DeepSeek V3.1 Terminus vs Mistral Large 3 2512

Across our 12-test suite DeepSeek V3.1 Terminus wins 4 tests, Mistral Large 3 2512 wins 2, and 6 are ties. Detailed walk-through (scores are our 1–5 proxies and ranks are from our test pool):

• Long context: DeepSeek 5 vs Mistral 4. DeepSeek is tied for 1st of 55 models ("tied for 1st with 36 other models"), while Mistral ranks 38 of 55. This means DeepSeek is noticeably stronger when retrieving or reasoning over 30K+ token documents in our tests.

• Strategic analysis: DeepSeek 5 vs Mistral 4. DeepSeek ties for 1st of 54, Mistral ranks 27 of 54 — DeepSeek produced better nuanced tradeoff reasoning with numbers in our scenarios.

• Creative problem solving: DeepSeek 4 vs Mistral 3. DeepSeek ranks 9 of 54 (shared), indicating stronger generation of non-obvious, feasible ideas in our tests.

• Persona consistency: DeepSeek 4 vs Mistral 3. DeepSeek ranks 38 of 53 (score shared by 7 models); Mistral ranks 45 of 53 — DeepSeek held character and resisted injection better in our prompts.

• Tool calling: Mistral 4 vs DeepSeek 3. Mistral ranks 18 of 54 (broadly competitive) while DeepSeek ranks 47 of 54; in our function-selection and argument-accuracy tasks Mistral picked correct functions and argument values more often.

• Faithfulness: Mistral 5 vs DeepSeek 3. Mistral ties for 1st of 55 (with 32 others); DeepSeek ranks 52 of 55 — Mistral sticks to source material far more reliably in our tests.

• Structured output: tie at 5 and both tied for 1st of 54. Both models follow JSON/schema constraints at top-tier levels in our tests.

• Constrained rewriting, classification, safety calibration, agentic planning, multilingual: ties (same numeric scores). Notably both score 1 on safety calibration and rank 32 of 55 — both models were conservative on harmful/allowed prompts in our suite. Agentic planning is 4 for both and ranks 16 of 54, so decomposing goals and recovery were similar.

Implication for real tasks: pick DeepSeek when you need long-document retrieval, complex numeric reasoning, or idea generation at lower cost. Pick Mistral when you need reliable faithfulness or stronger tool-calling behavior, or text+image->text capabilities (its modality is text+image->text in the payload).

Raw per‑mTok rates: DeepSeek V3.1 Terminus charges $0.21 input / $0.79 output per mTok; Mistral Large 3 2512 charges $0.50 input / $1.50 output per mTok. Assuming a 50/50 split between input and output tokens (explicit assumption): for 1M total tokens/month DeepSeek ≈ $500 (500 mTok input = $105; 500 mTok output = $395) vs Mistral ≈ $1,000 (500 mTok input = $250; 500 mTok output = $750). Scale that linearly: 10M tokens → DeepSeek ≈ $5,000 vs Mistral ≈ $10,000; 100M tokens → DeepSeek ≈ $50,000 vs Mistral ≈ $100,000. Who should care: startups, consumer apps, and high-volume pipelines will save ~50% on token spend with DeepSeek; teams that need Mistral’s tool-calling or faithfulness advantages should budget for roughly 1.9–2.4× higher per-token cost (Mistral input is 0.5/0.21≈2.38×, output is 1.5/0.79≈1.90×).

Choose DeepSeek V3.1 Terminus if you need: long-context retrieval and reasoning (5/5, tied for 1st), strategic analysis (5/5, tied for 1st), creative problem solving (4/5), structured outputs (5/5) — and you want much lower token costs ($0.21/$0.79 per mTok). Choose Mistral Large 3 2512 if you need: stronger tool calling (4/5, rank 18 of 54), top-tier faithfulness (5/5, tied for 1st), or text+image->text capability and you can accept roughly 2× token spend. If you need both long-context and best-in-class faithfulness, plan to prototype on both and weigh token cost vs the specific failure modes in your app.