# GLM-5.1 vs Mistral Large

> GLM-5.1 is the stronger model overall, scoring 47.6 to 31.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-1-vs-mistral-large
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and Mistral Large in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.1 leads 49.0 to 17.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for GLM-5.1 and 8.5% for Mistral Large.
- GLM-5.1 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $6 for Mistral Large.
- GLM-5.1 accepts more context: 200K tokens versus 131K.

## Snapshot

| | GLM-5.1 | Mistral Large |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 47.6 | 31.6 |
| Rank | 51 | 258 |
| Context | 200K | 131K |
| Input $/M | $1.40 | $2 |
| Output $/M | $4.40 | $6 |
| Weights | Open | Open |

## Coding

- GLM-5.1: 48.5 (#52)
- Mistral Large: 33.8 (#240)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| SciCode | 43.8% | 36.2% |
| LMArena Coding | 1486 | 1277 |
| ALE-Bench | 887.1 | 264.7 |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1509 | — |
| WeirdML | 57.1% | — |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |

## Agentic & Tool Use

- GLM-5.1: 24.8 (#106)
- Mistral Large: 28.4 (#83)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| APEX-Agents | 40.9% | — |
| Berkeley Function Calling Leaderboard | — | 38.4% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |

## Reasoning

- GLM-5.1: 38.2 (#57)
- Mistral Large: 15.4 (#305)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| SimpleBench | 55.1% | 22.5% |
| CritPt | 4.6% | 0% |
| LMArena Hard Prompts | 1472 | 1257 |
| Epoch Capabilities Index | 149.86 | 128.52 |
| NYT Connections (extended) | 77.7% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| LiveBench Reasoning | — | 43.5% |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |

## Math

- GLM-5.1: 49.0 (#48)
- Mistral Large: 17.1 (#285)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 8.5% |
| LMArena Math | 1472 | 1262 |
| FrontierMath (Feb 2025 set) | 33.4% | 0.3% |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath Tier 4 (v1) | 12.5% | — |

## Knowledge

- GLM-5.1: 55.0 (#45)
- Mistral Large: 30.0 (#224)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| GPQA Diamond | 89.9% | 51.3% |
| LMArena Expert | 1477 | 1232 |
| SimpleQA Verified | 34% | — |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |

## Multilingual

- GLM-5.1: 55.2 (#34)
- Mistral Large: 40.1 (#214)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| LMArena Non-English | 1448 | 1237 |
| LMArena Chinese | 1516 | 1240 |
| LMArena French | 1478 | 1324 |
| LMArena German | 1466 | 1254 |
| LMArena Japanese | 1436 | 1187 |
| LMArena Korean | 1423 | 1202 |
| LMArena Russian | 1453 | 1257 |
| LMArena Spanish | 1467 | 1268 |

## Instruction Following

- GLM-5.1: 76.5 (#41)
- Mistral Large: 68.1 (#186)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1451 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |

## Long Context

- GLM-5.1: 44.9 (#51)
- Mistral Large: 38.3 (#194)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1467 | 1262 |

## Writing & Preference

- GLM-5.1: 67.2 (#29)
- Mistral Large: 40.9 (#237)

| Benchmark | GLM-5.1 | Mistral Large |
|---|---|---|
| LMArena Text | 1462 | 1266 |
| LMArena Creative Writing | 1454 | 1243 |
| EQ-Bench Creative Writing | 1592 | 985 |
| LMArena Multi-Turn | 1473 | 1260 |
| Short-Story Creative Writing | — | 69% |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |

## FAQ

### Is GLM-5.1 better than Mistral Large?

GLM-5.1 is the stronger model overall, scoring 47.6 to 31.6 on the Noometry Index.

### Which is cheaper, GLM-5.1 or Mistral Large?

GLM-5.1 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Mistral Large lists at $2 and $6.

### Is GLM-5.1 or Mistral Large better for coding?

GLM-5.1 scores higher on coding benchmarks: 48.5 versus 33.8 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.1 does, with 200K tokens against 131K.

### How many benchmarks do GLM-5.1 and Mistral Large share?

26 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Mistral Large has 51.