DeepSeek V3.2 vs Llama 4 Scout

Overview (12-test suite, our testing): DeepSeek V3.2 wins 8 tests, Llama 4 Scout wins 2, and 2 are ties. Detailed walk-through: - Structured output: DeepSeek 5 vs Scout 4. DeepSeek ties for 1st on structured_output ("tied for 1st with 24 other models out of 54 tested"), meaning it reliably follows JSON/schema constraints — important when you need machine-parseable responses. - Strategic analysis: DeepSeek 5 vs Scout 2. DeepSeek is tied for 1st on strategic_analysis ("tied for 1st with 25 other models out of 54 tested"), so it better handles nuanced tradeoffs and numeric reasoning in our benchmarks. - Constrained rewriting: DeepSeek 4 vs Scout 3. DeepSeek ranks higher (rank 6 of 53 display) — better for tight-length transformations. - Creative problem solving: DeepSeek 4 vs Scout 3. DeepSeek ranks 9 of 54 (display) — stronger at specific, feasible idea generation in our tests. - Faithfulness: DeepSeek 5 vs Scout 4. DeepSeek is tied for 1st on faithfulness ("tied for 1st with 32 other models out of 55 tested"), so it sticks closer to source material in our evaluations. - Persona consistency: DeepSeek 5 vs Scout 3. DeepSeek is tied for 1st (display) and resists injection better in role-based prompts. - Agentic planning: DeepSeek 5 vs Scout 2. DeepSeek ties for 1st (display) while Scout is near bottom (rank 53 of 54), so DeepSeek better decomposes goals and plans recovery in our agentic tests. - Multilingual: DeepSeek 5 vs Scout 4. DeepSeek ties for 1st (display) — superior non-English parity in our suite. - Tool calling: DeepSeek 3 vs Scout 4. Llama 4 Scout wins this test; Scout ranks 18 of 54 (display) vs DeepSeek rank 47 of 54, indicating Scout is stronger at function selection, argument accuracy and sequencing in our tool-calling scenarios. - Classification: DeepSeek 3 vs Scout 4. Scout ties for 1st on classification ("tied for 1st with 29 other models out of 53 tested"), so routing/categorization tasks favor Scout in our data. - Long context: DeepSeek 5 vs Scout 5 — tie. Both are tied for 1st on long_context ("tied for 1st with 36 other models out of 55 tested"), so both handle 30K+ token retrieval similarly in our tests. - Safety calibration: DeepSeek 2 vs Scout 2 — tie (both rank 12 of 55 display). Practical meaning: DeepSeek is the safer bet for structured, faithful, and strategic outputs; Llama 4 Scout is better when you need lower cost, stronger classification, or more reliable tool-calling behavior.

Using the payload's per-mTok prices as cost per 1M tokens: DeepSeek V3.2 charges $0.26 input + $0.38 output = $0.64 per 1M tokens. Llama 4 Scout charges $0.08 input + $0.30 output = $0.38 per 1M tokens. At 1M tokens/month the bill is $0.64 (DeepSeek) vs $0.38 (Scout). At 10M/month it's $6.40 vs $3.80. At 100M/month it's $64.00 vs $38.00. The gap grows linearly: DeepSeek costs about $26 more per 100M tokens than Scout (price ratio 1.2667). High-volume deployments (millions+ tokens/month), multi-tenant services, or edge cost-sensitive products should favor Llama 4 Scout for lower operating expense; teams that need higher accuracy on structured outputs or agentic planning may accept DeepSeek's higher per-token cost.

Choose DeepSeek V3.2 if you need: - Reliable structured outputs / strict JSON or schema adherence (DeepSeek 5, tied for 1st). - High faithfulness and nuanced strategic analysis (DeepSeek 5, tied for 1st). - Strong agentic planning and persona consistency (DeepSeek 5 each). Choose Llama 4 Scout if you need: - Lower operating cost at scale ( $0.38 vs $0.64 per 1M tokens). - Better tool calling and function-selection in integrated workflows (Scout scores 4 vs DeepSeek 3 on tool_calling). - Strong classification and routing (Scout 4, tied for 1st). If you must balance both, use Scout for ingestion/classification and calls-to-tools, and DeepSeek for downstream structured generation and planning.