Codestral 2508 vs Llama 4 Scout

We ran a 12-test suite and compared scores (1-5). Wins, ties and context: Codestral 2508 wins 4 tests — structured_output 5 vs 4 (Codestral tied for 1st with 24 others out of 54), tool_calling 5 vs 4 (Codestral tied for 1st with 16 others of 54), faithfulness 5 vs 4 (Codestral tied for 1st with 32 others of 55), and agentic_planning 4 vs 2 (Codestral ranks 16 of 54). Those wins indicate Codestral is stronger at JSON/schema outputs, function selection/arguments, sticking to source material, and goal decomposition/recovery — useful for APIs, tool-enabled agents, and code-generation pipelines. Llama 4 Scout wins 3 tests — creative_problem_solving 3 vs 2 (better for specific idea generation), classification 4 vs 3 (Llama tied for 1st with 29 others out of 53, so best-in-class for routing/labeling), and safety_calibration 2 vs 1 (Llama ranks 12 of 55 on safety_calibration, so it refuses harmful requests more reliably in our tests). Five tests tie: strategic_analysis (2/2), constrained_rewriting (3/3), long_context (5/5 tied for 1st with 36 others), persona_consistency (3/3), and multilingual (4/4). Long_context parity at 5/5 means both handle 30K+ token retrieval well. Practical takeaway: choose Codestral when you need exact structured outputs, low-latency tool orchestration, and high faithfulness; choose Llama 4 Scout when you need cheaper inference, stronger classification and safer refusals. All benchmark claims are from our 12-test suite.

Costs are materially different. Prices are per 1k tokens (mTok): Codestral 2508 input $0.30 / output $0.90; Llama 4 Scout input $0.08 / output $0.30. Using a simple 50/50 input/output split: at 1M tokens/month Codestral ≈ $600 vs Llama ≈ $190 (difference $410). At 10M tokens/month Codestral ≈ $6,000 vs Llama ≈ $1,900 (difference $4,100). At 100M tokens/month Codestral ≈ $60,000 vs Llama ≈ $19,000 (difference $41,000). The priceRatio in the payload is 3x overall; high-volume API consumers, startups, and cost-optimized production deployments should care most — Llama 4 Scout cuts monthly bill by roughly two-thirds under these assumptions. Low-latency, high-value tasks that need Codestral's strengths may justify the premium.

Choose Codestral 2508 if you need reliable JSON/schema outputs, best-in-class tool calling and high faithfulness for code generation, tool-enabled agents, or mission-critical structured APIs and can absorb higher costs. Choose Llama 4 Scout if you need a lower-cost model with strong classification and better safety calibration, or if you want multimodal text+image->text support while minimizing monthly spend.