DeepSeek V3.1 vs Llama 4 Scout

In our 12-test suite DeepSeek V3.1 wins six categories: structured_output (DeepSeek 5 vs Llama 4), faithfulness (5 vs 4), creative_problem_solving (5 vs 3), persona_consistency (5 vs 3), agentic_planning (4 vs 2), and strategic_analysis (4 vs 2). Notable specifics: DeepSeek's structured_output is 5/5 and is "tied for 1st with 24 other models out of 54 tested," meaning it reliably follows JSON/schema constraints for production-format outputs. Faithfulness is 5/5 and "tied for 1st with 32 others out of 55," so DeepSeek is less likely to hallucinate in our tests. Creative problem solving is 5 (tied for 1st), which shows stronger generation of non-obvious, feasible ideas. Llama 4 Scout wins three categories: tool_calling (4 vs 3), classification (4 vs 3), and safety_calibration (2 vs 1). Tool calling is a clear Llama advantage — Llama ranks 18 of 54 (tied) on tool_calling while DeepSeek ranks 47 of 54 — so in function selection, argument accuracy, and sequencing Llama is better in our runs. Classification is Llama's other strong suit (4/5, tied for 1st with 29 others), important for routing and tagging pipelines. Safety_calibration is 2 for Llama vs 1 for DeepSeek; Llama's rank (12 of 55) shows it rejects harmful prompts more often in our tests. The models tie on constrained_rewriting (3/3), long_context (5/5 — both tied for 1st), and multilingual (4/4). Long-context parity (both 5 and tied for 1st) means both handle 30K+ token retrieval accurately in our benchmarks, but note Llama's context_window is 327,680 vs DeepSeek's 32,768 in the payload, which matters for absolute context size. Overall, DeepSeek's wins favor strict-output, faithful, and creative tasks; Llama's wins favor tool-oriented, classification, and safer routing use cases.

Per the payload, DeepSeek V3.1 charges $0.15/mtok input + $0.75/mtok output = $0.90 combined per mtoken; Llama 4 Scout charges $0.08/mtok input + $0.30/mtok output = $0.38 combined. Assuming a 50/50 input/output token split: 1M tokens (1,000 mtok) costs DeepSeek $450/month and Llama $190/month (DeepSeek +$260). At 10M tokens: DeepSeek $4,500 vs Llama $1,900 (difference $2,600). At 100M tokens: DeepSeek $45,000 vs Llama $19,000 (difference $26,000). The priceRatio in the payload is 2.5, so DeepSeek is 2.5x more expensive per token. High-volume production apps, startups on tight budgets, and consumer-facing services should care about the Llama savings; teams that need the specific quality advantages DeepSeek demonstrates may justify the higher cost.

Choose DeepSeek V3.1 if you need production-ready structured outputs (JSON/schema), high faithfulness, strong creative problem solving, persona consistency, or better agentic planning — accept higher costs ($0.90/mtok). Choose Llama 4 Scout if you need a lower-cost model ($0.38/mtok), better tool calling and classification in our tests, multimodal input (text+image->text), or a much larger context window (327,680 vs 32,768) for extremely long documents.