DeepSeek V3.1 Terminus vs o4 Mini

Overview: In our 12-test suite, o4 Mini wins decisively on four metrics where scores differ: tool_calling (o4 Mini 5 vs DeepSeek 3), faithfulness (5 vs 3), classification (4 vs 3), and persona_consistency (5 vs 4). The two models tie on structured_output (5), strategic_analysis (5), constrained_rewriting (3), creative_problem_solving (4), long_context (5), safety_calibration (1), agentic_planning (4), and multilingual (5).

Tool calling: o4 Mini = 5, DeepSeek = 3. Per benchmarkDescriptions, tool_calling measures function selection, argument accuracy, and sequencing — o4 Mini’s 5 (tied for 1st with 16 others out of 54) means it is among the top models for reliable tool/agent workflows; DeepSeek’s rank (47 of 54) indicates weaker function selection and argument accuracy in our tests.

Faithfulness: o4 Mini = 5 (tied for 1st of 55), DeepSeek = 3 (rank 52 of 55). This gap signals o4 Mini sticks to source material with fewer hallucinations on tasks where factual fidelity matters.

Classification: o4 Mini = 4 (tied for 1st of 53), DeepSeek = 3 (rank 31 of 53). For routing, tagging, and decision-tree style outputs, o4 Mini is more reliable in our evaluation.

Persona consistency: o4 Mini = 5 (tied for 1st), DeepSeek = 4 (rank 38 of 53). If you need strict character/role maintenance or resistance to prompt injection, o4 Mini scored higher.

Ties and strengths: Both models score 5 on long_context and structured_output and are tied for 1st in those categories, so for retrieval across 30K+ tokens and JSON/schema compliance both perform at top levels in our tests. Strategic_analysis is 5 for both (tied for 1st), indicating comparable nuanced tradeoff reasoning with numbers. Creative_problem_solving is 4 for both (rank 9 of 54). Safety_calibration is low for both (1), and both rank 32 of 55 on that test in our suite.

External benchmarks (supplementary): o4 Mini includes external math test scores in the payload: 97.8% on MATH Level 5 and 81.7% on AIME 2025 (Epoch AI). We cite these as additional evidence of o4 Mini’s strong math/reasoning performance; DeepSeek has no external scores in the payload to compare.

Practical meaning: pick o4 Mini when correct function calls, factual sticking to sources, or classification accuracy materially affect product behavior (agents, code generation with tool calls, content routing). Pick DeepSeek when those specific failure modes are acceptable in exchange for far lower cost per token, or when you primarily rely on long-context and structured-output tasks that both models tie on.

DeepSeek V3.1 Terminus costs $0.21 input / $0.79 output per 1K tokens (combined $1.00 per 1K if you count input+output). o4 Mini costs $1.10 input / $4.40 output per 1K tokens (combined $5.50 per 1K). At 1M tokens/month (1,000 mTok) the combined cost is roughly $1,000 for DeepSeek vs $5,500 for o4 Mini; at 10M tokens it's $10,000 vs $55,000; at 100M tokens it's $100,000 vs $550,000. If you only count output tokens: 1M output tokens cost $790 (DeepSeek) vs $4,400 (o4 Mini). If only inputs: $210 vs $1,100. The price ratio in the payload (0.1795) shows DeepSeek runs at ~18% of o4 Mini for equivalent I/O. Teams doing high-volume inference or on tight budgets should consider DeepSeek; teams paying for correctness in tool usage, classification, and faithfulness will see why o4 Mini’s higher price can be justified.

Choose DeepSeek V3.1 Terminus if: you have high-volume inference or tight budgets and need top-tier long-context (5) and structured-output (5) at a fraction of the cost (combined ~$1.00 per 1K tokens). Good for large-context retrieval, schema-constrained responses, and when tool calling or strict faithfulness are secondary.

Choose o4 Mini if: you need reliable tool calling (5), strong faithfulness (5), accurate classification (4), and persona consistency (5) even at higher cost (combined ~$5.50 per 1K tokens). Ideal for agent-driven products, production tool integrations, and workflows where incorrect function choice or hallucinations carry user-facing risk.