# Codestral vs Llama-3.3-70B-Instruct

> Codestral and Llama-3.3-70B-Instruct score almost the same on the Noometry Index (30.6 vs 30.6), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/codestral-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. Codestral scores higher in 1 category and Llama-3.3-70B-Instruct in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Codestral leads 19.8 to 14.1.
- The biggest single-benchmark swing is BigCodeBench Instruct: 41.8% for Codestral and 46.9% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- Codestral accepts more context: 256K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | Mistral AI | Meta |
| Noometry Index | 30.6 | 30.6 |
| Rank | 290 | 291 |
| Context | 256K | 128K |
| Input $/M | $0.30 | $0.10 |
| Output $/M | $0.90 | $0.32 |
| Weights | Proprietary | Open |

## Coding

- Codestral: 27.3 (#321)
- Llama-3.3-70B-Instruct: 31.0 (#290)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| BigCodeBench Instruct | 41.8% | 46.9% |
| BigCodeBench Complete | 52.5% | 57.5% |
| Aider Polyglot | 11.1% | — |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| LiveBench Coding | — | 36.6% |
| LMArena Coding | — | 1268 |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |

## Agentic & Tool Use

- Codestral: —
- Llama-3.3-70B-Instruct: 25.8 (#105)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |

## Reasoning

- Codestral: 19.8 (#251)
- Llama-3.3-70B-Instruct: 14.1 (#327)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 32.5% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.33 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |

## Math

- Codestral: —
- Llama-3.3-70B-Instruct: 15.3 (#298)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| LMArena Math | — | 1267 |
| MATH Level 5 | — | 41.6% |

## Knowledge

- Codestral: —
- Llama-3.3-70B-Instruct: 30.6 (#226)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| LMArena Expert | — | 1225 |
| MMLU | — | 86.3% |

## Multilingual

- Codestral: —
- Llama-3.3-70B-Instruct: 39.9 (#220)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | — | 1236 |
| LMArena Chinese | — | 1217 |
| LMArena French | — | 1281 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1150 |
| LMArena Korean | — | 1143 |
| LMArena Russian | — | 1252 |
| LMArena Spanish | — | 1270 |

## Instruction Following

- Codestral: —
- Llama-3.3-70B-Instruct: 71.1 (#157)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | — | 82.7% |
| LMArena Instruction Following | — | 1242 |

## Long Context

- Codestral: —
- Llama-3.3-70B-Instruct: 26.4 (#295)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | — | 33.3% |
| LMArena Longer Query | — | 1256 |

## Writing & Preference

- Codestral: —
- Llama-3.3-70B-Instruct: 47.6 (#207)

| Benchmark | Codestral | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | — | 1274 |
| LMArena Creative Writing | — | 1250 |
| LMArena Multi-Turn | — | 1280 |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Codestral better than Llama-3.3-70B-Instruct?

Codestral and Llama-3.3-70B-Instruct score almost the same on the Noometry Index (30.6 vs 30.6), so choose on price, context window or the category you care about most.

### Which is cheaper, Codestral or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Codestral lists at $0.30 and $0.90.

### Is Codestral or Llama-3.3-70B-Instruct better for coding?

Llama-3.3-70B-Instruct scores higher on coding benchmarks: 31.0 versus 27.3 in the Noometry coding category.

### Which has the bigger context window?

Codestral does, with 256K tokens against 128K.

### How many benchmarks do Codestral and Llama-3.3-70B-Instruct share?

2 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Llama-3.3-70B-Instruct has 43.