# Llama 3.2 1B vs Mistral Small

> Mistral Small is the stronger model overall, scoring 33.4 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.7× less per token, which makes it the better buy when Mistral Small's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-2-1b-vs-mistral-small
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and Mistral Small in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Mistral Small leads 52.5 to 21.3.
- The biggest single-benchmark swing is BigCodeBench Complete: 11.3% for Llama 3.2 1B and 46.6% for Mistral Small.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.15 / $0.60 for Mistral Small.
- Mistral Small accepts more context: 262K tokens versus 60K.

## Snapshot

| | Llama 3.2 1B | Mistral Small |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 20.1 | 33.4 |
| Rank | 354 | 243 |
| Context | 60K | 262K |
| Input $/M | $0.027 | $0.15 |
| Output $/M | $0.20 | $0.60 |
| Weights | Open | Open |

## Coding

- Llama 3.2 1B: 21.1 (#338)
- Mistral Small: 34.0 (#247)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| BigCodeBench Instruct | 8.2% | 36.1% |
| LMArena Coding | 1070 | 1362 |
| BigCodeBench Complete | 11.3% | 46.6% |
| SciCode | — | 26.5% |
| LiveBench Coding | — | 36.2% |
| ALE-Bench | — | 497.62 |

## Agentic & Tool Use

- Llama 3.2 1B: 14.6 (#150)
- Mistral Small: 28.1 (#93)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | 37.1% |
| BALROG | 6.6% | — |

## Reasoning

- Llama 3.2 1B: 16.2 (#308)
- Mistral Small: 19.8 (#250)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1335 |
| Kagi LLM Benchmark | — | 37.8% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| Epoch Capabilities Index | 101.99 | — |
| LiveBench | — | 44% |

## Math

- Llama 3.2 1B: 10.4 (#313)
- Mistral Small: 16.4 (#293)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | 5.8% |
| LMArena Math | 1086 | 1341 |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |

## Knowledge

- Llama 3.2 1B: 7.2 (#312)
- Mistral Small: 31.0 (#222)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| GPQA Diamond | 23.9% | 47.5% |
| LMArena Expert | 1007 | 1291 |
| Vectara Hallucination Rate | — | 5.1% |
| MMLU | — | 68.7% |

## Multimodal

- Llama 3.2 1B: —
- Mistral Small: 33.5 (#96)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |

## Multilingual

- Llama 3.2 1B: 23.8 (#292)
- Mistral Small: 45.5 (#169)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| LMArena Non-English | 973 | 1315 |
| LMArena Chinese | 959 | 1340 |
| LMArena German | 1014 | 1340 |
| LMArena Russian | 941 | 1324 |
| LMArena French | — | 1337 |
| LMArena Japanese | — | 1275 |
| LMArena Korean | — | 1259 |
| LMArena Spanish | — | 1346 |

## Instruction Following

- Llama 3.2 1B: 52.4 (#290)
- Mistral Small: 66.4 (#209)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1031 | 1310 |
| LiveBench Instruction Following | — | 63.7% |

## Long Context

- Llama 3.2 1B: 31.9 (#274)
- Mistral Small: 40.4 (#156)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1050 | 1327 |

## Writing & Preference

- Llama 3.2 1B: 21.3 (#310)
- Mistral Small: 52.5 (#171)

| Benchmark | Llama 3.2 1B | Mistral Small |
|---|---|---|
| LMArena Text | 1055 | 1338 |
| LMArena Creative Writing | 1033 | 1305 |
| LMArena Multi-Turn | 1030 | 1344 |
| EQ-Bench Creative Writing | 200 | — |
| LiveBench Language | — | 30.5% |

## FAQ

### Is Llama 3.2 1B better than Mistral Small?

Mistral Small is the stronger model overall, scoring 33.4 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.7× less per token, which makes it the better buy when Mistral Small's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.2 1B or Mistral Small?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Mistral Small lists at $0.15 and $0.60.

### Is Llama 3.2 1B or Mistral Small better for coding?

Mistral Small scores higher on coding benchmarks: 34.0 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

Mistral Small does, with 262K tokens against 60K.

### How many benchmarks do Llama 3.2 1B and Mistral Small share?

18 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Mistral Small has 39.