Model comparison
GLM-5.1 vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 47.8 on the Noometry Index. GLM-5.1 costs 1.7× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GLM-5.1 scores higher in 2 categories and Qwen3.7 Max in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.4 to 49.7.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 36.8% for GLM-5.1 and 64.6% for Qwen3.7 Max.
- GLM-5.1 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 200K.
- GLM-5.1 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.1 | Qwen3.7 Max | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 47.8 | 51.5 |
| Released | 2026-04-07 | 2026-05-19 |
| Weights | Open | Proprietary |
| Context window | 200K | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $2.50 |
| Output $ / M tokens | $4.40 | $7.50 |
| Results tracked | 41 | 33 |
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Category by category
Coding Qwen3.7 Max leads
GLM-5.1: 48.7 (#55), Qwen3.7 Max: 50.4 (#45)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| SWE-bench Verified | 74.2% | 77.3% |
| LMArena WebDev | 1508 | 1515 |
| SciCode | 43.8% | 48.8% |
| LMArena Coding | 1485 | 1498 |
| ALE-Bench | 887.1 | 1,189 |
| WeirdML | 57.1% | — |
Agentic & Tool Use GLM-5.1 leads
GLM-5.1: 24.9 (#113), Qwen3.7 Max: 22.1 (#135)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| GBAEval | 0% | 0.4% |
| APEX-Agents | 40.9% | — |
| ExploitBench | 18.1% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning Qwen3.7 Max leads
GLM-5.1: 39.1 (#60), Qwen3.7 Max: 49.2 (#38)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 55.1% | 70.4% |
| NYT Connections (extended) | 77.7% | 85.1% |
| CritPt | 4.6% | 13.4% |
| Chess Puzzles | 19% | 19% |
| LMArena Hard Prompts | 1472 | 1483 |
| Epoch Capabilities Index | 149.84 | 153.68 |
| Thematic Generalization | 69.8% | — |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 32% |
| DTBench | — | 92.3% |
| LMCA | — | 44% |
Math Qwen3.7 Max leads
GLM-5.1: 49.7 (#60), Qwen3.7 Max: 62.4 (#32)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.8% | 64.6% |
| OTIS Mock AIME 2024-2025 | 93.3% | 95.6% |
| ProofBench | 22.2% | 26% |
| LMArena Math | 1473 | 1490 |
| FrontierMath Tier 4 | — | 34.1% |
| MathArena Final-Answer Competitions | 67.1% | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge Qwen3.7 Max leads
GLM-5.1: 54.9 (#50), Qwen3.7 Max: 61.6 (#28)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 89.9% | 90.9% |
| SimpleQA Verified | 34% | 55.8% |
| LMArena Expert | 1476 | 1488 |
Multilingual Qwen3.7 Max leads
GLM-5.1: 55.0 (#36), Qwen3.7 Max: 56.9 (#15)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1447 | 1474 |
| LMArena Chinese | 1515 | 1530 |
| LMArena Russian | 1454 | 1484 |
| LMArena French | 1474 | — |
| LMArena German | 1465 | — |
| LMArena Japanese | 1434 | — |
| LMArena Korean | 1418 | — |
| LMArena Spanish | 1469 | — |
Instruction Following Too close to call
GLM-5.1: 76.3 (#42), Qwen3.7 Max: 76.7 (#38)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1451 | 1460 |
Long Context Too close to call
GLM-5.1: 44.9 (#53), Qwen3.7 Max: 45.4 (#40)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1466 | 1482 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), Qwen3.7 Max: 65.0 (#54)
| Benchmark | GLM-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1461 | 1476 |
| LMArena Creative Writing | 1453 | 1449 |
| LMArena Multi-Turn | 1472 | 1481 |
| EQ-Bench Creative Writing | 1592 | — |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is GLM-5.1 better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 47.8 on the Noometry Index. GLM-5.1 costs 1.7× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or Qwen3.7 Max?
GLM-5.1 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is GLM-5.1 or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 48.7 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 200K.
How many benchmarks do GLM-5.1 and Qwen3.7 Max share?
27 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Qwen3.7 Max has 33.