Codestral 2508 vs GPT-4.1 Nano

Summary of our 12-test head-to-head (in our testing): Codestral 2508 wins tool_calling (5 vs 4) and long_context (5 vs 4); GPT-4.1 Nano wins constrained_rewriting (4 vs 3), safety_calibration (2 vs 1) and persona_consistency (4 vs 3). The remaining tests tie: structured_output (5/5), strategic_analysis (2/2), creative_problem_solving (2/2), faithfulness (5/5), classification (3/3), agentic_planning (4/4), multilingual (4/4). Context and ranking notes from our dataset: Codestral’s tool_calling and long_context scores each rank tied for 1st among tested models (tool_calling: "tied for 1st with 16 other models"; long_context: "tied for 1st with 36 other models"), which explains why it’s stronger at function selection, argument accuracy, sequencing, and retrieval across 30K+ tokens — important for large codebases, fill-in-the-middle (FIM) workflows and test generation. GPT-4.1 Nano’s constrained_rewriting ranks 6th of 53 (tied with 24 others), and safety_calibration ranks 12th of 55; that indicates GPT is better at tight-character compression tasks and safer refusal/permitted-request behavior in our tests. Both models tie at the top for structured_output and faithfulness (each "tied for 1st"), so neither sacrifices schema compliance or sticking to source material. Additional external data: beyond our internal scores, GPT-4.1 Nano scores 70% on MATH Level 5 (Epoch AI) and 28.9% on AIME 2025 (Epoch AI), which we list as supplementary external benchmarks (Epoch AI).

Raw per-token rates from the payload: Codestral 2508 input $0.30/mTok, output $0.90/mTok; GPT-4.1 Nano input $0.10/mTok, output $0.40/mTok. Interpreting mTok as 1,000 tokens gives per-million-token costs: Codestral input $300, output $900; GPT input $100, output $400. Assuming a 50/50 input/output split, per-million token cost = Codestral $600 vs GPT $250. At scale: 10M tokens/month ≈ Codestral $6,000 vs GPT $2,500; 100M tokens/month ≈ Codestral $60,000 vs GPT $25,000. The ~2.25× price ratio (payload priceRatio) matters for high-volume APIs, SaaS businesses, and cost-sensitive deployment; teams optimizing latency/throughput for code generation may accept Codestral’s higher bills, while product teams with heavy user traffic should favor GPT-4.1 Nano to lower operational spend.

Choose Codestral 2508 if you need low-latency, high-frequency coding features (FIM, code correction, test generation) and very large-context handling — it wins tool calling and long-context in our tests, and its 256k context window supports massive code contexts. Choose GPT-4.1 Nano if you need the lowest per-token cost, better safety calibration and constrained-rewriting, multimodal inputs, or stronger persona consistency — it wins more head-to-head benchmarks (3 vs 2), costs about $250 per 1M tokens (50/50 split) versus Codestral’s ~$600, and carries external math benchmark scores (MATH Level 5 70%, AIME 2025 28.9% according to Epoch AI).