DeepSeek V3.1 vs Ministral 3 14B 2512

Our 12-test suite (scores 1-5) shows DeepSeek V3.1 wins 5 tests, Ministral 3 wins 3, and 4 are ties. Detailed comparison (score, and ranking context):

• Faithfulness: DeepSeek 5 (tied for 1st of 55, tied with 32 others) vs Ministral 4 (rank 34 of 55). In our testing DeepSeek sticks to source material more reliably for tasks that require precise quoting or citeable facts.
• Structured output: DeepSeek 5 (tied for 1st of 54, tied with 24 others) vs Ministral 4 (rank 26 of 54). For JSON/schema compliance and strict formats choose DeepSeek when format correctness matters.
• Long context: DeepSeek 5 (tied for 1st of 55, tied with 36 others) vs Ministral 4 (rank 38 of 55). DeepSeek performed better on retrieval and accuracy across 30k+ token scenarios in our tests.
• Creative problem solving: DeepSeek 5 (tied for 1st of 54) vs Ministral 4 (rank 9 of 54). DeepSeek produced more specific, feasible ideas in our prompts.
• Agentic planning: DeepSeek 4 (rank 16 of 54) vs Ministral 3 (rank 42 of 54). DeepSeek is stronger at goal decomposition and recovery in our planning tasks.
• Constrained rewriting: DeepSeek 3 (rank 31 of 53) vs Ministral 4 (rank 6 of 53). Ministral better compresses content under hard character limits in our tests.
• Tool calling: DeepSeek 3 (rank 47 of 54) vs Ministral 4 (rank 18 of 54). Ministral selects functions and arguments more accurately in multi-step tool scenarios.
• Classification: DeepSeek 3 (rank 31 of 53) vs Ministral 4 (tied for 1st of 53). Ministral is the stronger router/categorizer in our classification suite.
• Safety calibration: both score 1 (rank 32 of 55 tied). Neither model stood out for nuanced refusal/permission behavior in our tests.
• Persona consistency, multilingual, strategic analysis: ties (scores and ranks comparable). For persona maintenance and non-English quality both models were similar in our suite. Practical interpretation: choose DeepSeek when you need high faithfulness, strict structured outputs, long-context retrieval, or creative problem solving. Choose Ministral where constrained rewriting, tool orchestration, classification, image inputs, or lower output cost are the priority.

Costs are given per million tokens. DeepSeek V3.1: input $0.15/mTok, output $0.75/mTok. Ministral 3 14B 2512: input $0.20/mTok, output $0.20/mTok. Using a 50/50 input/output split as an example: per 1M total tokens DeepSeek costs $0.45 vs Ministral $0.20; per 10M: $4.50 vs $2.00; per 100M: $45.00 vs $20.00. If your workload is output-heavy (e.g., long generated responses), DeepSeek’s $0.75/mTok output charge makes it ~3.75x more expensive on output than Ministral; if your workload is input-heavy, DeepSeek can be slightly cheaper on input ( $0.15 vs $0.20). Teams generating large volumes of output tokens (chatbots, content engines) should care about the output-cost gap; experimentation or low-volume proof-of-concept users will be less affected.

Choose DeepSeek V3.1 if you need: faithful, citation-safe outputs; strict JSON/schema compliance; best-in-class long-context retrieval and creative problem solving (it won faithfulness, structured_output, long_context, creative_problem_solving and agentic_planning in our tests). Choose Ministral 3 14B 2512 if you need: lower output cost and image-capable inputs (modality: text+image->text); better constrained rewriting, tool calling and classification in our tests (it won those three). If monthly output tokens are high, favor Ministral for cost savings; if correctness and format adherence are business-critical, accept DeepSeek’s higher output cost.