DeepSeek V3.2 vs Llama 3.3 70B Instruct

Head-to-head across our 12-test suite, DeepSeek V3.2 wins 8 benchmarks, Llama 3.3 70B Instruct wins 2, and 2 tie. DeepSeek wins: structured_output (A 5 vs B 4) — DeepSeek is tied for 1st on structured output ("tied for 1st with 24 other models"), meaning better JSON/schema compliance for APIs and downstream parsers. Strategic_analysis (A 5 vs B 3) — DeepSeek ties for 1st in nuanced tradeoff reasoning, useful for pricing, finance, or tradeoff decisions. Constrained_rewriting (A 4 vs B 3) — ranks 6th of 53, so DeepSeek compresses and rewrites reliably for length-limited outputs. Creative_problem_solving (A 4 vs B 3) — DeepSeek ranks in the top third (rank 9/54), giving more useful novel ideas. Faithfulness (A 5 vs B 4) — DeepSeek ties for 1st (high fidelity to source material). Persona_consistency (A 5 vs B 3) and agentic_planning (A 5 vs B 3) — DeepSeek ties for 1st on both, indicating stronger character maintenance and goal decomposition for multi-step agents. Multilingual (A 5 vs B 4) — DeepSeek ties for 1st, better for non-English parity. Llama 3.3 70B Instruct wins: tool_calling (B 4 vs A 3) — Llama ranks 18 of 54 on tool calling versus DeepSeek’s rank 47, so Llama is better at selecting functions, arguments and sequencing in our tests (relevant for function-calling integrations). Classification (B 4 vs A 3) — Llama is tied for 1st ("tied for 1st with 29 other models"), making it preferable for routing/categorization tasks. Ties: long_context (both 5) — both tied for 1st on retrieval at 30K+ tokens; safety_calibration (both 2) — both models show similar refusal/allow behavior in our tests. External math benchmarks (Epoch AI): Llama 3.3 70B Instruct reports 41.6% on MATH Level 5 and 5.1% on AIME 2025 (Epoch AI); DeepSeek has no external math scores in the payload. These external scores are supplementary and attributed to Epoch AI.

Raw unit prices: DeepSeek V3.2 input $0.26/1M-tokens, output $0.38/1M-tokens; Llama 3.3 70B Instruct input $0.10/1M-tokens, output $0.32/1M-tokens. With a 50/50 input/output split that yields per-million-token totals of: DeepSeek $0.32/1M tokens (0.13 + 0.19) and Llama $0.21/1M tokens (0.05 + 0.16). At scale: 1M tokens/mo = DeepSeek $0.32 vs Llama $0.21; 10M = $3.20 vs $2.10; 100M = $32.00 vs $21.00. If your workload is input-heavy (long prompts, retrieval), Llama’s $0.10 input price matters more; if you generate large outputs, the output prices narrow the gap but DeepSeek still costs ~18.75% more overall (priceRatio 1.1875). Teams doing millions of tokens/month should care — switching to Llama can save roughly $11 per 100M tokens under a 50/50 assumption; for extreme input-heavy workloads savings grow proportionally.

Choose DeepSeek V3.2 if you need production-grade structured outputs, high faithfulness, strong agentic planning, persona consistency, or multilingual parity — it wins 8 of 12 benchmarks and ranks tied for 1st on structured_output, faithfulness, long_context and planning. Choose Llama 3.3 70B Instruct if you are cost-sensitive or input-heavy (input $0.10 vs DeepSeek $0.26), or if you prioritize tool calling and classification (tool_calling 4 vs 3, classification 4 vs 3). If math competition performance matters, note Llama’s external MATH Level 5 41.6% and AIME 2025 5.1% (Epoch AI).