Model comparison
Codestral vs GLM-4.5V
GLM-4.5V is the stronger model overall, scoring 39.2 to 30.1 on the Noometry Index. Codestral costs 2.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. Codestral scores higher in 0 categories and GLM-4.5V in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-4.5V leads 38.9 to 26.9.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 32.5% for Codestral and 59.8% for GLM-4.5V.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Codestral accepts more context: 256K tokens versus 64K.
Side by side
| Codestral | GLM-4.5V | |
|---|---|---|
| Provider | Mistral AI | Z.ai (Zhipu) |
| Noometry Index | 30.1 | 39.2 |
| Released | 2024-05-29 | 2025-08-11 |
| Weights | Open | Open |
| Context window | 256K | 64K |
| Max output | 4K | 16K |
| Input $ / M tokens | $0.30 | $0.60 |
| Output $ / M tokens | $0.90 | $1.80 |
| Results tracked | 7 | 15 |
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Category by category
Coding GLM-4.5V leads
Codestral: 26.9 (#316), GLM-4.5V: 38.9 (#152)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| Aider Polyglot | 11.1% | — |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1347 |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Reasoning GLM-4.5V leads
Codestral: 19.1 (#244), GLM-4.5V: 26.5 (#118)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 59.8% |
| LMArena Hard Prompts | — | 1335 |
Math Not comparable
Codestral: —, GLM-4.5V: 34.7 (#157)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Math | — | 1353 |
Knowledge Not comparable
Codestral: —, GLM-4.5V: 36.8 (#155)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Expert | — | 1353 |
Multimodal Not comparable
Codestral: —, GLM-4.5V: 33.6 (#86)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Vision | — | 1154 |
Multilingual Not comparable
Codestral: —, GLM-4.5V: 44.9 (#172)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Non-English | — | 1304 |
| LMArena Chinese | — | 1338 |
| LMArena Russian | — | 1299 |
| LMArena Spanish | — | 1335 |
Instruction Following Not comparable
Codestral: —, GLM-4.5V: 69.4 (#170)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | — | 1311 |
Long Context Not comparable
Codestral: —, GLM-4.5V: 39.6 (#166)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | — | 1305 |
Writing & Preference Not comparable
Codestral: —, GLM-4.5V: 52.7 (#162)
| Benchmark | Codestral | GLM-4.5V |
|---|---|---|
| LMArena Text | — | 1333 |
| LMArena Creative Writing | — | 1295 |
| LMArena Multi-Turn | — | 1333 |
Frequently asked questions
Is Codestral better than GLM-4.5V?
GLM-4.5V is the stronger model overall, scoring 39.2 to 30.1 on the Noometry Index. Codestral costs 2.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Which is cheaper, Codestral or GLM-4.5V?
Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is Codestral or GLM-4.5V better for coding?
GLM-4.5V scores higher on coding benchmarks: 38.9 versus 26.9 in the Noometry coding category.
Which has the bigger context window?
Codestral does, with 256K tokens against 64K.
How many benchmarks do Codestral and GLM-4.5V share?
1 benchmark has published results for both models. Codestral has 7 scored results on Noometry and GLM-4.5V has 15.