# Codestral vs Llama 3.2 1B

> Codestral is the stronger model overall, scoring 30.6 to 20.1 on the Noometry Index. Llama 3.2 1B costs 6.4× less per token, which makes it the better buy when Codestral's lead doesn't matter for your workload.

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

## Summary

- They share 2 benchmarks with published results for both. Codestral scores higher in 2 categories and Llama 3.2 1B in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where Codestral leads 27.3 to 21.1.
- The biggest single-benchmark swing is BigCodeBench Complete: 52.5% for Codestral and 11.3% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- Codestral accepts more context: 256K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.

## Snapshot

| | Codestral | Llama 3.2 1B |
|---|---|---|
| Provider | Mistral AI | Meta |
| Noometry Index | 30.6 | 20.1 |
| Rank | 290 | 354 |
| Context | 256K | 60K |
| Input $/M | $0.30 | $0.027 |
| Output $/M | $0.90 | $0.20 |
| Weights | Proprietary | Open |

## Coding

- Codestral: 27.3 (#321)
- Llama 3.2 1B: 21.1 (#338)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| BigCodeBench Instruct | 41.8% | 8.2% |
| BigCodeBench Complete | 52.5% | 11.3% |
| Aider Polyglot | 11.1% | — |
| LMArena Coding | — | 1070 |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |

## Agentic & Tool Use

- Codestral: —
- Llama 3.2 1B: 14.6 (#150)

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

## Reasoning

- Codestral: 19.8 (#251)
- Llama 3.2 1B: 16.2 (#308)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1044 |
| Epoch Capabilities Index | — | 101.99 |

## Math

- Codestral: —
- Llama 3.2 1B: 10.4 (#313)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 0.6% |
| LMArena Math | — | 1086 |

## Knowledge

- Codestral: —
- Llama 3.2 1B: 7.2 (#312)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | — | 23.9% |
| LMArena Expert | — | 1007 |

## Multilingual

- Codestral: —
- Llama 3.2 1B: 23.8 (#292)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | — | 973 |
| LMArena Chinese | — | 959 |
| LMArena German | — | 1014 |
| LMArena Russian | — | 941 |

## Instruction Following

- Codestral: —
- Llama 3.2 1B: 52.4 (#290)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | — | 1031 |

## Long Context

- Codestral: —
- Llama 3.2 1B: 31.9 (#274)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | — | 1050 |

## Writing & Preference

- Codestral: —
- Llama 3.2 1B: 21.3 (#310)

| Benchmark | Codestral | Llama 3.2 1B |
|---|---|---|
| LMArena Text | — | 1055 |
| LMArena Creative Writing | — | 1033 |
| EQ-Bench Creative Writing | — | 200 |
| LMArena Multi-Turn | — | 1030 |

## FAQ

### Is Codestral better than Llama 3.2 1B?

Codestral is the stronger model overall, scoring 30.6 to 20.1 on the Noometry Index. Llama 3.2 1B costs 6.4× less per token, which makes it the better buy when Codestral's lead doesn't matter for your workload.

### Which is cheaper, Codestral or Llama 3.2 1B?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Codestral lists at $0.30 and $0.90.

### Is Codestral or Llama 3.2 1B better for coding?

Codestral scores higher on coding benchmarks: 27.3 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

Codestral does, with 256K tokens against 60K.

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

2 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Llama 3.2 1B has 22.