Codestral 2508 vs GPT-5.1

Summary of our 12-test comparison (scores from our suite):

• Ties: faithfulness (both 5) — both models are top-ranked for sticking to source material (Codestral and GPT-5.1 tied for 1st in faithfulness). long_context (both 5) — both tied for 1st on retrieval/accuracy across 30K+ tokens. agentic_planning (both 4) — both perform similarly on goal decomposition and recovery.
• Codestral 2508 wins: structured_output 5 vs 4 (Codestral tied for 1st in structured_output) — this maps to stronger JSON/schema compliance and precise format adherence; tool_calling 5 vs 4 (Codestral tied for 1st in tool_calling) — better function selection, argument accuracy, and sequencing, which matters for code-execution pipelines and FIM workflows.
• GPT-5.1 wins: strategic_analysis 5 vs 2 (GPT-5.1 tied for 1st) — large advantage on nuanced tradeoff reasoning and numeric strategies; constrained_rewriting 4 vs 3 (GPT-5.1 ranks 6th) — better at tight-character compression; creative_problem_solving 4 vs 2 (GPT-5.1 ranks 9th) — produces more feasible, non-obvious ideas; classification 4 vs 3 (GPT-5.1 tied for 1st) — stronger routing and categorization; safety_calibration 2 vs 1 (GPT-5.1 ranks 12th) — more appropriate refusal/permissiveness balance; persona_consistency 5 vs 3 (GPT-5.1 tied for 1st) — better at maintaining characters and resisting injection; multilingual 5 vs 4 (GPT-5.1 tied for 1st) — superior non‑English parity.
• External benchmarks (Epoch AI): GPT-5.1 scores 68% on SWE-bench Verified and 88.6% on AIME 2025; Codestral has no external SWE/AIME scores in the payload. GPT-5.1's external math/coding placement (rank 7 on SWE-bench and AIME per the payload) supports its reasoning and problem-solving wins in our suite. Practical meaning: pick Codestral when you need reliable function calls, precise JSON output, long-history coding sessions, and to minimize cost. Pick GPT-5.1 when you need stronger strategic reasoning, classification, multilingual quality, persona fidelity, or better performance on external coding/math benchmarks.

Per the payload, Codestral 2508 charges $0.30 input and $0.90 output per mTok; GPT-5.1 charges $1.25 input and $10.00 output per mTok. If you assume equal input/output volume, combined per-mTok costs are $1.20 for Codestral and $11.25 for GPT-5.1 (Codestral ≈ 0.09× the price). At 1M tokens/month (1,000 mTok): Codestral ≈ $1,200 vs GPT-5.1 ≈ $11,250. At 10M tokens/month: Codestral ≈ $12,000 vs GPT-5.1 ≈ $112,500. At 100M tokens/month: Codestral ≈ $120,000 vs GPT-5.1 ≈ $1,125,000. If your workload is output-heavy (long generations), the gap widens because GPT-5.1's $10/mTok output rate dominates spend. High-volume API users, SaaS companies, and cost-conscious teams should care most about this gap; experimental or low-volume projects may prioritize GPT-5.1's accuracy and capabilities despite the cost.

Choose Codestral 2508 if: you run high-volume coding pipelines, use tool-calling or FIM extensively, require strict JSON/schema compliance, and need a low-cost model (Codestral: $0.30 input / $0.90 output per mTok). Choose GPT-5.1 if: you need the best general-purpose reasoning, stronger classification, strategic analysis, multilingual outputs, or persona consistency (GPT-5.1: $1.25 input / $10.00 output per mTok and external scores: 68% SWE-bench Verified, 88.6% AIME 2025). If budget is the primary constraint at scale, Codestral's ~9% price of GPT-5.1 is decisive; if task-critical accuracy across many dimensions matters and cost is secondary, GPT-5.1 is the winner.