Codestral 2508 vs GPT-4o

Overview: In our 12-test suite Codestral 2508 wins 4 tests, GPT-4o wins 3, and 5 tests tie. Detailed walk-through (scores shown as our 1–5 internal ratings unless otherwise noted):

• structured_output: Codestral 2508 = 5 vs GPT-4o = 4. Codestral ties for 1st in rankingsA ('tied for 1st with 24 other models out of 54 tested'), so expect stronger JSON/schema compliance and format adherence in production pipelines.

• tool_calling: Codestral 2508 = 5 vs GPT-4o = 4. Codestral ranks tied for 1st ('tied for 1st with 16 other models out of 54 tested'), meaning more reliable function selection, argument accuracy and sequencing in our tests.

• faithfulness: Codestral 2508 = 5 vs GPT-4o = 4. Codestral is tied for 1st with many models ('tied for 1st with 32 other models out of 55 tested'), so it sticks to source material and hallucinates less in our runs.

• long_context: Codestral 2508 = 5 vs GPT-4o = 4. Codestral is 'tied for 1st with 36 other models out of 55 tested' — better retrieval accuracy at 30K+ tokens in our benchmarks.

• creative_problem_solving: Codestral 2508 = 2 vs GPT-4o = 3. GPT-4o wins here (rank 30 of 54), so it produces more non-obvious feasible ideas in our tasks.

• classification: Codestral 2508 = 3 vs GPT-4o = 4. GPT-4o ties for 1st ('tied for 1st with 29 other models out of 53 tested'), indicating stronger routing and categorization performance.

• persona_consistency: Codestral 2508 = 3 vs GPT-4o = 5. GPT-4o ties for 1st ('tied for 1st with 36 other models out of 53 tested'), so it maintains character and resists injection better in our runs.

• strategic_analysis, constrained_rewriting, safety_calibration, agentic_planning, multilingual: scores tie or are equal between models. For example strategic_analysis is 2/2 (tie) and agentic_planning is 4/4 (tie).

• External benchmarks (GPT-4o only): GPT-4o scores 31% on SWE-bench Verified, 53.3% on MATH Level 5, and 6.4% on AIME 2025 (these are Epoch AI results and are shown in the payload). We reference them as supplementary: they indicate GPT-4o has measurable external performance on coding/math tasks per Epoch AI, but in our internal 1–5 proxies Codestral dominated structured output, tool calling, faithfulness and long context.

Practical meaning: choose Codestral when you need robust schema outputs, reliable function/tool use, strict faithfulness and large-context retrieval at much lower cost. Choose GPT-4o when persona fidelity, classification, or multimodal inputs (text+image+file→text) are required or when the external SWE-bench/math signals are relevant.

Per the payload, Codestral 2508 charges $0.30 input + $0.90 output per MTok = $1.20/MTok total. GPT-4o charges $2.50 input + $10.00 output = $12.50/MTok total. At scale that gap compounds: 1M tokens/month → $1.20 vs $12.50; 10M → $12.00 vs $125.00; 100M → $120.00 vs $1,250.00. Teams with heavy automated code generation, CI/test generation, or high-throughput low-latency services should care most about Codestral's cost advantage. Organizations that need GPT-4o's multimodal inputs or its classification/persona strengths may justify the ~10x higher spend for lower-volume, higher-value tasks.

Choose Codestral 2508 if you need low-latency, high-throughput coding workflows, strict JSON/schema outputs, reliable tool calling, or long-context retrieval at low cost (Codestral: $0.90 output/MTok; $1.20 total/MTok). Choose GPT-4o if you need multimodal inputs (text+image+file→text), stronger persona consistency and classification in our tests, or if you accept higher cost ($10.00 output/MTok; $12.50 total/MTok) for those capabilities.