DeepSeek V3.1 vs GPT-5 Mini

Across our 12-test suite, GPT-5 Mini wins five tests, DeepSeek V3.1 wins one, and six tests tie. Specifics (score: DeepSeek vs GPT-5 Mini):

• Strategic analysis: 4 vs 5 — GPT-5 Mini wins and is ranked "tied for 1st with 25 other models" on strategic_analysis, meaning it performs at the top tier for nuanced tradeoff reasoning. Useful for financial or product tradeoff prompts.
• Constrained rewriting: 3 vs 4 — GPT-5 Mini wins (rank 6 of 53, display: "rank 6 of 53"), so it handles hard character limits and compression better in our tests.
• Classification: 3 vs 4 — GPT-5 Mini wins and is tied for 1st (display: "tied for 1st with 29 others"), so it’s the safer choice for routing, tagging, and decision trees.
• Safety calibration: 1 vs 3 — GPT-5 Mini wins (rank 10 of 55) and DeepSeek scores poorly here (rank 32); in practice GPT-5 Mini is better at refusing harmful requests while permitting legitimate ones in our testing.
• Multilingual: 4 vs 5 — GPT-5 Mini wins and is tied for 1st (display: "tied for 1st with 34 others"), so non-English parity favors GPT-5 Mini.
• Creative problem solving: 5 vs 4 — DeepSeek V3.1 wins (DeepSeek tied for 1st on this test), delivering more non-obvious, feasible ideas in our evaluation. Ties (both models scored identically): structured_output 5/5 (both tied for 1st), faithfulness 5/5 (both tied for 1st), long_context 5/5 (both tied for 1st), persona_consistency 5/5 (both tied for 1st), agentic_planning 4/4 (both rank 16), and tool_calling 3/3 (both rank 47). These ties indicate parity on JSON schema compliance, faithfulness to sources, retrieval at 30K+ tokens, persona maintenance, goal decomposition, and basic function selection in our tests. External benchmarks: GPT-5 Mini also posts external results on Epoch AI tests: 64.7% on SWE-bench Verified, 97.8% on MATH Level 5, and 86.7% on AIME 2025 (reported by Epoch AI). DeepSeek V3.1 has no external benchmark scores in the payload. These third-party scores further support GPT-5 Mini's strength on coding/math-style problems.

Per the payload, DeepSeek V3.1 charges $0.15/mTok input and $0.75/mTok output; GPT-5 Mini charges $0.25/mTok input and $2.00/mTok output. For 1M tokens (1,000 mTok): DeepSeek = $150 input, $750 output, $900 combined; GPT-5 Mini = $250 input, $2,000 output, $2,250 combined. For 10M tokens: DeepSeek = $1,500 input, $7,500 output, $9,000 combined; GPT-5 Mini = $2,500 input, $20,000 output, $22,500 combined. For 100M tokens: DeepSeek = $15,000 input, $75,000 output, $90,000 combined; GPT-5 Mini = $25,000 input, $200,000 output, $225,000 combined. Who should care: any high-volume app that produces lots of output tokens (chatbots, document generation, summarization) will see large absolute savings with DeepSeek; teams that need the higher classification/safety/multilingual performance should budget the premium for GPT-5 Mini.

Choose DeepSeek V3.1 if you need a lower-cost model with strong creative problem solving, structured output, and a 32,768-token context window — pick it when token volume is high and budget is critical (it charges $0.75/mTok output). Choose GPT-5 Mini if you prioritize classification, safety calibration, multilingual parity, constrained rewriting, or multimodal inputs (text+image+file); expect to pay a premium ($2.00/mTok output) for those gains.