# GLM-5.3-Flash vs Mistral Nemo

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 25.9 on the Noometry Index. Mistral Nemo costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-mistral-nemo
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. GLM-5.3-Flash scores higher in 5 categories and Mistral Nemo in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 12.3.
- The biggest single-benchmark swing is GPQA Diamond: 90.2% for GLM-5.3-Flash and 29.9% for Mistral Nemo.
- Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 49.6 | 25.9 |
| Rank | 45 | 332 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.15 |
| Output $/M | $0.50 | $0.15 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Mistral Nemo: —

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| LMArena Coding | 1506 | — |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Mistral Nemo: 23.3 (#118)

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 27.6% |
| BALROG | — | 17.6% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Mistral Nemo: 19.9 (#230)

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| Epoch Capabilities Index | 151.9 | 118.63 |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| LMArena Hard Prompts | 1490 | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 48.6% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| PIQA | — | 83.5% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Mistral Nemo: 23.5 (#263)

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| LMArena Math | 1499 | — |
| MATH Level 5 | — | 10.8% |
| GSM8K | — | 84.2% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Mistral Nemo: 12.3 (#293)

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| GPQA Diamond | 90.2% | 29.9% |
| LMArena Expert | 1512 | — |
| BoolQ | — | 82.5% |

## Multimodal

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

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Mistral Nemo: —

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1529 | — |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Russian | 1472 | — |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Mistral Nemo: —

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| LMArena Instruction Following | 1480 | — |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Mistral Nemo: —

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| LMArena Longer Query | 1479 | — |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Mistral Nemo: 28.6 (#291)

| Benchmark | GLM-5.3-Flash | Mistral Nemo |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1440 | — |
| EQ-Bench Creative Writing | — | 881 |
| LMArena Multi-Turn | 1471 | — |

## FAQ

### Is GLM-5.3-Flash better than Mistral Nemo?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 25.9 on the Noometry Index. Mistral Nemo costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

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

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

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 128K.

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

2 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Mistral Nemo has 10.