Model comparison
Llama 4 Scout vs Mistral 7B
Llama 4 Scout is the stronger model overall, scoring 27.7 to 23.0 on the Noometry Index.
Last verified . 22 shared benchmarks.
Meta
Mistral AISummary
- They share 22 benchmarks with published results for both. Llama 4 Scout scores higher in 5 categories and Mistral 7B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Scout leads 31.9 to 7.4.
- The biggest single-benchmark swing is MATH Level 5: 62.3% for Llama 4 Scout and 3.7% for Mistral 7B.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.25 / $0.25 for Mistral 7B.
- Llama 4 Scout accepts more context: 128K tokens versus 8K.
Side by side
| Llama 4 Scout | Mistral 7B | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 27.7 | 23.0 |
| Released | 2025-04-05 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 128K | 8K |
| Max output | 4K | 8K |
| Input $ / M tokens | $0.10 | $0.25 |
| Output $ / M tokens | $0.30 | $0.25 |
| Results tracked | 43 | 37 |
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Category by category
Coding Mistral 7B leads
Llama 4 Scout: 20.2 (#339), Mistral 7B: 26.4 (#326)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Coding | 1286 | 1082 |
| BigCodeBench Complete | 43.1% | 27.3% |
| SWE-bench Verified (bash only) | 9.1% | — |
| SciCode | 17% | — |
| BigCodeBench Instruct | — | 19.5% |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
Llama 4 Scout: 24.6 (#119), Mistral 7B: —
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning Mistral 7B leads
Llama 4 Scout: 9.1 (#345), Mistral 7B: 13.1 (#336)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Hard Prompts | 1266 | 1067 |
| DTBench | 57.9% | 42.5% |
| Epoch Capabilities Index | 129.64 | 112.21 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| ARC-AGI-1 | 0.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 0% |
| LMCA | 12% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| ForecastBench | 57.5 | — |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math Llama 4 Scout leads
Llama 4 Scout: 19.6 (#286), Mistral 7B: 8.1 (#325)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 0.3% |
| LMArena Math | 1287 | 1085 |
| MATH Level 5 | 62.3% | 3.7% |
| Omni-MATH | 37.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
| GSM8K | — | 54.4% |
Knowledge Llama 4 Scout leads
Llama 4 Scout: 31.9 (#217), Mistral 7B: 7.4 (#311)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| GPQA Diamond | 51.8% | 15.2% |
| LMArena Expert | 1235 | 1036 |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
Llama 4 Scout: 32.2 (#102), Mistral 7B: —
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual Llama 4 Scout leads
Llama 4 Scout: 41.0 (#212), Mistral 7B: 25.8 (#283)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1252 | 1012 |
| LMArena Chinese | 1255 | 1009 |
| LMArena French | 1282 | 1037 |
| LMArena German | 1272 | 987 |
| LMArena Japanese | 1206 | 878 |
| LMArena Russian | 1263 | 1018 |
| LMArena Spanish | 1278 | 1026 |
| LMArena Korean | 1207 | — |
Instruction Following Llama 4 Scout leads
Llama 4 Scout: 65.8 (#217), Mistral 7B: 54.2 (#280)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1248 | 1060 |
| IFEval | 81.8% | — |
Long Context Mistral 7B leads
Llama 4 Scout: 27.5 (#294), Mistral 7B: 32.2 (#271)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1265 | 1060 |
| Fiction.LiveBench | 36% | — |
Writing & Preference Llama 4 Scout leads
Llama 4 Scout: 37.0 (#261), Mistral 7B: 30.7 (#286)
| Benchmark | Llama 4 Scout | Mistral 7B |
|---|---|---|
| LMArena Text | 1279 | 1090 |
| LMArena Creative Writing | 1249 | 1068 |
| LMArena Multi-Turn | 1280 | 1062 |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
Frequently asked questions
Is Llama 4 Scout better than Mistral 7B?
Llama 4 Scout is the stronger model overall, scoring 27.7 to 23.0 on the Noometry Index.
Which is cheaper, Llama 4 Scout or Mistral 7B?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Mistral 7B lists at $0.25 and $0.25.
Is Llama 4 Scout or Mistral 7B better for coding?
Mistral 7B scores higher on coding benchmarks: 26.4 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Llama 4 Scout does, with 128K tokens against 8K.
How many benchmarks do Llama 4 Scout and Mistral 7B share?
22 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Mistral 7B has 37.