Llama 4 Maverick vs Ministral 3 8B 2512

Across the 12 benchmarks in our test suite where both models were measured, Ministral 3 8B 2512 wins 4, Llama 4 Maverick wins 1, and they tie on 7.

Where Ministral 3 8B 2512 wins:

• Constrained rewriting (5 vs 3): Ministral scores 5/5, tied for 1st among 5 models out of 53 tested. Maverick scores 3/5, placing it in the bottom half of the field. This is a meaningful gap for tasks like summarization within character limits, copy editing, or content compression.
• Classification (4 vs 3): Ministral scores 4/5, tied for 1st with 29 other models out of 53. Maverick scores 3/5, placing rank 31 of 53. Better classification translates directly to routing, intent detection, and labeling pipelines.
• Strategic analysis (3 vs 2): Ministral scores 3/5 (rank 36 of 54) versus Maverick's 2/5 (rank 44 of 54). Neither is strong here — both fall well below the field median of 4/5 — but Ministral is measurably less weak on nuanced tradeoff reasoning.
• Tool calling (4 vs not scored): Ministral scores 4/5 (rank 18 of 54) on function selection and argument accuracy. Llama 4 Maverick has no tool calling score in our data — the test hit a 429 rate limit on OpenRouter during testing (April 13, 2026) and is noted as likely transient. Treat Maverick's tool calling capability as unverified in our suite.

Where Llama 4 Maverick wins:

• Safety calibration (2 vs 1): Maverick scores 2/5 (rank 12 of 55), Ministral scores 1/5 (rank 32 of 55). This is Maverick's only benchmark win, and context matters: both scores are below the field median of 2/5, so neither model shines on this dimension. Maverick is modestly better at refusing harmful requests while permitting legitimate ones.

Ties (7 benchmarks): Both models score identically on structured output (4/5), creative problem solving (3/5), faithfulness (4/5), long context (4/5), persona consistency (5/5), agentic planning (3/5), and multilingual (4/5). On persona consistency, both tie for 1st with 36 other models — this score is a weak differentiator across the field. On multilingual, both rank 36 of 55. On long context, both rank 38 of 55 with a 4/5 score, though Maverick's context window is 4x larger (1,048,576 vs 262,144 tokens), which may matter for very long document tasks beyond what our 30K+ retrieval test covers.

Both models share an identical input cost of $0.15 per million tokens. The gap opens on output: Llama 4 Maverick charges $0.60/Mtok versus Ministral 3 8B 2512's $0.15/Mtok — a 4x difference. In practice, that means at 1M output tokens/month you pay $0.60 for Maverick vs $0.15 for Ministral 3 8B 2512, a $0.45 gap. Scale to 10M output tokens and it's $6 vs $1.50 — a $4.50 monthly difference. At 100M output tokens (a serious production workload), Maverick costs $60 vs $15 for Ministral 3 8B 2512, a $45/month gap. Developers running high-volume inference pipelines — chatbots, document processing, classification services — should weight this heavily. Llama 4 Maverick's 4x output premium is only justifiable if you specifically need its larger context window (1,048,576 tokens vs 262,144) or its marginally better safety calibration score.

Choose Ministral 3 8B 2512 if: you're building classification systems, constrained rewriting pipelines (summaries, copy compression), or agentic workflows requiring tool calling — it wins or ties on all those dimensions and costs 4x less on output. It's also the better pick for high-volume production APIs where output token costs accumulate, or anywhere strategic analysis is in scope.

Choose Llama 4 Maverick if: you need a context window beyond 262K tokens (Maverick supports up to 1,048,576 tokens, nearly 4x larger), you require its image input modality with the specific parameter set it supports (including min_p, top_k, logit_bias), or marginally better safety calibration is a hard requirement. Be aware that its tool calling performance is unverified in our suite due to a rate-limit event during testing.