Codestral 2508 vs GPT-5.2

In our 12-test suite, GPT-5.2 wins the majority: strategic_analysis (GPT-5.2 5 vs Codestral 2), constrained_rewriting (4 vs 3), creative_problem_solving (5 vs 2), classification (4 vs 3), safety_calibration (5 vs 1), persona_consistency (5 vs 3), agentic_planning (5 vs 4), and multilingual (5 vs 4). Codestral 2508 wins structured_output (5 vs 4) and tool_calling (5 vs 4). Faithfulness and long_context tie at 5/5 each. What this means in practice: GPT-5.2's higher strategic_analysis and agentic_planning scores translate to better performance on nuanced tradeoff reasoning and multi-step goal decomposition (GPT-5.2 is tied for 1st in strategic_analysis and agentic_planning in our rankings). Its safety_calibration 5/5 (tied for 1st) makes it far more reliable at refusing harmful requests and permitting legitimate ones compared to Codestral's 1/5 (rank 32 of 55). GPT-5.2 also tops external math benchmarks in the payload: 73.8% on SWE-bench Verified and 96.1% on AIME 2025 (both from Epoch AI), which supports its strength on coding/math problem solving. Codestral wins where format and tooling matter: its 5/5 structured_output is tied for 1st (tied with 24 others) and tool_calling 5/5 is tied for 1st (tied with 16 others), so for JSON-schema compliance, FIM, code correction and reliable function selection in high-frequency code tasks, Codestral is preferable. Rankings context: Codestral is tied for 1st in faithfulness, structured_output, long_context and tool_calling in our tests; GPT-5.2 is tied for 1st in multiple categories (faithfulness, persona_consistency, agentic_planning, strategic_analysis, long_context, creative_problem_solving, classification, multilingual, safety_calibration). Note: GPT-5.2 supports text+image+file->text modality in the payload while Codestral is text->text; that modality difference matters for multimodal workflows.

Per the payload rates: Codestral 2508 charges $0.30 per mTok input and $0.90 per mTok output; GPT-5.2 charges $1.75 per mTok input and $14.00 per mTok output. Assuming a 50/50 split of tokens (input vs output), 1M tokens/month (1,000 mTok total => 500 mTok input + 500 mTok output) costs: Codestral ≈ $600 (500×$0.30 + 500×$0.90) vs GPT-5.2 ≈ $7,875 (500×$1.75 + 500×$14.00). At 10M tokens/month multiply those by 10: Codestral ≈ $6,000 vs GPT-5.2 ≈ $78,750. At 100M tokens/month: Codestral ≈ $60,000 vs GPT-5.2 ≈ $787,500. The cost gap matters for high-throughput services, continuous integration/test generation, and startups running large-scale chat or coding pipelines; Codestral reduces compute spend by an order of magnitude in these scenarios. Teams needing the capabilities where GPT-5.2 leads (agentic planning, safety-sensitive apps, advanced multilingual/creative tasks) may justify the higher spend.

Choose Codestral 2508 if you need low-latency, high-throughput coding workflows (FIM, code correction, test generation), strict structured-output compliance, and a much lower cost-per-token — it's the pragmatic choice for engineering pipelines and scale. Choose GPT-5.2 if your priority is multi-step reasoning, agentic planning, safety-critical behavior, multilingual or creative problem-solving, or if you need multimodal input (text+image+file->text) — it wins the majority of benchmarks in our testing despite much higher cost.