Llama 4 Maverick vs Ministral 3 3B 2512

Summary of our 12-test suite head-to-head (scores are our internal 1-5 ratings and ranks are from the payload):

• Ministral wins: constrained rewriting 5 vs 3 (Ministral tied for 1st of 53, display: "tied for 1st with 4 other models"), faithfulness 5 vs 4 (Ministral tied for 1st of 55, display: "tied for 1st with 32 other models"), classification 4 vs 3 (Ministral tied for 1st of 53, display: "tied for 1st with 29 other models"), and tool calling 4 vs Llama's transient tool calling rate-limit (Ministral rank 18 of 54, display: "rank 18 of 54 (29 models share this score)"). Practically, that means Ministral is stronger at compressing text into hard character limits, sticking to source material, accurate routing/categorization, and function selection/argument sequencing on our tests.
• Llama wins: persona consistency 5 vs 4 (Llama tied for 1st of 53, display: "tied for 1st with 36 other models") and safety calibration 2 vs 1 (Llama rank 12 of 55, display: "rank 12 of 55 (20 models share this score)"). That indicates Llama better preserves character/persona and more reliably refuses harmful requests in our testing.
• Ties (equal scores): structured output 4/4 (both rank 26 of 54), strategic analysis 2/2 (both rank 44 of 54), creative problem solving 3/3 (both rank 30 of 54), long context 4/4 (both rank 38 of 55), agentic planning 3/3 (both rank 42 of 54), multilingual 4/4 (both rank 36 of 55). For example, both models scored 4 on long context but Llama provides a much larger context_window (1,048,576 vs 131,072), which affects real-world long-document workflows despite equal long context scores.
• Quirks: Llama 4 Maverick hit a transient tool calling 429 rate limit on OpenRouter during our run (noted in the payload), which affected that test's run reliability. Use these score-by-score results to match model strengths to task demands rather than assuming a single overall winner.

Prices in the payload are per mTok (we assume per 1,000 tokens). Using a 50/50 input/output token split as a practical approximation: Llama 4 Maverick charges input $0.15/mTok and output $0.60/mTok; Ministral 3 3B 2512 charges $0.10/mTok for both input and output. Cost examples (50/50 split):

• 1M tokens/month: Llama = $375 (input $75 + output $300); Ministral = $100 (input $50 + output $50).
• 10M tokens/month: Llama = $3,750; Ministral = $1,000.
• 100M tokens/month: Llama = $37,500; Ministral = $10,000. Who should care: teams running high-volume inference or low-margin products will feel a large impact — at 100M tokens/month the delta is $27,500. If your workload is small (<<1M tokens) the quality tradeoffs may matter more than cost, but at scale the price gap dominates.

Choose Ministral 3 3B 2512 if: you need a low-cost production model with stronger constrained rewriting, tool calling, faithfulness, and classification on our tests — and you want to minimize inference spend (output $0.10/mTok). Choose Llama 4 Maverick if: maintaining persona, safer refusal behavior, or extreme context capacity matters more than cost (Llama offers a 1,048,576 token window and wins persona consistency and safety calibration in our testing), and you can afford the higher output price ($0.60/mTok).