Model comparison
GLM-4.7-Flash vs Qwen3 Coder Next
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 34.3 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in coding, where GLM-4.7-Flash leads 40.6 to 36.3.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.12 / $0.80 for Qwen3 Coder Next.
- Qwen3 Coder Next accepts more context: 262K tokens versus 200K.
Side by side
| GLM-4.7-Flash | Qwen3 Coder Next | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.8 | 34.3 |
| Released | 2026-01-19 | 2026-02-02 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.06 | $0.12 |
| Output $ / M tokens | $0.40 | $0.80 |
| Results tracked | 21 | 3 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Qwen3 Coder Next: 36.3 (#210)
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| SciCode | — | 32.3% |
| WeirdML | — | 34.4% |
| LMArena Coding | 1383 | — |
Reasoning Qwen3 Coder Next leads
GLM-4.7-Flash: 20.9 (#229), Qwen3 Coder Next: 22.4 (#196)
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LMArena Hard Prompts | 1356 | — |
Math Not comparable
GLM-4.7-Flash: 36.1 (#173), Qwen3 Coder Next: —
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| LMArena Math | 1355 | — |
Knowledge Not comparable
GLM-4.7-Flash: 35.5 (#184), Qwen3 Coder Next: —
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
| LMArena Expert | 1357 | — |
Multilingual Not comparable
GLM-4.7-Flash: 46.5 (#158), Qwen3 Coder Next: —
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| LMArena Non-English | 1330 | — |
| LMArena Chinese | 1403 | — |
| LMArena French | 1332 | — |
| LMArena German | 1337 | — |
| LMArena Korean | 1283 | — |
| LMArena Russian | 1332 | — |
| LMArena Spanish | 1350 | — |
Instruction Following Not comparable
GLM-4.7-Flash: 70.1 (#167), Qwen3 Coder Next: —
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| LMArena Instruction Following | 1327 | — |
Long Context Not comparable
GLM-4.7-Flash: 40.9 (#148), Qwen3 Coder Next: —
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| LMArena Longer Query | 1345 | — |
Writing & Preference Not comparable
GLM-4.7-Flash: 47.4 (#210), Qwen3 Coder Next: —
| Benchmark | GLM-4.7-Flash | Qwen3 Coder Next |
|---|---|---|
| LMArena Text | 1351 | — |
| LMArena Creative Writing | 1297 | — |
| EQ-Bench Creative Writing | 1125 | — |
| LMArena Multi-Turn | 1342 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen3 Coder Next?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 34.3 on the Noometry Index.
Which is cheaper, GLM-4.7-Flash or Qwen3 Coder Next?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3 Coder Next lists at $0.12 and $0.80.
Is GLM-4.7-Flash or Qwen3 Coder Next better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 36.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3 Coder Next does, with 262K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Qwen3 Coder Next share?
0 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3 Coder Next has 3.