Llama 4 Maverick vs Mistral Large 3 2512

Across the 11 benchmarks where both models were scored in our testing, Mistral Large 3 2512 wins 6, Llama 4 Maverick wins 2, and 4 are tied.

Where Mistral Large 3 2512 wins:

• Structured output (5 vs 4): Mistral Large 3 2512 ties for 1st among 54 models; Llama 4 Maverick ranks 26th. For JSON schema compliance and format adherence in production pipelines, this gap is meaningful.
• Strategic analysis (4 vs 2): The sharpest gap in this comparison. Mistral Large 3 2512 ranks 27th of 54; Llama 4 Maverick ranks 44th of 54. Nuanced tradeoff reasoning with real numbers is a clear Mistral strength.
• Faithfulness (5 vs 4): Mistral Large 3 2512 ties for 1st of 55 models on sticking to source material without hallucinating. Llama 4 Maverick ranks 34th. For RAG applications and summarization where accuracy to source matters, this is significant.
• Agentic planning (4 vs 3): Mistral Large 3 2512 ranks 16th of 54; Llama 4 Maverick ranks 42nd. Goal decomposition and failure recovery is substantially better in our testing.
• Tool calling (4 vs not tested): Llama 4 Maverick's tool calling test hit a 429 rate limit during our testing on 2026-04-13, so no score was recorded. Mistral Large 3 2512 ranks 18th of 54 with a 4/5. This is a data gap, not a confirmed weakness, but it means we cannot verify Maverick's tool calling performance.
• Multilingual (5 vs 4): Mistral Large 3 2512 ties for 1st of 55 models; Llama 4 Maverick ranks 36th. For non-English output quality, Mistral Large 3 2512 is the safer choice.

Where Llama 4 Maverick wins:

• Persona consistency (5 vs 3): Llama 4 Maverick ties for 1st of 53 models. Mistral Large 3 2512 ranks 45th — a significant drop. For chatbot personas, roleplay, and injection resistance, Maverick has a real edge.
• Safety calibration (2 vs 1): Both models score below the field median (p50: 2), but Llama 4 Maverick ranks 12th of 55 while Mistral Large 3 2512 ranks 32nd. Neither model excels here — this is a weak area for both.

Tied benchmarks (both score 3/5):

• Creative problem solving, classification, and constrained rewriting: tied at 3/5, both ranking around 30th of their respective pools. Neither model distinguishes itself on these tasks.

Long context (both 4/5): Both rank 38th of 55, tied identically. Llama 4 Maverick's 1M token context window vs Mistral Large 3 2512's 262K window is a structural difference not captured in this score — if you need to process very long documents, Maverick's architecture supports it even if both perform similarly on our 30K+ token retrieval test.

Llama 4 Maverick costs $0.15/M input and $0.60/M output. Mistral Large 3 2512 costs $0.50/M input and $1.50/M output — 3.3× more on input and 2.5× more on output.

At 1M output tokens/month, the gap is just $0.90 ($0.60 vs $1.50) — negligible for most teams. At 10M output tokens, it's $9 vs $15, still manageable. At 100M output tokens — typical for a production API serving thousands of users — you're paying $60,000/year for Llama 4 Maverick vs $150,000/year for Mistral Large 3 2512. That $90,000 annual gap is a real budget line.

Who should care: consumer-facing products with high token volume, batch processing pipelines, or any workload that generates thousands of completions per day. For low-volume internal tools or prototyping, the quality difference from Mistral Large 3 2512's benchmark wins may well justify the cost. For high-volume commodity tasks where classification (tied at 3/5) or constrained rewriting (tied at 3/5) is all you need, Llama 4 Maverick's pricing is hard to beat.

Choose Llama 4 Maverick if:

• Your application requires strong persona consistency (chatbots, character-based products, system prompt robustness) — it ranks in the top tier on this benchmark in our testing
• You're running at high token volume (100M+ output tokens/month) where the $0.90/M output cost difference compounds significantly
• You need a context window beyond 262K tokens — Maverick's 1M token window is structurally larger
• Your workload is primarily classification, constrained rewriting, or creative problem solving (tied scores, so no quality tradeoff)
• You accept the caveat that tool calling performance is unverified due to a rate limit during our testing

Choose Mistral Large 3 2512 if:

• You're building agentic or function-calling workflows where agentic planning (4 vs 3) and structured output (5 vs 4) matter directly
• Your application involves analysis, reasoning, or summarization — Mistral Large 3 2512's faithfulness (5 vs 4) and strategic analysis (4 vs 2) scores are materially better
• You need verified tool calling performance — Mistral Large 3 2512 scored 4/5 in our tests; Maverick's result is missing due to a rate limit
• You serve non-English users — Mistral Large 3 2512 scores 5 vs 4 on multilingual output
• Volume is under 10M output tokens/month, where the cost difference is under $9/month and quality wins should dominate the decision