DeepSeek V3.1 Terminus vs Llama 4 Scout

Across our 12-test suite (scores 1–5), DeepSeek V3.1 Terminus wins 6 tests, Llama 4 Scout wins 4, and 2 tie. Detailed walkthrough (score A = DeepSeek, B = Llama):

• Structured output: A 5 vs B 4 — DeepSeek tied for 1st with 24 others on JSON/schema compliance, so expect reliable format adherence for integrations and APIs. (DeepSeek ranks tied for 1st of 54.)
• Strategic analysis: A 5 vs B 2 — DeepSeek is far stronger at nuanced tradeoff reasoning (tied for 1st of 54); use it for financial tradeoffs or multi-constraint planning.
• Creative problem solving: A 4 vs B 3 — DeepSeek ranks 9th of 54, producing more feasible, non-obvious ideas in our tests.
• Persona consistency: A 4 vs B 3 — DeepSeek maintains character better (rank 38/53 vs 45/53), useful for branding and role-based assistants.
• Agentic planning: A 4 vs B 2 — DeepSeek (rank 16/54) decomposes goals and handles failure recovery better in our scenarios; Llama lags (rank 53/54).
• Multilingual: A 5 vs B 4 — DeepSeek tied for 1st (55 tested), better parity across languages in our evaluations.
• Tool calling: A 3 vs B 4 — Llama 4 Scout wins, ranking 18/54 vs DeepSeek 47/54; it selects functions and sequences arguments more accurately, so prefer it for tool-integrated agents.
• Faithfulness: A 3 vs B 4 — Llama is better at sticking to source material (rank 34/55 vs DeepSeek rank 52/55), reducing hallucination risk for factual tasks.
• Classification: A 3 vs B 4 — Llama tied for 1st (with 29 others) on routing/categorization accuracy; choose Llama for high-throughput classifiers.
• Safety calibration: A 1 vs B 2 — Llama is more conservative/accurate on refusals (rank 12/55 vs DeepSeek 32/55), relevant for moderation-sensitive apps.
• Constrained rewriting: tie 3 vs 3 — both rank 31/53; neither is a clear leader for hard character-limited compression tasks.
• Long context: tie 5 vs 5 — both tied for 1st (55 tested). Note: Llama 4 Scout reports a larger context window (327,680 tokens) vs DeepSeek 163,840 and also supports text+image->text modality, which may matter for multimodal long-context workflows despite the tie in our retrieval test. In short: DeepSeek dominates reasoning, structured format fidelity, creativity, agent planning, persona, and multilingual; Llama wins where safety, classification, and tool-calling matter.

Pricing (per 1k tokens): DeepSeek V3.1 Terminus input $0.21 / output $0.79; Llama 4 Scout input $0.08 / output $0.30. Assuming a 50/50 split of input vs output tokens, monthly costs are: 1M tokens — DeepSeek $500 vs Llama $190 (DeepSeek +$310); 10M tokens — DeepSeek $5,000 vs Llama $1,900 (DeepSeek +$3,100); 100M tokens — DeepSeek $50,000 vs Llama $19,000 (DeepSeek +$31,000). If your app is high-volume (10M+ tokens/mo) or cost-sensitive (startups, consumer apps), the $0.19 vs $0.50 per 1k-token effective price matters — Llama 4 Scout is materially cheaper. If you prioritize highest-ranked strategic analysis, structured-output fidelity, or multilingual quality and can absorb the premium, DeepSeek justifies the cost.

Choose DeepSeek V3.1 Terminus if you need best-in-class strategic reasoning, precise structured outputs (JSON/schema), stronger multilingual support, and better agentic planning — and you can pay the premium. Choose Llama 4 Scout if you need a lower-cost option with superior tool-calling, classification/routing, and safer refusals, or if you require multimodal (text+image->text) inputs and a larger context window.