DeepSeek V3.1 Terminus vs GPT-5 Mini

We compare both models across our 12-test suite (scores 1–5, reported below) and include third-party math/coding data for GPT-5 Mini. Ties and wins below refer to our internal tests. - Structured output: tie — both score 5/5 and are tied for 1st in structured_output (DeepSeek: tied for 1st with 24 others; GPT-5 Mini: tied for 1st). This means both reliably follow JSON/schema constraints. - Strategic analysis: tie — both 5/5 and tied for 1st; expect similar nuanced tradeoff reasoning. - Creative problem solving: tie — both 4/5 (rank 9 of 54), so ideation quality is comparable. - Tool calling: tie — both 3/5 (rank 47 of 54); expect middling function selection and sequencing. - Long context: tie — both 5/5 and tied for 1st (DeepSeek context window 163,840; GPT-5 Mini 400,000), so both handle 30K+ retrieval tasks in our tests. - Agentic planning: tie — both 4/5 (rank 16 of 54), similar goal decomposition behaviors. - Multilingual: tie — both 5/5 and tied for 1st, good non-English parity for both models. - Constrained rewriting: GPT-5 Mini wins — 4/5 vs DeepSeek 3/5; GPT-5 Mini ranks 6 of 53 (tied with 24 others). For tight character/limit compression tasks GPT-5 Mini is measurably stronger. - Faithfulness: GPT-5 Mini wins — 5/5 vs DeepSeek 3/5; GPT-5 Mini is tied for 1st on faithfulness (rank 1 of 55). Expect fewer hallucinations and tighter source adherence in our tests. - Classification: GPT-5 Mini wins — 4/5 vs DeepSeek 3/5; GPT-5 Mini is tied for 1st in classification (rank 1 of 53), so routing/categorization is better in our suite. - Safety calibration: GPT-5 Mini wins — 3/5 vs DeepSeek 1/5; GPT-5 Mini ranks 10 of 55 for safety_calibration, DeepSeek ranks 32 of 55. GPT-5 Mini more reliably refuses harmful prompts while permitting legitimate ones. - Persona consistency: GPT-5 Mini wins — 5/5 vs DeepSeek 4/5; GPT-5 Mini is tied for 1st (rank 1 of 53). For sustained character or assistant roles GPT-5 Mini holds persona better in our tests. External benchmarks (Epoch AI) supplement these results: GPT-5 Mini scores 64.7% on SWE-bench Verified (Epoch AI), 97.8% on MATH Level 5 (Epoch AI) and 86.7% on AIME 2025 (Epoch AI) — these external metrics underline GPT-5 Mini’s strength on math and coding tasks. DeepSeek has no external SWE-bench / math scores in the payload to compare. Overall, GPT-5 Mini wins 5 categories outright in our suite; 7 categories are ties; DeepSeek wins none outright.

DeepSeek V3.1 Terminus charges $0.21/mTok input and $0.79/mTok output; GPT-5 Mini charges $0.25/mTok input and $2.00/mTok output. At 1M tokens/month (1,000 mTok): DeepSeek = $210 input + $790 output = $1,000 total; GPT-5 Mini = $250 input + $2,000 output = $2,250 total — a $1,250/month gap. At 10M tokens: DeepSeek ≈ $10,000 vs GPT-5 Mini ≈ $22,500 (gap $12,500). At 100M tokens: DeepSeek ≈ $100,000 vs GPT-5 Mini ≈ $225,000 (gap $125,000). Teams with high query volume or tight unit-economics (SaaS chat, large-scale generation pipelines) should care about DeepSeek’s lower operating cost; teams needing higher faithfulness, safety, multimodal inputs or top-tier math/coding accuracy may accept GPT-5 Mini’s higher cost.

Choose DeepSeek V3.1 Terminus if you need a cost-efficient, text-only model with a very large context (163,840 tokens), reliable structured-output performance, and you expect high monthly throughput where $0.79/mTok output materially reduces operating costs. Choose GPT-5 Mini if you need stronger faithfulness, safety calibration, classification, persona consistency, or top-tier math/coding results (97.8% on MATH Level 5 per Epoch AI) and can absorb the higher output cost ($2.00/mTok) and gain multimodal inputs and a 400,000-token context window.