DeepSeek V3.1 vs o4 Mini

Summary: Across our 11 shared internal tests, o4 Mini wins 4 benchmarks, DeepSeek V3.1 wins 1, and 7 are ties (winLossTie in payload). Details by test:

• Tool calling: DeepSeek V3.1 = 3 (ranked "rank 47 of 54 (6 models share this score)"), o4 Mini = 5 ("tied for 1st with 16 other models out of 54 tested"). Practically: o4 Mini is substantially more reliable for correct function selection and arguments.
• Multilingual: DeepSeek V3.1 = 4 (rank "rank 36 of 55"), o4 Mini = 5 ("tied for 1st with 34 other models out of 55"). For non‑English outputs, o4 Mini is the safer choice.
• Classification: DeepSeek V3.1 = 3 ("rank 31 of 53"), o4 Mini = 4 ("tied for 1st with 29 other models out of 53"). Routing and labeling tasks favor o4 Mini.
• Strategic analysis: DeepSeek V3.1 = 4 ("rank 27 of 54"), o4 Mini = 5 ("tied for 1st with 25 other models out of 54"). For nuanced tradeoffs and number-driven decisions, o4 Mini scored higher.
• Creative problem solving: DeepSeek V3.1 = 5 ("tied for 1st with 7 other models out of 54 tested"), o4 Mini = 4 (rank "9 of 54"). DeepSeek generates more non-obvious, feasible ideas in our tests.
• Faithfulness: both score 5 and tie (DeepSeek display: "tied for 1st with 32 other models out of 55 tested"; o4 Mini display matches). Both stick closely to source material in our testing.
• Structured output, long context, persona consistency, constrained rewriting, agentic planning, safety calibration: ties (see payload displays). Notably both models scored 5 on long_context and structured_output, so for retrieval at 30K+ tokens or strict JSON schema adherence they perform equally in our suite. External benchmarks: o4 Mini posts high external math results — on MATH Level 5 (Epoch AI) it scores 97.8% (payload) and ranks 2 of 14 per Epoch AI; on AIME 2025 (Epoch AI) it scores 81.7% and is ranked 13 of 23. These external scores support o4 Mini's strong numeric/reasoning performance outside our internal suite.

Per the payload prices, DeepSeek V3.1 charges $0.15 (input) + $0.75 (output) = $0.90 per mTok; o4 Mini charges $1.10 + $4.40 = $5.50 per mTok. Interpreting mTok as the billing unit in the payload, that translates to roughly $900 vs $5,500 per 1M tokens, $9,000 vs $55,000 per 10M, and $90,000 vs $550,000 per 100M. The ~6.1x sticker-price gap means teams with sustained, high-volume inference (10M+ tokens/month) should carefully consider DeepSeek V3.1 to contain costs; teams that need top tool-calling, multilingual or classification quality may justify o4 Mini's higher spend.

Choose o4 Mini if you need best-in-class tool calling, multilingual output, classification, or strategic analysis and can absorb higher inference costs — it wins 4 benchmarks including tool calling (5 vs 3). Choose DeepSeek V3.1 if you need a lower-cost option with top creativity and comparable faithfulness/structured-output/long-context performance — it wins creative problem solving (5 vs 4) and costs roughly $900 vs $5,500 per 1M tokens.