Llama 4 Scout vs Mistral Small 3.1 24B

Summary of head-to-head scores (our 12-test suite): Llama 4 Scout wins creative problem solving (3 vs 2), tool calling (4 vs 1), classification (4 vs 3), safety calibration (2 vs 1), and persona consistency (3 vs 2). Mistral wins strategic analysis (3 vs 2) and agentic planning (3 vs 2). They tie on structured output (4/4), constrained rewriting (3/3), faithfulness (4/4), long context (5/5) and multilingual (4/4). Context and implications: - Tool calling: Llama scores 4 vs Mistral's 1; ranking shows Llama at rank 18 of 54 (tied) while Mistral sits at rank 53 of 54 — practically, Llama is far more reliable for function selection and argument accuracy. Note Mistral's metadata lists a no_tool calling quirk. - Classification: Llama scores 4 and is tied for 1st (tied with 29 others out of 53), while Mistral scores 3 (rank 31 of 53) — Llama is the safer routing/classification choice. - Long context: both score 5 and are tied for 1st (with 36 others) — both are solid for 30K+ token retrieval tasks. - Agentic planning & strategic analysis: Mistral wins both (3 vs 2); Mistral ranks better on agentic planning (rank 42 of 54) than Llama (rank 53 of 54), so Mistral produces stronger goal decomposition and tradeoff reasoning in our tests. - Safety and persona: Llama's safety calibration is 2 vs Mistral's 1 (Llama rank 12 of 55 vs Mistral rank 32), meaning Llama more reliably refuses harmful requests in our testing. Overall, Llama is the better all-rounder in our benchmarks (5 wins vs 2), with decisive advantages in tool calling, classification and safer behavior; Mistral shows targeted strengths in planning/strategy but is handicapped by no_tool calling and higher cost.

We use the payload's per-mtok prices (mtok = 1,000 tokens). Llama 4 Scout: $0.08 input / $0.30 output per 1k tokens. Mistral Small 3.1 24B: $0.35 input / $0.56 output per 1k tokens. Assuming a 50/50 split of input/output tokens, monthly costs are: for 1M tokens — Llama $190 vs Mistral $455; 10M tokens — Llama $1,900 vs Mistral $4,550; 100M tokens — Llama $19,000 vs Mistral $45,500. If your workload is output-heavy, per-1M-token output-only costs are $300 (Llama) vs $560 (Mistral). The upshot: Llama reduces token spend by ~58% in typical balanced usage; teams with tight budgets or high-volume apps should prefer Llama. Teams focused on edge-case planning/strategy who accept a ~2.4–2.6x price premium may consider Mistral.

Choose Llama 4 Scout if you need: - reliable tool calling and function argument accuracy (tool calling 4 vs 1), - classification and routing (classification 4 vs 3; tied for 1st rank), - strong long-context handling (5/5 tie) and better safety calibration (2 vs 1), or if you must minimize token costs (example: 1M balanced tokens = $190 vs $455 for Mistral). Choose Mistral Small 3.1 24B if you prioritize: - strategic analysis and agentic planning (scores 3 vs 2 in our tests) and are willing to pay ~2.4x–2.6x more for those gains, and you do not need tool calling (the model lists a no_tool calling quirk).