Llama 4 Maverick vs Ministral 3 14B 2512
For most production use cases that balance performance and cost, Ministral 3 14B 2512 is the better pick (wins 5 of 12 benchmarks in our testing). Llama 4 Maverick is preferable where safety calibration and extreme context or long single outputs matter, but it comes at a roughly 3x cost premium on typical token mixes.
meta
Llama 4 Maverick
Benchmark Scores
External Benchmarks
Pricing
Input
$0.150/MTok
Output
$0.600/MTok
modelpicker.net
mistral
Ministral 3 14B 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.200/MTok
Output
$0.200/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite, Ministral 3 14B 2512 wins five specific tests in our testing: strategic analysis (B=4 vs A=2; B ranks 27 of 54 vs A 44 of 54), constrained rewriting (B=4 vs A=3; B ranks 6 of 53 vs A 31 of 53), creative problem solving (B=4 vs A=3; B ranks 9 of 54 vs A 30 of 54), tool calling (B=4; ranks 18 of 54 — Llama encountered a tool calling rate limit in our run), and classification (B=4 vs A=3; B is tied for 1st with 29 others out of 53). Llama 4 Maverick wins one test: safety calibration (A=2 vs B=1), where Llama ranks 12 of 55 compared with Mistral's 32 of 55 — meaning in our testing Llama is more likely to refuse harmful requests correctly while permitting legitimate ones. The models tie on structured output (both 4), faithfulness (4), long context (4), persona consistency (both 5 and tied for 1st), agentic planning (both 3), and multilingual (both 4). Practical implications: choose Mistral for classification, coding/tool workflows, compressed rewriting, and higher-quality creative/strategic outputs; choose Llama when safety calibration and extremely large context or longer single outputs are primary requirements. Note context and output limits in the payload: Llama 4 Maverick shows a 1,048,576 token context window and a max_output_tokens of 16,384; Ministral 3 14B 2512 has a 262,144 token context window and unspecified max_output_tokens — that raw context capacity favors Llama for very long-document retrieval or very long single responses.
Pricing Analysis
Per the payload prices, Llama 4 Maverick charges $0.15 per mTok input and $0.60 per mTok output; Ministral 3 14B 2512 charges $0.20 per mTok input and $0.20 per mTok output. Assuming a 50/50 input/output token split (500 mTok of each per 1M tokens): Llama 4 Maverick costs 500*$0.15 + 500*$0.60 = $375 per 1M tokens; Mistral costs 500*$0.20 + 500*$0.20 = $200 per 1M tokens. At scale that becomes: 10M tokens = $3,750 (Llama) vs $2,000 (Mistral); 100M tokens = $37,500 vs $20,000. If your workload is generation-heavy (e.g., 80% output), Llama rises to ~ $510 per 1M tokens while Mistral remains ~$200 per 1M tokens. The ~3x priceRatio in the payload matters for SaaS and high-volume API users: teams sending >10M tokens/month or heavy-output apps should prefer Mistral for cost efficiency; teams where a stricter safety refusal profile or extremely large single-output/context needs justify the extra spend may prefer Llama 4 Maverick.
Real-World Cost Comparison
Bottom Line
Choose Llama 4 Maverick if: you need stronger safety calibration in our testing, massive context windows (1,048,576 tokens), longer single-output ceilings (16,384 max_output_tokens), and you can absorb a ~3x token-cost premium. Ideal for high-safety chat assistants, long-context retrieval, or applications requiring long single outputs. Choose Ministral 3 14B 2512 if: you prioritize cost-efficiency and higher scores on classification, tool calling, constrained rewriting, creative problem solving, and strategic analysis (Mistral wins 5 tests to Llama's 1 in our testing). Ideal for high-volume APIs, coding assistants, classification/routing, and constrained-output generators.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.