Codestral 2508 vs Llama 3.3 70B Instruct

Head-to-head across our 12-test suite the two models split results 4 wins each with 4 ties. Detailed breakdown: - Codestral 2508 wins: faithfulness 5 vs 4 (tied for 1st of 55 models with 32 others — strong on sticking to source); structured_output 5 vs 4 (tied for 1st of 54 with 24 others — best for JSON/schema compliance); tool_calling 5 vs 4 (tied for 1st of 54 with 16 others — better function selection and args); agentic_planning 4 vs 3 (rank 16 of 54 vs Llama rank 42 — better goal decomposition/recovery). - Llama 3.3 70B Instruct wins: classification 4 vs 3 (tied for 1st of 53 with 29 others — top for routing and categorization); safety_calibration 2 vs 1 (rank 12 of 55 vs rank 32 — more likely to refuse harmful requests appropriately); strategic_analysis 3 vs 2 (rank 36 vs 44 — better nuanced tradeoff reasoning); creative_problem_solving 3 vs 2 (rank 30 vs 47 — more creative feasible ideas). - Ties: long_context 5/5 (both tied for 1st of 55 — equivalent retrieval at 30K+ tokens), constrained_rewriting 3/3 (both rank 31 of 53), persona_consistency 3/3 (both rank 45 of 53), multilingual 4/4 (both rank 36 of 55). - External math benchmarks (supplementary): Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 and 5.1% on AIME 2025 (Epoch AI) — useful if you gauge math/competition performance from third‑party measures; Codestral has no external math scores in the payload. Interpretation for tasks: choose Codestral when you need strict schema outputs, reliable tool calls, and high fidelity to source material; choose Llama for cheaper at-scale classification, safer refusals, and modest gains in reasoning creativity.

Raw per‑million-token costs from the payload: Codestral 2508 charges $0.30 per 1M input tokens and $0.90 per 1M output tokens; Llama 3.3 70B Instruct charges $0.10 per 1M input and $0.32 per 1M output. If you assume a 50/50 input/output split, cost per 1M total tokens is $0.60 for Codestral vs $0.21 for Llama. Scaled to monthly volumes (50/50): 1M tokens → Codestral $0.60, Llama $0.21; 10M → Codestral $6.00, Llama $2.10; 100M → Codestral $60, Llama $21. The payload lists a priceRatio of 2.8125, so Codestral runs roughly 2.8x more expensive in typical comparisons — important for high‑volume consumer services, chatbots, or any application with continuous inference costs. Teams focused on accuracy of structured outputs and tool orchestration may accept the premium; cost-sensitive or large-scale classification/ routing workloads should favor Llama 3.3 70B Instruct.

Choose Codestral 2508 if you need production‑grade code workflows, schema/JSON compliance, high‑quality tool calling, or stronger faithfulness and agentic planning — e.g., CI test generation, FIM/code correction, tool-enabled agents that must pass strict JSON schemas. Accept the ~2.8x cost premium for these gains. Choose Llama 3.3 70B Instruct if you must minimize inference spend or prioritize classification, safety calibration, or creative problem solving — e.g., large‑scale routing/classification, consumer chat where cost per token dominates, or applications that value safety refusals. Both tie on long context and multilingual output, so use cost and feature fit as tie-breakers.