# GLM-4.7-Flash vs Pixtral Large

> GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 32.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-pixtral-large
- Last updated: 2026-10-12
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. GLM-4.7-Flash scores higher in 1 category and Pixtral Large in 1 category; one gap is clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.7-Flash leads 47.4 to 32.9.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GLM-4.7-Flash accepts more context: 200K tokens versus 128K.

## Snapshot

| | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 38.8 | 32.2 |
| Rank | 180 | 259 |
| Context | 200K | 128K |
| Input $/M | $0.06 | $2 |
| Output $/M | $0.40 | $6 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.6 (#135)
- Pixtral Large: —

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| LMArena Coding | 1383 | — |

## Reasoning

- GLM-4.7-Flash: 20.9 (#229)
- Pixtral Large: 21.7 (#218)

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| Chess Puzzles | 0% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1356 | — |

## Math

- GLM-4.7-Flash: 36.1 (#173)
- Pixtral Large: —

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| LMArena Math | 1355 | — |

## Knowledge

- GLM-4.7-Flash: 35.5 (#184)
- Pixtral Large: —

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
| LMArena Expert | 1357 | — |

## Multimodal

- GLM-4.7-Flash: —
- Pixtral Large: 30.6 (#111)

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |

## Multilingual

- GLM-4.7-Flash: 46.5 (#158)
- Pixtral Large: —

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1330 | — |
| LMArena Chinese | 1403 | — |
| LMArena French | 1332 | — |
| LMArena German | 1337 | — |
| LMArena Korean | 1283 | — |
| LMArena Russian | 1332 | — |
| LMArena Spanish | 1350 | — |

## Instruction Following

- GLM-4.7-Flash: 70.1 (#167)
- Pixtral Large: —

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1327 | — |

## Long Context

- GLM-4.7-Flash: 40.9 (#148)
- Pixtral Large: —

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1345 | — |

## Writing & Preference

- GLM-4.7-Flash: 47.4 (#210)
- Pixtral Large: 32.9 (#278)

| Benchmark | GLM-4.7-Flash | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1125 | 988 |
| LMArena Text | 1351 | — |
| LMArena Creative Writing | 1297 | — |
| LMArena Multi-Turn | 1342 | — |

## FAQ

### Is GLM-4.7-Flash better than Pixtral Large?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 32.2 on the Noometry Index.

### Which is cheaper, GLM-4.7-Flash or Pixtral Large?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Pixtral Large lists at $2 and $6.

### Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 128K.

### How many benchmarks do GLM-4.7-Flash and Pixtral Large share?

1 benchmark has published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Pixtral Large has 3.