Model comparison
Codestral vs GLM-4.7-Flash
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 30.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AISummary
- The widest gap is in coding, where GLM-4.7-Flash leads 40.6 to 27.3.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- Codestral accepts more context: 256K tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| Codestral | GLM-4.7-Flash | |
|---|---|---|
| Provider | Mistral AI | Z.ai (Zhipu) |
| Noometry Index | 30.6 | 38.8 |
| Released | 2024-05-29 | 2026-01-19 |
| Weights | Proprietary | Open |
| Context window | 256K | 200K |
| Max output | 8K | 131K |
| Input $ / M tokens | $0.30 | $0.06 |
| Output $ / M tokens | $0.90 | $0.40 |
| Results tracked | 7 | 21 |
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Category by category
Coding GLM-4.7-Flash leads
Codestral: 27.3 (#321), GLM-4.7-Flash: 40.6 (#135)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| Aider Polyglot | 11.1% | — |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1383 |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Reasoning GLM-4.7-Flash leads
Codestral: 19.8 (#251), GLM-4.7-Flash: 20.9 (#229)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1356 |
Math Not comparable
Codestral: —, GLM-4.7-Flash: 36.1 (#173)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 58.3% |
| LMArena Math | — | 1355 |
Knowledge Not comparable
Codestral: —, GLM-4.7-Flash: 35.5 (#184)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| GPQA Diamond | — | 60.5% |
| Vectara Hallucination Rate | — | 9.3% |
| LMArena Expert | — | 1357 |
Multilingual Not comparable
Codestral: —, GLM-4.7-Flash: 46.5 (#158)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| 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
Codestral: —, GLM-4.7-Flash: 70.1 (#167)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| LMArena Instruction Following | — | 1327 |
Long Context Not comparable
Codestral: —, GLM-4.7-Flash: 40.9 (#148)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| LMArena Longer Query | — | 1345 |
Writing & Preference Not comparable
Codestral: —, GLM-4.7-Flash: 47.4 (#210)
| Benchmark | Codestral | GLM-4.7-Flash |
|---|---|---|
| LMArena Text | — | 1351 |
| LMArena Creative Writing | — | 1297 |
| EQ-Bench Creative Writing | — | 1125 |
| LMArena Multi-Turn | — | 1342 |
Frequently asked questions
Is Codestral better than GLM-4.7-Flash?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 30.6 on the Noometry Index.
Which is cheaper, Codestral or GLM-4.7-Flash?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Codestral lists at $0.30 and $0.90.
Is Codestral or GLM-4.7-Flash better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 27.3 in the Noometry coding category.
Which has the bigger context window?
Codestral does, with 256K tokens against 200K.
How many benchmarks do Codestral and GLM-4.7-Flash share?
0 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and GLM-4.7-Flash has 21.