DeepSeek V3.2 vs Mistral Large 3 2512

Summary of head-to-head results in our 12-test suite (score scale 1–5): DeepSeek V3.2 wins 7 tests, Mistral Large 3 2512 wins 1, and 4 tests tie. Detailed walk-through (scores shown as DeepSeek vs Mistral):

• strategic_analysis: 5 vs 4 — DeepSeek wins and is tied for 1st in our ranking ("tied for 1st with 25 other models out of 54 tested"), indicating stronger nuanced tradeoff reasoning for finance, policy, or cost/benefit tasks.
• constrained_rewriting: 4 vs 3 — DeepSeek wins (rank 6/53) so it's better at tight-character compression and strict-form rewriting.
• creative_problem_solving: 4 vs 3 — DeepSeek wins (rank 9/54), meaning more useful for idea generation requiring specific, feasible suggestions.
• long_context: 5 vs 4 — DeepSeek wins and is tied for 1st ("tied for 1st with 36 other models out of 55 tested"); that shows top-tier retrieval accuracy at 30K+ token contexts even though Mistral has a larger raw context window (262,144 vs 163,840).
• safety_calibration: 2 vs 1 — DeepSeek wins (rank 12/55 vs Mistral rank 32/55), so DeepSeek better balances refusal/allow decisions in our safety tests.
• persona_consistency: 5 vs 3 — DeepSeek wins and is tied for 1st ("tied for 1st with 36 other models out of 53 tested"), useful for chatbots or role-based agents that must maintain tone and resist injection.
• agentic_planning: 5 vs 4 — DeepSeek wins and is tied for 1st ("tied for 1st with 14 other models out of 54 tested"), showing stronger goal decomposition and failure recovery in our tests.
• tool_calling: 3 vs 4 — Mistral wins here (Mistral rank 18/54 vs DeepSeek rank 47/54). The tool_calling benchmark measures function selection, argument accuracy, and sequencing; Mistral’s advantage means fewer errors when invoking external APIs or functions in multi-step tool workflows.
• structured_output, faithfulness, classification, multilingual: ties (both score 5 or 3 depending on test). For example, both models score 5 on structured_output and faithfulness, indicating equivalent JSON/schema compliance and adherence to source material in our testing. What this means in practice: pick DeepSeek when you need cheaper inference plus stronger planning, long-context retrieval, consistent personalities, and safer refusals. Pick Mistral when accurate tool invocation (function selection and arguments) is the bottleneck in your integration.

Pricing (per million tokens): DeepSeek input $0.26, output $0.38; Mistral input $0.50, output $1.50. DeepSeek output is ~25.3% of Mistral's output cost (0.38/1.50). Practical examples assuming a 50/50 split of input/output tokens: per 1M tokens DeepSeek ≈ $0.32 vs Mistral $1.00; per 10M tokens DeepSeek ≈ $3.20 vs Mistral $10.00; per 100M tokens DeepSeek ≈ $32 vs Mistral $100. If you bill or process high volumes (10M–100M tokens/month), DeepSeek saves roughly $6.80 per 10M and $68 per 100M under the 50/50 assumption. Teams with heavy output-token workloads (large responses, many generations, or image->text postprocessing where output dominates) should care most about this gap; small proof-of-concept projects will feel the difference less but still benefit from DeepSeek's lower unit costs. Note: we show input/output separately so you can recompute for your actual I/O mix.

Choose DeepSeek V3.2 if: you run high-volume production workloads and need top-tier long-context retrieval, agentic planning, persona consistency, and a much lower per-token price (output $0.38/M). It’s ideal for multi-step agents, long-document applications, and chatbots that value persona and safety calibration. Choose Mistral Large 3 2512 if: your primary failure mode is incorrect function selection or argument sequencing — Mistral scores higher on tool_calling (4 vs 3) — or you require the model’s larger raw context window or multimodal input support; expect to pay more (input $0.50/M, output $1.50/M).