DeepSeek V3.1 Terminus vs Llama 3.3 70B Instruct

Summary of head-to-head results in our 12-test suite (score scale 1–5):

• Wins for DeepSeek V3.1 Terminus (A): structured output 5 vs 4, strategic analysis 5 vs 3, creative problem solving 4 vs 3, persona consistency 4 vs 3, agentic planning 4 vs 3, multilingual 5 vs 4. These are meaningful for real tasks: structured output (JSON/schema compliance) is a clear A win and A is tied for 1st in structured output (tied with 24 other models out of 54), so A is among the top performers for strict schema adherence. Strategic_analysis A is 5 (tied for 1st with 25 others), so for nuanced tradeoff reasoning and numeric tradeoffs A ranks at the top of our pool.
• Wins for Llama 3.3 70B Instruct (B): tool calling 4 vs 3, faithfulness 4 vs 3, classification 4 vs 3, safety calibration 2 vs 1. For agentic workflows that rely on tool selection and argument accuracy, B’s tool calling score (4) is substantially better; B ranks 18 of 54 on tool calling versus A’s rank 47. Classification is a strong suit for B — B ties for 1st of 53 models — so routing/categorization apps will favor Llama. On safety calibration, B ranks 12 of 55 while A is 32 of 55, meaning Llama is more likely in our testing to refuse harmful requests and handle edge safety decisions correctly.
• Ties: long context 5 vs 5 (both tied for 1st with 36 others), constrained rewriting 3 vs 3 (both mid-pack). Long-context parity means both models handle 30K+ token retrieval tasks equally well in our tests.
• Rankings context: DeepSeek’s faithfulness rank is low (rank 52 of 55), which explains its 3/5 faithfulness score; Llama’s faithfulness is better (score 4, rank 34). Creative_problem_solving favors DeepSeek (A rank 9 vs B rank 30), indicating A generates more specific, feasible ideas in our suite.
• External benchmarks: beyond our internal 1–5 tests, Llama 3.3 70B Instruct reports 41.6% on MATH Level 5 and 5.1% on AIME 2025 according to Epoch AI. Those external math scores are supplementary and should be factored independently from our 12-test results.

Per-token pricing (per million tokens): DeepSeek V3.1 Terminus charges $0.21 input and $0.79 output per mTok; Llama 3.3 70B Instruct charges $0.10 input and $0.32 output per mTok. If you measure a simple 1M-input + 1M-output workload (pairing 1M input tokens with 1M output tokens), DeepSeek costs $1.00 vs Llama $0.42. Scale that: 10M in+out = $10.00 vs $4.20; 100M in+out = $100.00 vs $42.00. The price ratio in the payload is 2.46875, meaning DeepSeek is roughly 2.47× more expensive for typical balanced I/O usage. Teams operating at millions to hundreds of millions of tokens/month (analytics platforms, high-traffic chat or summarization services) should weigh this gap: DeepSeek buys stronger structured-output, multilingual and strategic reasoning per our tests, while Llama cuts infrastructure spend substantially.

Choose DeepSeek V3.1 Terminus if you need: strict structured output (JSON/schema), top-tier strategic analysis and creative problem solving, strong multilingual outputs, or agentic planning for complex decompositions — accept ~2.47× higher per-token cost for those gains. Choose Llama 3.3 70B Instruct if you need: cheaper compute (input $0.10 / output $0.32 per mTok), better tool calling, stronger classification and safety calibration in our tests, or a lower-cost default for high-volume routing and controlled agent workflows.