Model comparison
GLM-4.7-Flash vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 9.3× less per token, which makes it the better buy when Qwen3.5 397B-A17B's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and Qwen3.5 397B-A17B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5 397B-A17B leads 53.3 to 35.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 88.9% for Qwen3.5 397B-A17B.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.60 / $3.60 for Qwen3.5 397B-A17B.
- Qwen3.5 397B-A17B accepts more context: 262K tokens versus 200K.
Side by side
| GLM-4.7-Flash | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.8 | 46.0 |
| Released | 2026-01-19 | 2026-02-01 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.06 | $0.60 |
| Output $ / M tokens | $0.40 | $3.60 |
| Results tracked | 21 | 36 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 40.6 (#135), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Coding | 1383 | 1465 |
| LMArena WebDev | — | 1400 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| APEX-Agents | — | 24.9% |
| τ²-bench Airline | — | 81.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 84.4% |
| τ²-bench Telecom | — | 97.8% |
Reasoning Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 20.9 (#229), Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| Chess Puzzles | 0% | 13% |
| LMArena Hard Prompts | 1356 | 1448 |
| Kagi LLM Benchmark | — | 73.7% |
| NYT Connections (extended) | — | 58.9% |
| Thematic Generalization | — | 65.1% |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 87.5% |
| LMCA | — | 37.9% |
| Epoch Capabilities Index | — | 146.65 |
Math Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 36.1 (#173), Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 88.9% |
| LMArena Math | 1355 | 1454 |
| FrontierMath (Tiers 1-3) | — | 31.2% |
Knowledge Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 35.5 (#184), Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | 60.5% | 86.4% |
| LMArena Expert | 1357 | 1462 |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GLM-4.7-Flash: —, Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | — | 1263 |
Multilingual Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 46.5 (#158), Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | 1330 | 1430 |
| LMArena Chinese | 1403 | 1500 |
| LMArena French | 1332 | 1461 |
| LMArena German | 1337 | 1447 |
| LMArena Korean | 1283 | 1384 |
| LMArena Russian | 1332 | 1429 |
| LMArena Spanish | 1350 | 1441 |
| LMArena Japanese | — | 1426 |
Instruction Following Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 70.1 (#167), Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1424 |
Long Context Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 40.9 (#148), Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | 1345 | 1442 |
Writing & Preference Qwen3.5 397B-A17B leads
GLM-4.7-Flash: 47.4 (#210), Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | GLM-4.7-Flash | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | 1351 | 1438 |
| LMArena Creative Writing | 1297 | 1401 |
| EQ-Bench Creative Writing | 1125 | 1478 |
| LMArena Multi-Turn | 1342 | 1446 |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 9.3× less per token, which makes it the better buy when Qwen3.5 397B-A17B's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Qwen3.5 397B-A17B?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3.5 397B-A17B lists at $0.60 and $3.60.
Is GLM-4.7-Flash or Qwen3.5 397B-A17B better for coding?
Qwen3.5 397B-A17B scores higher on coding benchmarks: 42.0 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 397B-A17B does, with 262K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Qwen3.5 397B-A17B share?
20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3.5 397B-A17B has 36.