# Codestral vs GLM-5.2

> GLM-5.2 is the stronger model overall, scoring 51.1 to 30.6 on the Noometry Index. Codestral costs 4.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/codestral-vs-glm-5-2
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. Codestral scores higher in 0 categories and GLM-5.2 in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.2 leads 51.3 to 27.3.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 32.5% for Codestral and 62.6% for GLM-5.2.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 256K.
- GLM-5.2 has downloadable open weights; the other is API-only.

## Snapshot

| | Codestral | GLM-5.2 |
|---|---|---|
| Provider | Mistral AI | Z.ai (Zhipu) |
| Noometry Index | 30.6 | 51.1 |
| Rank | 290 | 44 |
| Context | 256K | 1M |
| Input $/M | $0.30 | $1.40 |
| Output $/M | $0.90 | $4.40 |
| Weights | Proprietary | Open |

## Coding

- Codestral: 27.3 (#321)
- GLM-5.2: 51.3 (#41)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| ALE-Bench | 137.78 | 1,047 |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| Aider Polyglot | 11.1% | — |
| LMArena WebDev | — | 1603 |
| SciCode | — | 50.5% |
| WeirdML | — | 70.1% |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1485 |
| BigCodeBench Complete | 52.5% | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |

## Agentic & Tool Use

- Codestral: —
- GLM-5.2: 32.4 (#63)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |

## Reasoning

- Codestral: 19.8 (#251)
- GLM-5.2: 42.3 (#52)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 62.6% |
| ARC-AGI-2 | — | 22.8% |
| SimpleBench | — | 58.8% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| CritPt | — | 20.9% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| LMArena Hard Prompts | — | 1480 |
| Mystery Game Puzzles | — | 19% |
| DTBench | — | 93.6% |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| Epoch Capabilities Index | — | 151.78 |

## Math

- Codestral: —
- GLM-5.2: 55.7 (#43)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| OTIS Mock AIME 2024-2025 | — | 86.4% |
| ProofBench | — | 35% |
| LMArena Math | — | 1482 |

## Knowledge

- Codestral: —
- GLM-5.2: 57.1 (#40)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| GPQA Diamond | — | 91.9% |
| SimpleQA Verified | — | 34.2% |
| LMArena Expert | — | 1486 |

## Multilingual

- Codestral: —
- GLM-5.2: 55.8 (#26)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| LMArena Non-English | — | 1459 |
| LMArena Chinese | — | 1519 |
| LMArena French | — | 1479 |
| LMArena German | — | 1468 |
| LMArena Japanese | — | 1451 |
| LMArena Korean | — | 1445 |
| LMArena Russian | — | 1466 |
| LMArena Spanish | — | 1477 |

## Instruction Following

- Codestral: —
- GLM-5.2: 76.9 (#34)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | — | 1465 |

## Long Context

- Codestral: —
- GLM-5.2: 45.3 (#43)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | — | 1479 |

## Writing & Preference

- Codestral: —
- GLM-5.2: 70.4 (#21)

| Benchmark | Codestral | GLM-5.2 |
|---|---|---|
| LMArena Text | — | 1470 |
| LMArena Creative Writing | — | 1462 |
| EQ-Bench Creative Writing | — | 1757 |
| EQ-Bench 4 | — | 1222 |
| LMArena Multi-Turn | — | 1469 |

## FAQ

### Is Codestral better than GLM-5.2?

GLM-5.2 is the stronger model overall, scoring 51.1 to 30.6 on the Noometry Index. Codestral costs 4.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, Codestral or GLM-5.2?

Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is Codestral or GLM-5.2 better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 27.3 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 256K.

### How many benchmarks do Codestral and GLM-5.2 share?

2 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and GLM-5.2 has 51.