Llama 4 Scout vs Mistral Small 3.2 24B

Across our 12-test suite, Llama 4 Scout wins 4 categories, Mistral Small 3.2 24B wins 2, and they tie on 6. Neither model has been assigned an overall average score in the current dataset, so we're working from individual benchmark results.

Where Llama 4 Scout wins:

• Long context (5 vs 4): Llama 4 Scout scores 5/5, tied for 1st among 55 models in our testing. Mistral Small 3.2 24B scores 4/5, ranked 38th of 55. With a 327,680-token context window vs Mistral's 128,000, this is both a benchmark win and a raw capability advantage — Scout can process documents roughly 2.5x longer.
• Classification (4 vs 3): Scout scores 4/5, tied for 1st among 53 models. Mistral Small 3.2 24B scores 3/5, ranked 31st of 53. For routing, tagging, and categorization tasks, this is a meaningful gap.
• Creative problem solving (3 vs 2): Scout scores 3/5 (ranked 30th of 54); Mistral Small 3.2 24B scores 2/5 (ranked 47th of 54). Neither model excels here — both fall below the 50th percentile median of 4 — but Scout is notably less weak.
• Safety calibration (2 vs 1): Scout scores 2/5 (ranked 12th of 55); Mistral Small 3.2 24B scores 1/5 (ranked 32nd of 55). Both are below the field median of 2, but Scout is the stronger performer. Note that the median here is low — safety calibration is a weakness across the benchmark pool.

Where Mistral Small 3.2 24B wins:

• Agentic planning (4 vs 2): This is Mistral Small 3.2 24B's clearest advantage. It scores 4/5, ranked 16th of 54 in our testing. Llama 4 Scout scores just 2/5, ranked 53rd of 54 — near the bottom of the field. For goal decomposition, multi-step task execution, and failure recovery, Mistral Small 3.2 24B is substantially better.
• Constrained rewriting (4 vs 3): Mistral Small 3.2 24B scores 4/5, ranked 6th of 53. Scout scores 3/5, ranked 31st of 53. For compression tasks with hard character limits — ad copy, headlines, summaries — Mistral Small 3.2 24B is the better tool.

Where they tie: Both models score identically on structured output (4/4), strategic analysis (2/2), tool calling (4/4), faithfulness (4/4), persona consistency (3/3), and multilingual (4/4). Tool calling and structured output are particularly important for production API use — the tie here means neither model has an edge for JSON-based integrations or function calling workflows. Both score 2/5 on strategic analysis, below the field median of 4, meaning nuanced tradeoff reasoning is a shared weakness.

Llama 4 Scout costs $0.08/M input and $0.30/M output. Mistral Small 3.2 24B costs $0.075/M input and $0.20/M output. The input gap is trivial — a rounding error at any volume. The output gap is where it matters: Llama 4 Scout costs 50% more per output token. At 1M output tokens/month, that's $300 vs $200 — a $100 difference you may not notice. At 10M output tokens/month, it's $3,000 vs $2,000, a $1,000/month gap that warrants scrutiny. At 100M output tokens/month, you're paying $30,000 vs $20,000 — $10,000/month extra for Llama 4 Scout's benchmark advantages. High-volume API users running output-heavy workloads (long-form generation, summarization at scale) should weigh whether Llama 4 Scout's wins in classification and long-context justify that premium. For most workloads where the two models tie across 6 benchmarks, Mistral Small 3.2 24B offers the better value.

Choose Llama 4 Scout if:

• Your application requires processing documents over 128K tokens — Scout's 327,680-token context window is a hard capability advantage Mistral Small 3.2 24B cannot match.
• You're building classification or routing pipelines where Scout's tied-for-1st score (4/5 vs 3/5) translates to fewer mislabeled outputs.
• Output volume is moderate (under 10M tokens/month) and the $0.10/M output premium is acceptable for the benchmark gains.
• You need a safer response profile — Scout's safety calibration score (2 vs 1) is modestly better.

Choose Mistral Small 3.2 24B if:

• You're building agentic systems, autonomous pipelines, or multi-step workflows. Scout ranked 53rd of 54 on agentic planning in our testing; Mistral Small 3.2 24B ranked 16th. This is not a close call.
• Your use case involves constrained rewriting — headlines, character-limited copy, editorial compression — where Mistral Small 3.2 24B ranks 6th of 53 vs Scout's 31st.
• Output volume is high (10M+ tokens/month) and you want to capture the $0.10/M output savings without sacrificing much on the 6 benchmarks where the models tie.
• Your context window needs fit within 128K tokens and you don't need Scout's extended window.