DeepSeek V3.2 vs GPT-5.2

Overview (our 12-test suite): GPT-5.2 wins 4 tests, DeepSeek V3.2 wins 1, and 7 tests are ties. Detailed walk-through (scores are from our testing):

• Structured output: DeepSeek 5 vs GPT-5.2 4 — DeepSeek ties for 1st on structured_output ("tied for 1st with 24 other models out of 54 tested"), meaning it is the safer choice where strict JSON/schema compliance matters.
• Tool calling: GPT-5.2 4 vs DeepSeek 3 — GPT-5.2 ranks 18 of 54 (better at function selection, args, sequencing) while DeepSeek ranks 47 of 54, so expect fewer tool-selection errors with GPT-5.2 in agentic workflows.
• Classification: GPT-5.2 4 vs DeepSeek 3 — GPT-5.2 is tied for 1st in our classification ranking ("tied for 1st with 29 others"), so it is more reliable for routing and labeling tasks in our tests.
• Safety calibration: GPT-5.2 5 vs DeepSeek 2 — GPT-5.2 is tied for 1st on safety_calibration ("tied for 1st with 4 other models out of 55 tested"); DeepSeek sits at rank 12, so GPT-5.2 refused or allowed content more appropriately in our safety scenarios.
• Creative problem solving: GPT-5.2 5 vs DeepSeek 4 — GPT-5.2 ranks higher (tied for 1st) for non‑obvious, feasible ideas; DeepSeek is solid but one notch lower in our suite.
• Ties (both models score identically in our tests): strategic_analysis (5), constrained_rewriting (4), faithfulness (5), long_context (5), persona_consistency (5), agentic_planning (5), multilingual (5). For example, both models tie for 1st on long_context and persona_consistency in our rankings, so neither loses ground for retrieval over 30K+ tokens or character consistency. External benchmarks (Epoch AI): GPT-5.2 scores 73.8% on SWE-bench Verified and 96.1% on AIME 2025 — cite: "on SWE-bench Verified (Epoch AI)" and "on AIME 2025 (Epoch AI)" — these third‑party results reinforce GPT-5.2's strength on coding/math-style challenges. In short: GPT-5.2 leads on safety, tool use, classification and creative problem solving; DeepSeek leads only on strict structured-output tasks and offers equivalent performance on many other axes in our tests.

Pricing (per mTok from payload): DeepSeek V3.2 input $0.26 / output $0.38; GPT-5.2 input $1.75 / output $14.00. Assuming a 50/50 input:output token split, 1M tokens/month costs ~ $0.32 on DeepSeek and ~$7.88 on GPT-5.2. At 10M tokens/month that's ~ $3.20 vs ~$78.75; at 100M tokens/month it's ~ $32 vs ~$787.50. The result: DeepSeek is roughly 4% of GPT-5.2's cost at these volumes under a 50/50 split (driven by DeepSeek's $0.38 vs GPT-5.2's $14 output rate). Who should care: product teams, startups, and high-volume API users with tens to hundreds of millions of tokens/month — DeepSeek materially reduces run costs. Choose GPT-5.2 when the specific wins (tool-calling, safety, classification, creative problem solving) justify paying an order-of-magnitude higher per-token price.

Choose DeepSeek V3.2 if: you must enforce strict JSON/schema output (DeepSeek scores 5 on structured_output and is tied for 1st), need strong long-context/persona/faithful outputs at a fraction of the cost (DeepSeek input $0.26 / output $0.38). Choose GPT-5.2 if: you need the safest behavior, more reliable tool-calling and classification (GPT-5.2 wins those tests and ranks higher in our tool_calling and classification rankings), or you require the multimodal/formats GPT-5.2 supports (payload shows GPT-5.2 modality is text+image+file->text). Cost-sensitive, high-volume workloads favor DeepSeek; safety- and agentic-heavy production systems favor GPT-5.2.