GLM 4.7 Flash vs Qwen3.7 Max
Qwen3.7 Max leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 29.5 to 14.9, and GLM 4.7 Flash costs 11.1x less per output token ($0.40 vs $4.425 per million). Updated October 21, 2026; 16 benchmarks have a result for both.
GLM 4.7 Flash or Qwen3.7 Max: which to pick
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 11.1x cheaper ($0.40 vs $4.425 per million) and work measured by τ²-Bench Telecom, where it leads.
- Shared results
- 16
- GLM 4.7 Flash only
- 27
- Qwen3.7 Max only
- 43
- Like-for-like categories
- 7 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
Only Qwen3.7 Max has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 14.9.
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
No call
Only Qwen3.7 Max has a GPQA Diamond result, 90.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.7 Max has a MMLU Pro result, 89.3%.
Directional only
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1194.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.7 Max has the number, $4.69.
Directional only
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Math, 37.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against Qwen3.7 Max 79.0%, 37.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Qwen3.7 Max $4.425 per million output tokens, 11.1x apart.
- Context window
- GLM 4.7 Flash takes 200K 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 Flash | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| Web design | 0 | 7 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 15 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1515 | Only Qwen3.7 Max measured |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | Not measured | 87.1% | Only Qwen3.7 Max measured |
| SWE-bench Verified (Vals AI) | Not measured | 68.8% | Only Qwen3.7 Max measured |
| 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 |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 50.8% | Qwen3.7 Max |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena Text: coding | 1382 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | 1389 | Not measured | Only GLM 4.7 Flash measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | Not measured | 1512 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | Not measured | 154.0 | Only Qwen3.7 Max measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 Flash | 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 | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1155 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1158 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1197 | 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 | 98.8% | 94.7% | GLM 4.7 Flash |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.2% | Only Qwen3.7 Max measured |
| GPQA Diamond (Artificial Analysis run) | 58.1% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 60.5% | 90.9% | Qwen3.7 Max |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| 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 | 1356 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: expert | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | 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 | Not measured | 26.0% | Only Qwen3.7 Max measured |
| LMArena Text: math | 1355 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.3% | Only Qwen3.7 Max measured |
| LegalBench (Vals AI) | Not measured | 84.9% | Only Qwen3.7 Max measured |
| CorpFin (Vals AI) | Not measured | 63.7% | Only Qwen3.7 Max measured |
| TaxEval (Vals AI) | Not measured | 75.3% | Only Qwen3.7 Max measured |
| MedCode (Vals AI) | Not measured | 38.8% | Only Qwen3.7 Max measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| LMArena Text: science | 1358 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | 1343 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | 1343 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1208 | 1270 | Qwen3.7 Max |
| Design Arena: game dev | 1135 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1123 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1121 | 1266 | Qwen3.7 Max |
| Design Arena: all code categories | 1178 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1038 | 1217 | Qwen3.7 Max |
| Design Arena: ASCII art | Not measured | 1211 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 Flash | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 79.4% | Only Qwen3.7 Max measured |
| IFBench | 60.8% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 41.7% | 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 |
| LMArena Text | 1351 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | 1327 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | 1296 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: English | 1373 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | 1330 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | 1403 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: German | 1336 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Korean | 1283 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: French | 1332 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | 1350 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | 1333 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | 1291 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | 1342 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | 1345 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | 1320 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | 1304 | Not measured | Only GLM 4.7 Flash 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 4.7 Flash | 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 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 Flash 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 Flash | Qwen3.7 Max |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | May 21, 2026 |
| Context window | 200K | 1M |
| Input $/Mtok | $0.0653 | $1.475 |
| Output $/Mtok | $0.40 | $4.425 |
| Coding index | Not benchmarked | 54.1 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 Flash better than Qwen3.7 Max for coding?
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 11.1x cheaper ($0.40 vs $4.425 per million) and work measured by τ²-Bench Telecom, where it leads.
Which is cheaper, GLM 4.7 Flash or Qwen3.7 Max?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Cost per solved task is known for Qwen3.7 Max only.
Which has the bigger context window, GLM 4.7 Flash or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs Qwen3.7 Max comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 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.