Codestral 2508 vs o3

Head-to-head scores in our 12-test suite: - o3 wins: strategic_analysis 5 vs 2 (o3 ranks “tied for 1st with 25 other models”), creative_problem_solving 4 vs 2 (o3 rank 9/54), constrained_rewriting 4 vs 3 (o3 rank 6/53), persona_consistency 5 vs 3 (o3 tied for 1st), agentic_planning 5 vs 4 (o3 tied for 1st), multilingual 5 vs 4 (o3 tied for 1st). These wins mean o3 is demonstrably stronger for nuanced tradeoff reasoning, non-obvious feasible ideas, compression into hard limits, maintaining persona, goal decomposition, and non-English parity. - Codestral 2508 wins: long_context 5 vs 4 (Codestral tied for 1st with 36 others). Practically, Codestral is better at retrieval and accuracy across very long contexts (30K+ tokens). - Ties (no clear winner): structured_output 5/5 (both tied for 1st), tool_calling 5/5 (tied for 1st), faithfulness 5/5 (tied for 1st), classification 3/3, safety_calibration 1/1. Tool calling and structured JSON-format tasks are equally strong on both models in our tests; both models show low safety_calibration scores (1/5) in our suite and should be treated cautiously on risky prompts. - External benchmarks (supplementary): o3 scores 62.3% on SWE-bench Verified (Epoch AI), 97.8% on MATH Level 5 (Epoch AI) and 83.9% on AIME 2025 (Epoch AI). Codestral 2508 has no external scores in the payload. These external results reinforce o3’s strength on advanced math and code-repair style tasks but do not override our internal 12-test comparison.

Payload prices are given per mTok. Use cost = (tokens/1000) * cost_per_mtok. Assuming a 50/50 split of input/output tokens: - At 1,000,000 total tokens/month (500k input, 500k output): Codestral 2508 = 5000.3 + 5000.9 = $600; o3 = 5002 + 5008 = $5,000. - At 10,000,000 total tokens/month: Codestral = $6,000; o3 = $50,000. - At 100,000,000 total tokens/month: Codestral = $60,000; o3 = $500,000. That gap (roughly 8.3x higher monthly spend on o3 under a 50/50 usage mix) matters most to high-volume API customers, startups, and cost-sensitive production pipelines. If your workload is output-heavy (large generated responses) the gap widens because o3's output cost ($8 per mTok) is especially high versus Codestral's $0.9 per mTok.

Choose Codestral 2508 if: - You need low-latency, high-frequency coding workflows (fill-in-the-middle, code correction, test generation per the model description), heavy long-context retrieval (long_context 5 vs 4), or you have high-volume usage where cost is critical (Codestral costs $0.3/$0.9 per mTok vs o3’s $2/$8). Choose o3 if: - You prioritize nuanced decision-making, creative problem solving, agentic planning, persona consistency, or multilingual performance (o3 wins 6 of 12 tests, including strategic_analysis 5 vs 2 and agentic_planning 5 vs 4), and you can absorb substantially higher per-token costs. If you need both long context and high-end strategic reasoning, weigh the cost tradeoff: o3 is stronger on most metrics but is materially more expensive.