Codestral 2508 vs GPT-5 Nano

We compare internal scores from our 12-test suite. In our testing: Codestral 2508 wins tool_calling (5 vs 4) and faithfulness (5 vs 4). Codestral's tool_calling is tied for 1st with 16 other models out of 54 tested and its faithfulness score is tied for 1st with 32 other models out of 55 — this indicates strong function selection, argument accuracy and low hallucination risk in code-related flows. GPT-5 Nano wins strategic_analysis (4 vs 2), creative_problem_solving (3 vs 2), safety_calibration (4 vs 1), persona_consistency (4 vs 3), and multilingual (5 vs 4). Notably, GPT-5 Nano's safety_calibration ranks 6 of 55 (tied with 3 others) versus Codestral's 32 of 55 — a material difference for applications that must refuse or gate harmful content. GPT-5 Nano's multilingual 5/5 ties for 1st (with 34 others), so it handles non-English output more reliably in our tests. Both models tie on structured_output (5), constrained_rewriting (3), classification (3), long_context (5), and agentic_planning (4); structured_output and long_context are tied for 1st across many models, so both are strong at JSON/format compliance and very-long context handling. Outside our suite, GPT-5 Nano also posts strong external math scores: 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI), supporting its strength on formal reasoning/math tasks.

Per the payload prices (per 1,000 tokens): Codestral 2508 input $0.30 / output $0.90; GPT-5 Nano input $0.05 / output $0.40. Per 1M tokens (1,000 × per-mtok): Codestral = $300 (1M input) or $900 (1M output); GPT-5 Nano = $50 (1M input) or $400 (1M output). Using a 50/50 input/output split yields Codestral ≈ $600 per 1M tokens versus GPT-5 Nano ≈ $225 per 1M tokens. At scale: for 10M tokens/month assume linear scaling → Codestral ≈ $6,000 vs GPT-5 Nano ≈ $2,250; for 100M → Codestral ≈ $60,000 vs GPT-5 Nano ≈ $22,500. Teams with heavy token volumes (10M+) or tight budgets should care: GPT-5 Nano reduces monthly inference spend by roughly 2.7× under a 50/50 token mix. If your workload is dominated by output tokens only, the absolute gap is larger ($900M vs $400M per million-output-token basis).

Choose Codestral 2508 if: you prioritize code-first workflows (FIM, code correction, test generation), need the highest faithfulness and best-in-class tool calling (5/5 for both in our tests), and can accept ~ $600 per 1M tokens (50/50) for better code fidelity. Choose GPT-5 Nano if: you need a lower-cost, general-purpose developer model (≈ $225 per 1M tokens at 50/50), stronger safety calibration, multilingual output, and better strategic/creative problem solving; also pick GPT-5 Nano if external math performance matters (95.2% MATH Level 5, 81.1% AIME 2025 per Epoch AI).