Codestral 2508 vs GPT-4.1

Overview (our testing): GPT‑4.1 wins 6 benchmarks, Codestral 2508 wins 1, and 5 are ties. Detail by test (scoreA = Codestral, scoreB = GPT‑4.1):

• Strategic analysis: 2 vs 5 — GPT‑4.1 wins and in our rankings it is tied for 1st of 54 on strategic_analysis; Codestral ranks 44 of 54. This matters for nuanced tradeoff reasoning and numeric decision tasks.
• Constrained rewriting: 3 vs 5 — GPT‑4.1 wins and is tied for 1st of 53; Codestral ranks 31. Use GPT‑4.1 when tight character/format compression is critical.
• Creative problem solving: 2 vs 3 — GPT‑4.1 wins (rank 30/54) while Codestral is lower (rank 47); expect more non‑obvious feasible ideas from GPT‑4.1.
• Classification: 3 vs 4 — GPT‑4.1 wins and is tied for 1st of 53; Codestral trails (rank 31). Routing and labeling pipelines favor GPT‑4.1.
• Persona consistency: 3 vs 5 — GPT‑4.1 wins and is tied for 1st (36 models share top); Codestral is low (rank 45). For stubborn character/role adherence, GPT‑4.1 performs better.
• Multilingual: 4 vs 5 — GPT‑4.1 wins and is tied for 1st; Codestral ranks 36. Non‑English parity favors GPT‑4.1.
• Structured output: 5 vs 4 — Codestral wins and is tied for 1st with 24 others (Codestral tied for 1st in our ranking for structured_output); GPT‑4.1 is rank 26 of 54. If JSON/schema compliance is your gating factor, Codestral is stronger in our tests.
• Tool calling, faithfulness, long context, safety_calibration, agentic_planning: ties. Notably both score 5 for faithfulness and long_context and both are tied for 1st in long_context; both score 5 on tool_calling (tied for 1st). These ties mean either model can handle large contexts and tool-selection logic in our suite. External benchmarks (Epoch AI): GPT‑4.1 scores 48.5% on SWE‑bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 — we cite Epoch AI for those results. Codestral has no external scores in the payload. Overall: GPT‑4.1 is stronger across reasoning, classification, multilingual, and persona tasks in our tests; Codestral is a standout for structured output and is much more cost‑effective.

Prices from the payload: Codestral 2508 charges $0.30 input / $0.90 output per mTok; GPT‑4.1 charges $2.00 input / $8.00 output per mTok. Assuming a 50/50 split of input vs output tokens, per 1M total tokens (1,000 mToks) Codestral ≈ $600 (500*$0.30 + 500*$0.90) vs GPT‑4.1 ≈ $5,000 (500*$2 + 500*$8). At 10M tokens/month: $6,000 vs $50,000. At 100M tokens/month: $60,000 vs $500,000. The payload also gives a priceRatio of 0.1125 — Codestral costs ~11.25% of GPT‑4.1 on the same token mix. Who should care: high‑volume applications, startups, and SaaS products with heavy token usage will see six‑figure differences at scale; research or low‑volume teams will feel the gap less but still meaningful for repeated experiments.

Choose Codestral 2508 if: you need low‑latency, cost‑sensitive text→text workloads with high JSON/schema fidelity (structured_output 5/5), large but not million‑token context needs (256k window), and want to minimize per‑token spend (≈$0.90 output mTok). Choose GPT‑4.1 if: you need top performance in strategic_analysis (5 vs 2), constrained_rewriting (5 vs 3), classification, persona consistency, multilingual tasks, or multimodal inputs (GPT‑4.1 supports text+image+file→text). Expect to pay ~8.3x more per output mTok ($8.00 vs $0.90) for those gains.