Llama 4 Maverick vs Mistral Small 3.2 24B

Summary of wins/ties in our 12-test suite: each model wins 3 tests and they tie on 6. Llama 4 Maverick wins creative problem solving (score 3 vs Mistral 2), safety calibration (2 vs 1) and persona consistency (5 vs 3). Persona_consistency is a standout for Llama — it is tied for 1st (tied with 36 others) on that test, which matters for chatbots and role-play where maintaining character and resisting injection is essential. Mistral Small 3.2 24B wins constrained rewriting (4 vs 3), tool calling (4 vs Llama’s transient rate-limited run), and agentic planning (4 vs 3). Notably, constrained rewriting is a strong area for Mistral (rank 6 of 53), so it’s better when you must compress or fit output into strict character limits. Tool calling (Mistral rank 18 of 54) and agentic planning (Mistral rank 16 of 54 vs Llama rank 42 of 54) mean Mistral is superior for function selection, argument accuracy, and goal decomposition in our tests. The following tests are ties: structured output (both 4, rank 26 of 54), strategic analysis (both 2, rank 44 of 54), faithfulness (both 4, rank 34 of 55), classification (both 3, rank 31 of 53), long context (both 4, rank 38 of 55) and multilingual (both 4, rank 36 of 55). One operational quirk: Llama 4 Maverick’s tool calling run hit a 429 rate limit on OpenRouter during testing (likely transient), but Mistral produced a clean tool calling score of 4. Also consider context windows: Llama lists a 1,048,576 token window vs Mistral’s 128,000 — the raw capacity favors Llama for extremely long contexts even though long context test scores tied at 4 in our suite.

Prices (per 1M tokens): Llama 4 Maverick input $0.15, output $0.60; Mistral Small 3.2 24B input $0.075, output $0.20. Assuming a 50/50 input/output split: monthly cost for Llama = $0.375 per 1M tokens, $3.75 per 10M, $37.50 per 100M. For Mistral = $0.1375 per 1M, $1.375 per 10M, $13.75 per 100M. Output-heavy workloads amplify the gap (e.g., 90% output: Llama ≈ $0.555 per 1M vs Mistral ≈ $0.1875 per 1M). Who should care: product teams at scale, chat/API businesses, and anyone generating large volumes of model output — the ~3x output cost ratio (priceRatio = 3) makes Mistral materially cheaper at 10M+ tokens/month.

Choose Llama 4 Maverick if you need: - Strong persona consistency and creative outputs (persona consistency 5 vs 3). - Better safety calibration in our tests (2 vs 1). - Very large raw context capacity (1,048,576 token window) for archival or multi-document tasks. Choose Mistral Small 3.2 24B if you need: - Cost-efficient production usage (input/output $0.075/$0.20 vs $0.15/$0.60). - Better constrained rewriting (score 4; rank 6/53), tool calling (score 4; rank 18/54), or agentic planning (score 4; rank 16/54). - A lower-cost option for high-volume output or function-calling workflows.