GLM 4.5 vs Qwen3.7 Flash
Qwen3.7 Flash costs 16.9x less per output token than GLM 4.5 ($0.13 vs $2.20 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
GLM 4.5 or Qwen3.7 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 for work measured by ALE-Bench, where it leads.
- Shared results
- 1
- GLM 4.5 only
- 59
- Qwen3.7 Flash only
- 5
- Like-for-like categories
- 1 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.5 has a AA Intelligence Index result, 12.8.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.5 has a MMLU Pro result, 81.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.5 has a Design Arena: websites result, 1170.
Directional only
Long documents
Listed context window
Qwen3.7 Flash
Qwen3.7 Flash takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Flash
GLM 4.5 $0.0017, Qwen3.7 Flash $0.0001 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, Qwen3.7 Flash not benchmarked.
- Widest category
- Coding, 2.5 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GLM 4.5 344.8 against Qwen3.7 Flash 420.1, 2.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Qwen3.7 Flash $0.13 per million output tokens, 16.9x apart.
- Context window
- GLM 4.5 takes 131K tokens, Qwen3.7 Flash 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 4.5 | Bars | Qwen3.7 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Vals AI) | 67.5% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.5 measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | 420.1 | Qwen3.7 Flash |
| LMArena Text: coding | 1434 | Not measured | Only GLM 4.5 measured |
| LMArena Text: software and IT | 1440 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | Not measured | Only GLM 4.5 measured |
| Epoch Capabilities Index | Not measured | 144.1 | Only Qwen3.7 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | Not measured | Only GLM 4.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.7 Flash measured |
| Mystery game puzzles | Not measured | 15.0% | Only Qwen3.7 Flash measured |
| LMArena Text: hard prompts | 1429 | Not measured | Only GLM 4.5 measured |
| LMArena Text: expert | 1433 | Not measured | Only GLM 4.5 measured |
| LMArena Text: hard prompts in English | 1429 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| OTIS Mock AIME | Not measured | 86.7% | Only Qwen3.7 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 19.3% | Only Qwen3.7 Flash measured |
| LMArena Text: math | 1427 | Not measured | Only GLM 4.5 measured |
| LMArena Text: mathematical work | 1430 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | Not measured | Only GLM 4.5 measured |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CorpFin (Vals AI) | 61.0% | Not measured | Only GLM 4.5 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 4.5 measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | Not measured | Only GLM 4.5 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.5 measured |
| LMArena Text: legal and government | 1438 | Not measured | Only GLM 4.5 measured |
| LMArena Text: business and finance | 1420 | Not measured | Only GLM 4.5 measured |
| 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 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: UI components | 1144 | Not measured | Only GLM 4.5 measured |
| Design Arena: game dev | 1145 | Not measured | Only GLM 4.5 measured |
| Design Arena: data viz | 1159 | Not measured | Only GLM 4.5 measured |
| Design Arena: 3D | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.5 measured |
| Design Arena: SVG | 1096 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | Leader |
|---|---|---|---|
| IFBench | 44.1% | Not measured | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only GLM 4.5 measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | Not measured | Only GLM 4.5 measured |
| LMArena Text: instruction following | 1404 | Not measured | Only GLM 4.5 measured |
| LMArena Text: creative writing | 1395 | Not measured | Only GLM 4.5 measured |
| LMArena Text: English | 1437 | Not measured | Only GLM 4.5 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Chinese | 1465 | Not measured | Only GLM 4.5 measured |
| LMArena Text: German | 1407 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Korean | 1380 | Not measured | Only GLM 4.5 measured |
| LMArena Text: French | 1418 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Spanish | 1454 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Russian | 1413 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Polish | 1422 | Not measured | Only GLM 4.5 measured |
| LMArena Text: multi-turn | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: longer queries | 1412 | Not measured | Only GLM 4.5 measured |
| LMArena Text: writing and language | 1403 | Not measured | Only GLM 4.5 measured |
| LMArena Text: entertainment and media | 1404 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.7 Flash | 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
Qwen3.7 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 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 4.5 | Qwen3.7 Flash |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | July 27, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.60 | $0.03 |
| Output $/Mtok | $2.20 | $0.13 |
| 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 4.5 better than Qwen3.7 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by ALE-Bench, where it leads.
Which is cheaper, GLM 4.5 or Qwen3.7 Flash?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.7 Flash costs $0.03 and $0.13. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 or Qwen3.7 Flash?
Qwen3.7 Flash, with 1M tokens against 131K.
How many benchmarks is the GLM 4.5 vs Qwen3.7 Flash comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Qwen3.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.