# GLM-5.3-Flash vs Mistral 7B

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 22.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-mistral-7b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Mistral 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 7.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 0.3% for Mistral 7B.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.25 / $0.25 for Mistral 7B.
- GLM-5.3-Flash accepts more context: 1M tokens versus 8K.

## Snapshot

| | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 49.6 | 22.9 |
| Rank | 45 | 346 |
| Context | 1M | 8K |
| Input $/M | $0.15 | $0.25 |
| Output $/M | $0.50 | $0.25 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Mistral 7B: 25.9 (#321)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| LMArena Coding | 1506 | 1082 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Mistral 7B: —

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Mistral 7B: 12.6 (#331)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1490 | 1067 |
| Epoch Capabilities Index | 151.9 | 111.97 |
| CritPt | 15.4% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 42.5% |
| Surface Evolver Bench | 52.5% | — |
| Adversarial NLI | — | 47.1% |
| Bench to the Future 3 | 0.15 | — |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Mistral 7B: 7.6 (#319)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 0.3% |
| LMArena Math | 1499 | 1085 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Mistral 7B: 7.3 (#306)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| GPQA Diamond | 90.2% | 15.2% |
| LMArena Expert | 1512 | 1036 |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Mistral 7B: —

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Mistral 7B: 26.0 (#278)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1462 | 1012 |
| LMArena Chinese | 1529 | 1009 |
| LMArena French | 1495 | 1036 |
| LMArena German | 1457 | 987 |
| LMArena Japanese | 1420 | 878 |
| LMArena Russian | 1472 | 1018 |
| LMArena Spanish | 1471 | 1026 |
| LMArena Korean | 1443 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Mistral 7B: 54.5 (#275)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1060 |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Mistral 7B: 32.2 (#266)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1479 | 1060 |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Mistral 7B: 30.9 (#281)

| Benchmark | GLM-5.3-Flash | Mistral 7B |
|---|---|---|
| LMArena Text | 1471 | 1090 |
| LMArena Creative Writing | 1440 | 1068 |
| LMArena Multi-Turn | 1471 | 1062 |

## FAQ

### Is GLM-5.3-Flash better than Mistral 7B?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 22.9 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or Mistral 7B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Mistral 7B lists at $0.25 and $0.25.

### Is GLM-5.3-Flash or Mistral 7B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 25.9 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 8K.

### How many benchmarks do GLM-5.3-Flash and Mistral 7B share?

20 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Mistral 7B has 37.