Model comparison
GLM-5 vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.4 to 45.9 on the Noometry Index. GLM-5 costs 2.4× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. GLM-5 scores higher in 2 categories and Qwen3.7 Max in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.7 Max leads 48.4 to 27.6.
- The biggest single-benchmark swing is SimpleBench: 53.2% for GLM-5 and 70.4% for Qwen3.7 Max.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | Qwen3.7 Max | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 45.9 | 51.4 |
| Released | 2026-02-11 | 2026-05-19 |
| Weights | Open | Proprietary |
| Context window | 205K | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1 | $2.50 |
| Output $ / M tokens | $3.20 | $7.50 |
| Results tracked | 45 | 33 |
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Category by category
Coding Qwen3.7 Max leads
GLM-5: 49.0 (#47), Qwen3.7 Max: 50.2 (#43)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| SWE-bench Verified | 72.1% | 77.3% |
| LMArena WebDev | 1434 | 1515 |
| LMArena Coding | 1461 | 1498 |
| ALE-Bench | 765.62 | 1,189 |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 48.8% |
| WeirdML | 48.2% | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 30.9 (#65), Qwen3.7 Max: 21.8 (#129)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| GBAEval | — | 0.4% |
| Vending-Bench 2 | 4,432 | — |
Reasoning Qwen3.7 Max leads
GLM-5: 27.6 (#100), Qwen3.7 Max: 48.4 (#36)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 53.2% | 70.4% |
| NYT Connections (extended) | 74.8% | 85.1% |
| Chess Puzzles | 10% | 19% |
| LMArena Hard Prompts | 1451 | 1483 |
| Epoch Capabilities Index | 145.84 | 153.74 |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 75% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 13.4% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 32% |
| DTBench | — | 92.3% |
| LMCA | — | 44% |
| ForecastBench | 61 | — |
Math Qwen3.7 Max leads
GLM-5: 44.8 (#66), Qwen3.7 Max: 62.3 (#26)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 95.6% |
| LMArena Math | 1440 | 1488 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 34.1% |
| MathArena Final-Answer Competitions | 65.7% | — |
| ProofBench | — | 26% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3.7 Max leads
GLM-5: 52.2 (#59), Qwen3.7 Max: 61.7 (#24)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 87.8% | 90.9% |
| LMArena Expert | 1454 | 1488 |
| SimpleQA Verified | — | 55.8% |
| Vectara Hallucination Rate | 10.1% | — |
Multilingual Qwen3.7 Max leads
GLM-5: 53.9 (#57), Qwen3.7 Max: 57.1 (#13)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1430 | 1475 |
| LMArena Chinese | 1511 | 1533 |
| LMArena Russian | 1436 | 1482 |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1421 | — |
| LMArena Spanish | 1452 | — |
Instruction Following Qwen3.7 Max leads
GLM-5: 75.4 (#64), Qwen3.7 Max: 76.9 (#34)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1427 | 1460 |
Long Context Too close to call
GLM-5: 44.6 (#58), Qwen3.7 Max: 45.4 (#34)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1446 | 1484 |
| CL-bench | 18.7% | — |
Writing & Preference Too close to call
GLM-5: 66.2 (#38), Qwen3.7 Max: 65.2 (#52)
| Benchmark | GLM-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1446 | 1476 |
| LMArena Creative Writing | 1438 | 1450 |
| LMArena Multi-Turn | 1456 | 1482 |
| EQ-Bench Creative Writing | 1601 | — |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is GLM-5 better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.4 to 45.9 on the Noometry Index. GLM-5 costs 2.4× 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 or Qwen3.7 Max?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is GLM-5 or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.2 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 205K.
How many benchmarks do GLM-5 and Qwen3.7 Max share?
21 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3.7 Max has 33.