DeepSeek V3.1 Terminus vs Ministral 3 14B 2512

Across our 12-test suite the two models split wins 5–5 with 2 ties (creative_problem_solving and safety_calibration). Test-by-test:

• long_context: DeepSeek 5 vs Ministral 4. DeepSeek ties for 1st of 55 (tied with 36 others), making it the clear choice for tasks that must retrieve or reason across 30K+ tokens.
• structured_output: DeepSeek 5 vs Ministral 4. DeepSeek is tied for 1st of 54 (tied with 24), so it’s stronger at JSON/schema compliance and format adherence.
• strategic_analysis: DeepSeek 5 vs Ministral 4. DeepSeek is tied for 1st of 54 (tied with 25), which translates to better nuanced tradeoff reasoning in our tests.
• agentic_planning: DeepSeek 4 vs Ministral 3. DeepSeek ranks 16/54, so it decomposes goals and plans recovery paths more reliably in our scenarios.
• multilingual: DeepSeek 5 vs Ministral 4. DeepSeek is tied for 1st of 55 (tied with 34), so multilingual parity favors DeepSeek in our testing.
• constrained_rewriting: Ministral 4 vs DeepSeek 3. Ministral ranks 6/53, indicating stronger compression into hard character limits.
• tool_calling: Ministral 4 vs DeepSeek 3. Ministral ranks 18/54 versus DeepSeek at 47/54, so Ministral is better at selecting functions, arguments, and sequencing calls in our tool-calling tests.
• faithfulness: Ministral 4 vs DeepSeek 3. Ministral ranks 34/55 vs DeepSeek at 52/55 — in our tests Ministral sticks to source material with fewer hallucinations.
• classification: Ministral 4 vs DeepSeek 3. Ministral is tied for 1st of 53 (tied with 29), so it outperformed DeepSeek on routing and categorization tasks.
• persona_consistency: Ministral 5 vs DeepSeek 4. Ministral is tied for 1st of 53 (tied with 36), showing stronger resistance to injection and better role consistency in our tests.
• creative_problem_solving: tie 4/4. Both rank similarly (each rank 9 in creative tasks) producing non-obvious feasible ideas at the same level in our evaluation.
• safety_calibration: tie 1/1. Both models scored poorly on safety calibration in our tests (rank 32 of 55 for each), so neither should be relied on as a sole safety filter. Interpretation for real tasks: pick DeepSeek when you must handle massive contexts, strict schema outputs, strategic reasoning, or multilingual work. Pick Ministral for classification, faithful sourcing, persona-driven chatbots, constrained rewriting, and better tool-calling — all at materially lower output cost.

Per the payload, DeepSeek V3.1 Terminus charges $0.21 input / $0.79 output per mTok; Ministral 3 14B 2512 charges $0.20 input / $0.20 output per mTok. Note the payload's priceRatio = 3.95, which matches DeepSeek's $0.79 output ÷ Ministral's $0.20 output. Using mTok as the unit in the payload, a simple equal-split example (50% prompt tokens, 50% generated tokens):

• 1M tokens/month (500k in, 500k out): DeepSeek ≈ $500 (0.21×500 + 0.79×500); Ministral ≈ $200 (0.20×500 + 0.20×500).
• 10M tokens/month: DeepSeek ≈ $5,000; Ministral ≈ $2,000.
• 100M tokens/month: DeepSeek ≈ $50,000; Ministral ≈ $20,000. Who should care: startups, scale-ups, and cost-sensitive products that generate lots of output tokens (summaries, long replies, content generation) will see the largest savings with Ministral. Teams that require very long-context reasoning or strict structured outputs should budget for DeepSeek’s higher output price (DeepSeek’s output cost is 3.95× higher per the payload).

Choose DeepSeek V3.1 Terminus if you need: very long-context retrieval (ranks tied for 1st on long_context), reliable structured/JSON outputs (tied for 1st on structured_output), strategic numerical reasoning (tied for 1st on strategic_analysis), or best-in-class multilingual behavior — and you can accept higher output costs.
Choose Ministral 3 14B 2512 if you need: best-effort classification (tied for 1st on classification), strong persona consistency (tied for 1st on persona_consistency), better tool-calling (rank 18/54), higher faithfulness, or if you must minimize per-token spend (Ministral’s output is $0.20 vs DeepSeek $0.79).