# GLM-5.2 vs Mistral Large 3

> GLM-5.2 is the stronger model overall, scoring 50.9 to 38.6 on the Noometry Index. Mistral Large 3 costs 5.7× 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/glm-5-2-vs-mistral-large-3
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Mistral Large 3 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 14.6.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 7.5% for Mistral Large 3.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 50.9 | 38.6 |
| Rank | 38 | 172 |
| Context | 1M | 262K |
| Input $/M | $1.40 | $0.25 |
| Output $/M | $4.40 | $0.75 |
| Weights | Open | Open |

## Coding

- GLM-5.2: 50.9 (#37)
- Mistral Large 3: 34.1 (#230)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena WebDev | 1604 | 1230 |
| LMArena Coding | 1484 | 1447 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| ALE-Bench | 1,047 | — |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- Mistral Large 3: —

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- Mistral Large 3: 14.6 (#314)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 62.6% | 50.9% |
| NYT Connections (extended) | 74.3% | 7.5% |
| LMArena Hard Prompts | 1480 | 1429 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 23% |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.76 | — |

## Math

- GLM-5.2: 55.4 (#35)
- Mistral Large 3: 36.0 (#131)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1482 | 1415 |
| 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% | — |

## Knowledge

- GLM-5.2: 57.1 (#36)
- Mistral Large 3: 35.5 (#173)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Expert | 1484 | 1421 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 14.5% |

## Multimodal

- GLM-5.2: —
- Mistral Large 3: 37.6 (#65)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Vision | — | 1222 |

## Multilingual

- GLM-5.2: 56.0 (#25)
- Mistral Large 3: 52.6 (#79)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1459 | 1412 |
| LMArena Chinese | 1518 | 1448 |
| LMArena French | 1476 | 1456 |
| LMArena German | 1470 | 1438 |
| LMArena Japanese | 1451 | 1399 |
| LMArena Korean | 1445 | 1384 |
| LMArena Russian | 1467 | 1412 |
| LMArena Spanish | 1477 | 1437 |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- Mistral Large 3: 74.1 (#104)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1402 |

## Long Context

- GLM-5.2: 45.3 (#43)
- Mistral Large 3: 43.1 (#102)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1479 | 1413 |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- Mistral Large 3: 60.2 (#100)

| Benchmark | GLM-5.2 | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1470 | 1427 |
| LMArena Creative Writing | 1465 | 1385 |
| EQ-Bench Creative Writing | 1757 | 1412 |
| LMArena Multi-Turn | 1468 | 1429 |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than Mistral Large 3?

GLM-5.2 is the stronger model overall, scoring 50.9 to 38.6 on the Noometry Index. Mistral Large 3 costs 5.7× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.2 or Mistral Large 3?

Mistral Large 3 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or Mistral Large 3 better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 34.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.2 and Mistral Large 3 share?

21 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Mistral Large 3 has 24.