | 26.0% | Qwen3.7 Max |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 4.7 measured |
| LMArena Text: expert | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | 1452 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| 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% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only Qwen3.7 Max measured |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | 6.0% | 26.0% | Qwen3.7 Max |
| LMArena Text: math | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: mathematical work | 1411 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 83.4% | 84.9% | Qwen3.7 Max |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | 68.8% | 75.3% | Qwen3.7 Max |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 38.8% | Qwen3.7 Max |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 55.8% | Qwen3.7 Max |
| LMArena Text: science | 1457 | Not measured | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | 1469 | Not measured | Only GLM 4.7 measured |
| LMArena Text: legal and government | 1444 | Not measured | Only GLM 4.7 measured |
| LMArena Text: business and finance | 1429 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1196 | 1267 | Qwen3.7 Max |
| Design Arena: game dev | 1188 | 1264 | Qwen3.7 Max |
| Design Arena: data viz | 1195 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1190 | 1265 | Qwen3.7 Max |
| Design Arena: all code categories | 1216 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1142 | 1216 | Qwen3.7 Max |
| Design Arena: ASCII art | 1165 | 1211 | Qwen3.7 Max |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.7 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | 79.4% | Qwen3.7 Max |
| IFBench | 67.9% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 71.0% | 79.0% | Qwen3.7 Max |
| DTBench | Not measured | 92.3% | Only Qwen3.7 Max measured |
| LMCA | Not measured | 44.0% | Only Qwen3.7 Max measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: instruction following | 1412 | Not measured | Only GLM 4.7 measured |
| LMArena Text: creative writing | 1401 | Not measured | Only GLM 4.7 measured |
| LMArena Text: English | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Chinese | 1495 | Not measured | Only GLM 4.7 measured |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1434 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Russian | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | Not measured | Only GLM 4.7 measured |
| LMArena Text: longer queries | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: writing and language | 1411 | Not measured | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | Qwen3.7 Max | 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 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 | Qwen3.7 Max |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | May 21, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.60 | $1.475 |
| Output $/Mtok | $2.20 | $4.425 |
| Coding index | 55.1 | 54.1 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.29 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Qwen3.7 Max for coding?
GLM 4.7 and Qwen3.7 Max are level for coding, 55.1 against 54.1 on the Vibe Coding Index, so price and context decide. Pick Qwen3.7 Max for a larger context window (1M against 205K tokens) and work measured by LMArena WebDev or AA Coding Index, where it leads. Pick GLM 4.7 for output tokens 2.0x cheaper ($2.20 vs $4.425 per million) and a lower cost per solved task ($0.29 against $4.69).
Which is cheaper, GLM 4.7 or Qwen3.7 Max?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Qwen3.7 Max $4.69.
Which has the bigger context window, GLM 4.7 or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Qwen3.7 Max comparison based on?
40 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.