Llama 4 Maverick vs Mistral Small 3.1 24B

Across our 12-test suite the models split wins 3–3 with 6 ties. Llama 4 Maverick wins: creative problem solving (3 vs 2; Llama rank 30 of 54, Mistral rank 47 of 54), safety calibration (2 vs 1; Llama rank 12 of 55, Mistral rank 32 of 55 — see benchmarkDescriptions: safety calibration = refusal/allow balance), and persona consistency (5 vs 2; Llama tied for 1st of 53, Mistral rank 51 of 53). Those results mean Llama is better at maintaining character, avoiding harmful outputs, and generating non-obvious ideas. Mistral Small 3.1 24B wins: long context (5 vs 4; Mistral tied for 1st of 55, Llama rank 38 of 55), strategic analysis (3 vs 2; Mistral rank 36 of 54, Llama rank 44 of 54), and (per our win/tie summary) tool calling — note however Mistral's tool calling score is 1/5 (rank 53 of 54) and the model is marked no_tool calling in its quirks, while Llama's tool calling run was transiently rate-limited during our test. Ties (both scored the same) include structured output (4), constrained rewriting (3), faithfulness (4), classification (3), agentic planning (3), and multilingual (4). Concretely: pick Llama when you need consistent persona, safer refusals, and better creative outputs; pick Mistral for tasks needing 30k+ token retrieval and slightly stronger strategic breakdowns. Ranks cited above are from our test set of 53–55 models (see detailed rankings in payload).

Costs are per 1k tokens (mTok). Llama 4 Maverick charges $0.15 input + $0.60 output = $0.75 per mTok. Mistral Small 3.1 24B charges $0.35 input + $0.56 output = $0.91 per mTok. Assuming equal input/output volume: 1M tokens (1,000 mTok) costs $750 (Llama) vs $910 (Mistral) — a $160 monthly gap. At 10M tokens: $7,500 vs $9,100 (difference $1,600). At 100M tokens: $75,000 vs $91,000 (difference $16,000). Teams doing high-volume inference or multi-tenant APIs should care about this gap; for low-volume prototypes the quality tradeoffs matter more than the incremental cost.

Choose Llama 4 Maverick if you build conversational assistants, persona-driven agents, or systems where safety calibration and creative problem generation matter — it scores 5/5 persona consistency and ranks tied for 1st there, and it scores better on safety calibration (rank 12 vs 32). Choose Mistral Small 3.1 24B if you need long-context work (long context 5/5, tied for 1st) or slightly better strategic analysis, and you can absorb higher input cost ($0.35/mTok). If monthly token volume is high (10M+ tokens), the ~ $1,600/month difference at 10M tokens favors Llama on cost; if long-context fidelity is critical, accept the higher price for Mistral.