Model comparison
GLM-4.7 vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 42.0 on the Noometry Index. GLM-4.7 costs 3.0× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Alibaba (Qwen)Summary
- They share 26 benchmarks with published results for both. GLM-4.7 scores higher in 0 categories and Qwen3.8 Max in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 38.6.
- The biggest single-benchmark swing is ProofBench: 6% for GLM-4.7 and 58% for Qwen3.8 Max.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | Qwen3.8 Max | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 56.8 |
| Released | 2025-12-22 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | 205K | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $6 |
| Results tracked | 36 | 39 |
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Category by category
Coding Qwen3.8 Max leads
GLM-4.7: 44.0 (#79), Qwen3.8 Max: 53.5 (#29)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| LMArena WebDev | 1435 | 1674 |
| SciCode | 45.1% | 53.2% |
| LMArena Coding | 1454 | 1502 |
| DeepSWE | — | 57.5% |
| FrontierSWE | — | 17.8% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Qwen3.8 Max leads
GLM-4.7: 26.5 (#103), Qwen3.8 Max: 45.4 (#14)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| APEX-Agents | — | 63.3% |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
| Vending-Bench 2 | 2,377 | — |
Reasoning Qwen3.8 Max leads
GLM-4.7: 24.3 (#164), Qwen3.8 Max: 54.4 (#26)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| CritPt | 1.7% | 20% |
| Chess Puzzles | 6% | 40% |
| LMArena Hard Prompts | 1443 | 1496 |
| Epoch Capabilities Index | 143.51 | 156.41 |
| SimpleBench | 47.7% | — |
| NYT Connections (extended) | — | 88.3% |
| Mystery Game Puzzles | — | 38% |
| DTBench | — | 92% |
| LMCA | — | 46.2% |
Math Qwen3.8 Max leads
GLM-4.7: 38.6 (#135), Qwen3.8 Max: 73.2 (#20)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 100% |
| ProofBench | 6% | 58% |
| LMArena Math | 1423 | 1499 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Qwen3.8 Max leads
GLM-4.7: 47.0 (#80), Qwen3.8 Max: 61.7 (#27)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 83.3% | 92.7% |
| SimpleQA Verified | 32.2% | 47.3% |
| LMArena Expert | 1424 | 1507 |
| Vectara Hallucination Rate | 11.7% | — |
Multimodal Not comparable
GLM-4.7: —, Qwen3.8 Max: 37.2 (#75)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
GLM-4.7: 52.8 (#79), Qwen3.8 Max: 56.7 (#18)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1417 | 1472 |
| LMArena Chinese | 1495 | 1538 |
| LMArena French | 1432 | 1503 |
| LMArena German | 1424 | 1483 |
| LMArena Japanese | 1439 | 1467 |
| LMArena Korean | 1399 | 1461 |
| LMArena Russian | 1423 | 1481 |
| LMArena Spanish | 1434 | 1492 |
Instruction Following Qwen3.8 Max leads
GLM-4.7: 74.4 (#95), Qwen3.8 Max: 77.6 (#17)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1411 | 1479 |
Long Context Qwen3.8 Max leads
GLM-4.7: 42.8 (#116), Qwen3.8 Max: 45.6 (#31)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1432 | 1489 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference Qwen3.8 Max leads
GLM-4.7: 60.9 (#93), Qwen3.8 Max: 67.1 (#30)
| Benchmark | GLM-4.7 | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1435 | 1483 |
| LMArena Creative Writing | 1401 | 1479 |
| LMArena Multi-Turn | 1446 | 1489 |
| EQ-Bench Creative Writing | 1413 | — |
Frequently asked questions
Is GLM-4.7 better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 42.0 on the Noometry Index. GLM-4.7 costs 3.0× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or Qwen3.8 Max?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is GLM-4.7 or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 205K.
How many benchmarks do GLM-4.7 and Qwen3.8 Max share?
26 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3.8 Max has 39.