Model comparison
Codestral vs Llama 4 Scout
Codestral is the stronger model overall, scoring 30.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.0× less per token, which makes it the better buy when Codestral's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Mistral AI
MetaSummary
- They share 2 benchmarks with published results for both. Codestral scores higher in 2 categories and Llama 4 Scout in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Codestral leads 19.8 to 9.1.
- The biggest single-benchmark swing is BigCodeBench Complete: 52.5% for Codestral and 43.1% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- Codestral accepts more context: 256K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Codestral | Llama 4 Scout | |
|---|---|---|
| Provider | Mistral AI | Meta |
| Noometry Index | 30.6 | 27.7 |
| Released | 2024-05-29 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 256K | 128K |
| Max output | 8K | 4K |
| Input $ / M tokens | $0.30 | $0.10 |
| Output $ / M tokens | $0.90 | $0.30 |
| Results tracked | 7 | 43 |
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Category by category
Coding Codestral leads
Codestral: 27.3 (#321), Llama 4 Scout: 20.2 (#339)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| BigCodeBench Complete | 52.5% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| Aider Polyglot | 11.1% | — |
| SciCode | — | 17% |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1286 |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning Codestral leads
Codestral: 19.8 (#251), Llama 4 Scout: 9.1 (#345)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 36.9% |
| ARC-AGI-2 | — | 0% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| LMArena Hard Prompts | — | 1266 |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
Math Not comparable
Codestral: —, Llama 4 Scout: 19.6 (#286)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| LMArena Math | — | 1287 |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Not comparable
Codestral: —, Llama 4 Scout: 31.9 (#217)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| LMArena Expert | — | 1235 |
Multimodal Not comparable
Codestral: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Not comparable
Codestral: —, Llama 4 Scout: 41.0 (#212)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | — | 1252 |
| LMArena Chinese | — | 1255 |
| LMArena French | — | 1282 |
| LMArena German | — | 1272 |
| LMArena Japanese | — | 1206 |
| LMArena Korean | — | 1207 |
| LMArena Russian | — | 1263 |
| LMArena Spanish | — | 1278 |
Instruction Following Not comparable
Codestral: —, Llama 4 Scout: 65.8 (#217)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| IFEval | — | 81.8% |
| LMArena Instruction Following | — | 1248 |
Long Context Not comparable
Codestral: —, Llama 4 Scout: 27.5 (#294)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | — | 36% |
| LMArena Longer Query | — | 1265 |
Writing & Preference Not comparable
Codestral: —, Llama 4 Scout: 37.0 (#261)
| Benchmark | Codestral | Llama 4 Scout |
|---|---|---|
| LMArena Text | — | 1279 |
| LMArena Creative Writing | — | 1249 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| LMArena Multi-Turn | — | 1280 |
Frequently asked questions
Is Codestral better than Llama 4 Scout?
Codestral is the stronger model overall, scoring 30.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.0× 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 4 Scout?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Codestral lists at $0.30 and $0.90.
Is Codestral or Llama 4 Scout better for coding?
Codestral scores higher on coding benchmarks: 27.3 versus 20.2 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 4 Scout share?
2 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Llama 4 Scout has 43.