Codestral 2508 vs DeepSeek V3.2
DeepSeek V3.2 is the better all-around choice for reasoning, agentic planning, multilingual and persona-sensitive applications — it wins 7 of 12 benchmarks in our testing. Codestral 2508 is the pick for function selection and coding-agent workflows (tool_calling 5 vs 3) but comes at a higher price: 0.90 vs 0.38 per mTok output.
mistral
Codestral 2508
Benchmark Scores
External Benchmarks
Pricing
Input
$0.300/MTok
Output
$0.900/MTok
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deepseek
DeepSeek V3.2
Benchmark Scores
External Benchmarks
Pricing
Input
$0.260/MTok
Output
$0.380/MTok
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Benchmark Analysis
Summary of head-to-head scores in our 12-test suite (scores 1-5):
- Wins for DeepSeek V3.2 (in our testing): strategic_analysis 5 vs 2 (DeepSeek ranks tied for 1st of 54), constrained_rewriting 4 vs 3 (DeepSeek rank 6/53), creative_problem_solving 4 vs 2 (DeepSeek rank 9/54), safety_calibration 2 vs 1 (DeepSeek rank 12/55), persona_consistency 5 vs 3 (DeepSeek tied for 1st of 53), agentic_planning 5 vs 4 (DeepSeek tied for 1st of 54), multilingual 5 vs 4 (DeepSeek tied for 1st of 55). These wins show DeepSeek is stronger for nuanced reasoning, persona-locked dialogue, failure recovery and multilingual outputs — important for assistants, analysis tools, and cross-language products.
- Wins for Codestral 2508 (in our testing): tool_calling 5 vs 3 — Codestral is tied for 1st on tool_calling (ranked tied for 1st with 16 others), meaning better function selection, argument accuracy and sequencing in our tests; this aligns with its coding-focused description.
- Ties: structured_output 5/5 (both tied for 1st), faithfulness 5/5 (both tied for 1st), classification 3/3, long_context 5/5 (both tied for 1st). Practically this means both models are equally strong at JSON/schema outputs, sticking to source material, basic routing/classification, and retrieval at 30K+ tokens in our benchmarks.
- Ranks matter: Codestral's top ranking on tool_calling is meaningful for code-generation agents and test-generation automation; DeepSeek's top ranks on strategic_analysis, agentic_planning and persona_consistency matter for multi-step planning, safe behavior, and consistent conversational agents. Safety_calibration remains low in absolute terms for both (1 vs 2), so extra guardrails are recommended.
Pricing Analysis
Using the model's listed per-mTok prices: Codestral 2508 charges input $0.30 + output $0.90 = $1.20 per mTok blended; DeepSeek V3.2 charges input $0.26 + output $0.38 = $0.64 per mTok blended. If you assume a 50/50 split of input/output tokens, costs are: for 1M tokens/month — Codestral ≈ $600, DeepSeek ≈ $320; for 10M — Codestral ≈ $6,000, DeepSeek ≈ $3,200; for 100M — Codestral ≈ $60,000, DeepSeek ≈ $32,000. Note the output-only cost gap is larger: Codestral output is $0.90/mTok vs DeepSeek $0.38/mTok (2.368×). High-volume deployments, LLM-powered SaaS, and teams running many code-generation or agentic calls should care most about this gap; small-scale experimentation costs are modest but scale quickly with traffic.
Real-World Cost Comparison
Bottom Line
Choose Codestral 2508 if: you run high-frequency coding workflows, FIM or automated test-generation where function selection and low-latency tool-calling matter (tool_calling 5 vs 3) and you accept higher per-token spend. Choose DeepSeek V3.2 if: you need stronger strategic reasoning, agentic planning, persona consistency and multilingual output (DeepSeek wins 7 of 12 benchmarks), and you want materially lower costs per token (output $0.38 vs $0.90 per mTok). If budget is a primary constraint at scale, DeepSeek delivers similar structured output and faithfulness while costing roughly half per blended mTok.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.