DeepSeek V3.1 vs o3

Wins, ties, and what they mean in practice (our 12-test suite):

• o3 wins (5 tests): strategic_analysis 5 vs 4 (o3 tied for 1st of 54 on strategic_analysis), agentic_planning 5 vs 4 (o3 tied for 1st of 54), tool_calling 5 vs 3 (o3 tied for 1st of 54; DeepSeek ranks 47/54), constrained_rewriting 4 vs 3 (o3 ranks 6/53), multilingual 5 vs 4 (o3 tied for 1st of 55). Practical takeaway: o3 is measurably stronger at function selection/sequencing, long-lived plans/agents, formal compression tasks, and non-English parity — critical for tool-integrated apps and agentic workflows.
• DeepSeek V3.1 wins (2 tests): creative_problem_solving 5 vs 4 (DeepSeek tied for 1st of 54) and long_context 5 vs 4 (DeepSeek tied for 1st of 55). Practical takeaway: DeepSeek shines when you need retrieval accuracy across very long prompts or higher-ranked novel idea generation under constraints.
• Ties (5 tests): structured_output (both 5, tied for 1st), faithfulness (both 5, tied for 1st), classification (both 3), safety_calibration (both 1, low), persona_consistency (both 5, tied for 1st). Meaning: both models are equally reliable at schema compliance and faithfulness in our tests, but both scored poorly on safety calibration in our suite.
• External benchmarks (Epoch AI): o3 scores 62.3 on SWE-bench Verified, 97.8 on MATH Level 5, and 83.9 on AIME 2025. Cite: on SWE-bench Verified (Epoch AI) o3 = 62.3%; on MATH Level 5 (Epoch AI) o3 = 97.8%; on AIME 2025 (Epoch AI) o3 = 83.9%. These external scores corroborate o3's strength on technical/math tasks. DeepSeek has no external benchmark scores in the payload. Overall interpretation: o3 is the stronger, more capable model for agentic/tool-enabled, multilingual, and strategic tasks at the cost of dramatically higher token pricing. DeepSeek is an expensive-performance outlier in cost: matching or exceeding long-context and creative problem-solving ability in our tests while being far cheaper.

DeepSeek V3.1 input/output: $0.15/$0.75 per mTok. o3 input/output: $2/$8 per mTok. Per 1M tokens (1,000 mTok): DeepSeek = $150 (input) and $750 (output) — $900 combined for 1M in+1M out; o3 = $2,000 (input) and $8,000 (output) — $10,000 combined. At 10M in+out: DeepSeek ≈ $9,000 vs o3 ≈ $100,000. At 100M in+out: DeepSeek ≈ $90,000 vs o3 ≈ $1,000,000. If you generate mostly output (1M output only) costs are $750 (DeepSeek) vs $8,000 (o3). High-volume apps, consumer-facing chatbots, and startups should care about this gap: o3 can be 11×–12× more expensive per token in these common comparisons (priceRatio in payload is 0.09375 favoring DeepSeek).

Choose DeepSeek V3.1 if: you need very long-context retrieval (long_context 5), top-tier creative problem solving (creative_problem_solving 5), or you have tight cost constraints — DeepSeek costs $0.15/$0.75 per mTok vs o3's $2/$8. Choose o3 if: you require best-in-class tool calling (tool_calling 5, tied for 1st), agentic planning (5), strategic analysis (5), constrained rewriting (4), or multilingual parity (5) and you can absorb much higher token costs; o3 also posts strong external math scores (MATH Level 5 = 97.8%, Epoch AI).