Model comparison
Codestral vs Llama 2-70B
Codestral is the stronger model overall, scoring 30.1 to 24.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AI
MetaSummary
- The widest gap is in reasoning, where Codestral leads 19.1 to 13.7.
Side by side
| Codestral | Llama 2-70B | |
|---|---|---|
| Provider | Mistral AI | Meta |
| Noometry Index | 30.1 | 24.1 |
| Released | 2024-05-29 | 2023-07-18 |
| Weights | Open | Open |
| Context window | 256K | — |
| Max output | 4K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 7 | 35 |
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Category by category
Coding Llama 2-70B leads
Codestral: 26.9 (#316), Llama 2-70B: 30.8 (#281)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| Aider Polyglot | 11.1% | — |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1079 |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Reasoning Codestral leads
Codestral: 19.1 (#244), Llama 2-70B: 13.7 (#324)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | — |
| LMArena Hard Prompts | — | 1073 |
| DTBench | — | 41.6% |
| BIG-Bench Hard | — | 64.9% |
| CommonsenseQA 2.0 | — | 50% |
| Epoch Capabilities Index | — | 113.59 |
| ForecastBench | — | 51.4 |
| HellaSwag | — | 85.3% |
| LAMBADA | — | 78.9% |
| PIQA | — | 82.8% |
| WinoGrande | — | 80.2% |
Math Not comparable
Codestral: —, Llama 2-70B: 7.5 (#320)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 0% |
| LMArena Math | — | 1091 |
| MATH Level 5 | — | 3.3% |
| GSM8K | — | 69.6% |
Knowledge Not comparable
Codestral: —, Llama 2-70B: 7.4 (#305)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| GPQA Diamond | — | 26.3% |
| LMArena Expert | — | 1039 |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 88.6% |
| MMLU | — | 69.9% |
| OpenBookQA | — | 60.2% |
| TriviaQA | — | 87.6% |
Multilingual Not comparable
Codestral: —, Llama 2-70B: 27.9 (#269)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| LMArena Non-English | — | 1045 |
| LMArena Chinese | — | 994 |
| LMArena French | — | 1089 |
| LMArena German | — | 1041 |
| LMArena Japanese | — | 927 |
| LMArena Korean | — | 964 |
| LMArena Russian | — | 1083 |
| LMArena Spanish | — | 1142 |
Instruction Following Not comparable
Codestral: —, Llama 2-70B: 55.1 (#273)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| LMArena Instruction Following | — | 1071 |
Long Context Not comparable
Codestral: —, Llama 2-70B: 32.2 (#265)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| LMArena Longer Query | — | 1062 |
Writing & Preference Not comparable
Codestral: —, Llama 2-70B: 32.5 (#274)
| Benchmark | Codestral | Llama 2-70B |
|---|---|---|
| LMArena Text | — | 1115 |
| LMArena Creative Writing | — | 1075 |
| LMArena Multi-Turn | — | 1088 |
Frequently asked questions
Is Codestral better than Llama 2-70B?
Codestral is the stronger model overall, scoring 30.1 to 24.1 on the Noometry Index.
Is Codestral or Llama 2-70B better for coding?
Llama 2-70B scores higher on coding benchmarks: 30.8 versus 26.9 in the Noometry coding category.
How many benchmarks do Codestral and Llama 2-70B share?
0 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Llama 2-70B has 35.