DeepSeek V3.1 vs GPT-5.2

Across our 12-test suite, GPT-5.2 wins 7 benchmarks, DeepSeek V3.1 wins 1, and 4 are ties (winLossTie). Detailed walk-through (scores are our test values; ranks reference our model pool):

• Strategic analysis: GPT-5.2 5 vs DeepSeek 4 — GPT-5.2 wins and is ranked tied for 1st of 54 models, meaning it's better at nuanced numeric tradeoffs in practice.
• Constrained rewriting: GPT-5.2 4 vs DeepSeek 3 — GPT-5.2 (rank 6/53) handles tight character compression more reliably.
• Tool calling: GPT-5.2 4 vs DeepSeek 3 — GPT-5.2 (rank 18/54) is better at selecting functions and arguments; DeepSeek's rank is low (47/54). This matters for agentic flows and automation.
• Classification: GPT-5.2 4 vs DeepSeek 3 — GPT-5.2 ranks tied for 1st (1/53) and will route/categorize more accurately in our tests.
• Safety calibration: GPT-5.2 5 vs DeepSeek 1 — large gap; GPT-5.2 is tied for 1st (1/55) and will more consistently refuse harmful prompts while permitting legitimate ones.
• Agentic planning: GPT-5.2 5 vs DeepSeek 4 — GPT-5.2 tied for 1st (1/54), so it decomposes objectives and recovers from failures better in our scenarios.
• Multilingual: GPT-5.2 5 vs DeepSeek 4 — GPT-5.2 tied for 1st (1/55), giving it an edge for non-English production.

Wins for DeepSeek V3.1:

• Structured output: DeepSeek 5 vs GPT-5.2 4 — DeepSeek is tied for 1st (1/54) on JSON schema compliance in our tests, making it the superior choice when strict format adherence matters.

Ties (both models score 5 in our tests): creative problem solving, faithfulness, long context, and persona consistency — both models are tied for top ranks on those axes (see tied rank displays). Notable external results: GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI) and ranks 5 of 12 there, and 96.1% on AIME 2025 (Epoch AI) ranking 1 of 23 — these external benchmarks (Epoch AI) reinforce GPT-5.2's strengths on coding/math-style tasks.

Practical meaning: choose GPT-5.2 for high-assurance planning, safety, classification, multilingual and tool-driven workflows; choose DeepSeek for strict structured outputs or when cost per token is the controlling constraint.

Per the payload, DeepSeek V3.1 charges $0.15 input + $0.75 output = $0.90 per mTok. GPT-5.2 charges $1.75 input + $14.00 output = $15.75 per mTok. At 1M tokens/month (1,000 mTok) the monthly bill is $900 (DeepSeek) vs $15,750 (GPT-5.2). At 10M tokens (10,000 mTok) it's $9,000 vs $157,500. At 100M tokens (100,000 mTok) it's $90,000 vs $1,575,000. Teams with high-volume, cost-sensitive workloads (chatbots at scale, bulk generation) should prefer DeepSeek to avoid 10–20x+ cost multiplier. Organizations that need top-tier agentic planning, strict safety calibration, or best-in-class external math/coding bench results may justify GPT-5.2's higher raw spend.

Choose DeepSeek V3.1 if: you need strict structured-output fidelity (DeepSeek 5 vs GPT-5.2 4), long-context and persona fidelity at far lower cost ($0.90/mTok), or you must run high-volume workloads where every dollar matters. Choose GPT-5.2 if: you require best-in-class agentic planning, safety calibration, classification, multilingual support, or external benchmark performance (73.8% SWE-bench Verified and 96.1% AIME 2025 per Epoch AI) and can absorb ~$15.75/mTok pricing.