# Llama 3.1-70B vs Mistral Small

> Mistral Small is the stronger model overall, scoring 33.1 to 29.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/llama-3-1-70b-vs-mistral-small
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. Llama 3.1-70B scores higher in 1 category and Mistral Small in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Mistral Small leads 52.6 to 35.6.
- The biggest single-benchmark swing is DTBench: 60% for Llama 3.1-70B and 70.9% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- Mistral Small accepts more context: 256K tokens versus 128K.

## Snapshot

| | Llama 3.1-70B | Mistral Small |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 29.4 | 33.1 |
| Rank | 299 | 238 |
| Context | 128K | 256K |
| Input $/M | $0.40 | $0.15 |
| Output $/M | $0.40 | $0.60 |
| Weights | Open | Open |

## Coding

- Llama 3.1-70B: 29.8 (#292)
- Mistral Small: 33.5 (#242)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| BigCodeBench Instruct | 46.1% | 36.1% |
| LMArena Coding | 1260 | 1363 |
| BigCodeBench Complete | 54.8% | 46.6% |
| SciCode | — | 26.5% |
| WeirdML | 9% | — |
| LiveBench Coding | — | 36.2% |
| ALE-Bench | — | 497.62 |

## Agentic & Tool Use

- Llama 3.1-70B: 24.9 (#104)
- Mistral Small: 27.9 (#87)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.1% |
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |

## Reasoning

- Llama 3.1-70B: 20.8 (#215)
- Mistral Small: 19.3 (#238)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1336 |
| DTBench | 60% | 70.9% |
| LMCA | 14.8% | 20.6% |
| Kagi LLM Benchmark | — | 37.8% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 44.8% |
| LiveBench Data Analysis | — | 53.7% |
| Epoch Capabilities Index | 125.91 | — |
| LiveBench | — | 44% |

## Math

- Llama 3.1-70B: 12.7 (#298)
- Mistral Small: 15.4 (#287)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | 5.8% |
| LMArena Math | 1252 | 1341 |
| MATH Level 5 | 36.7% | 46.8% |
| Omni-MATH | 21% | — |
| LiveBench Math | — | 39.9% |

## Knowledge

- Llama 3.1-70B: 24.1 (#264)
- Mistral Small: 30.9 (#214)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| GPQA Diamond | 44.2% | 47.5% |
| LMArena Expert | 1209 | 1293 |
| MMLU | 80.1% | 68.7% |
| MMLU-Pro | 65.3% | — |
| Vectara Hallucination Rate | — | 5.1% |
| GPQA (HELM) | 42.6% | — |

## Multimodal

- Llama 3.1-70B: —
- Mistral Small: 32.9 (#91)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |

## Multilingual

- Llama 3.1-70B: 38.9 (#220)
- Mistral Small: 45.7 (#164)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| LMArena Non-English | 1219 | 1315 |
| LMArena Chinese | 1215 | 1342 |
| LMArena French | 1260 | 1337 |
| LMArena German | 1222 | 1341 |
| LMArena Japanese | 1132 | 1275 |
| LMArena Korean | 1140 | 1260 |
| LMArena Russian | 1234 | 1324 |
| LMArena Spanish | 1253 | 1346 |

## Instruction Following

- Llama 3.1-70B: 65.5 (#218)
- Mistral Small: 66.6 (#204)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1231 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
| IFEval | 82.1% | — |

## Long Context

- Llama 3.1-70B: 37.6 (#208)
- Mistral Small: 40.4 (#151)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1241 | 1327 |

## Writing & Preference

- Llama 3.1-70B: 35.6 (#262)
- Mistral Small: 52.6 (#165)

| Benchmark | Llama 3.1-70B | Mistral Small |
|---|---|---|
| LMArena Text | 1261 | 1338 |
| LMArena Creative Writing | 1232 | 1304 |
| LMArena Multi-Turn | 1256 | 1344 |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
| LiveBench Language | — | 30.5% |

## FAQ

### Is Llama 3.1-70B better than Mistral Small?

Mistral Small is the stronger model overall, scoring 33.1 to 29.4 on the Noometry Index.

### Which is cheaper, Llama 3.1-70B or Mistral Small?

Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.

### Is Llama 3.1-70B or Mistral Small better for coding?

Mistral Small scores higher on coding benchmarks: 33.5 versus 29.8 in the Noometry coding category.

### Which has the bigger context window?

Mistral Small does, with 256K tokens against 128K.

### How many benchmarks do Llama 3.1-70B and Mistral Small share?

25 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Mistral Small has 39.