DeepSeek V3.2 vs GPT-4.1

Across our 12-test suite (scores 1–5), DeepSeek V3.2 wins 4 tests, GPT-4.1 wins 3, and 5 tests tie. Detailed walk-through (scores shown are from our testing):

• Structured output: DeepSeek 5 vs GPT-4.1 4 — DeepSeek is tied for 1st (tied with 24 others) for JSON/schema compliance; that makes it a safer pick when you need exact machine-readable formats.
• Tool calling: DeepSeek 3 vs GPT-4.1 5 — GPT-4.1 is tied for 1st in tool calling, so it selects functions, arguments, and sequencing more accurately in our tests. This matters for agentic systems and tool-integrated flows.
• Long context: DeepSeek 5 vs GPT-4.1 5 — both tied for 1st on long-context retrieval in our testing; note GPT-4.1’s context_window is 1,047,576 tokens vs DeepSeek’s 163,840 tokens (payload fields). For very large document windows GPT-4.1’s token ceiling is larger.
• Persona consistency & Multilingual & Faithfulness & Strategic analysis: ties (both score 5 in our tests), indicating comparable quality for character maintenance, non-English output, fidelity to source, and nuanced tradeoff reasoning.
• Agentic planning: DeepSeek 5 vs GPT-4.1 4 — DeepSeek ranks tied 1st for goal decomposition and failure recovery; expect stronger multi-step planning in our tests.
• Constrained rewriting: DeepSeek 4 vs GPT-4.1 5 — GPT-4.1 ranks tied for 1st here, so it compresses and preserves content better when strict character or token limits apply.
• Creative problem solving: DeepSeek 4 vs GPT-4.1 3 — DeepSeek shows more non-obvious, feasible ideas in our evaluation.
• Classification: DeepSeek 3 vs GPT-4.1 4 — GPT-4.1 is tied for 1st on classification; it categorizes and routes more accurately in our tests.
• Safety calibration: DeepSeek 2 vs GPT-4.1 1 — DeepSeek refused/allowed edge cases more appropriately in our testing (rank 12 vs GPT-4.1 rank 32). Additionally, on third-party benchmarks (supplementary): GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 (Epoch AI). Those external scores provide extra context for code and math tasks but do not replace our 12-test suite results.

Raw per-million-token rates from the payload: DeepSeek V3.2 input $0.26 / output $0.38 per M tokens; GPT-4.1 input $2 / output $8 per M tokens. Using a simple 50/50 input/output split, cost per 1M total tokens is $0.32 for DeepSeek and $5.00 for GPT-4.1. At scale: 10M tokens/month costs $3.20 (DeepSeek) vs $50 (GPT-4.1); 100M tokens/month costs $32 vs $500. If your usage is output-heavy (80% output), DeepSeek runs ~$0.356/M vs GPT-4.1 ~$6.80/M; if input-heavy (80% input), DeepSeek ~$0.284/M vs GPT-4.1 ~$3.20/M. The gap matters for high-volume apps, embedded assistants, or any product with sustained token usage — DeepSeek reduces monthly inference spend by an order of magnitude in typical mixes, while GPT-4.1 is costlier but may justify the premium where its specific wins (tool calling, constrained rewriting, classification, multi-modal inputs) are critical.

Choose DeepSeek V3.2 if you need low-cost, production-scale LLM usage with best-in-class structured output, strong agentic planning, creative problem solving, and a very favorable price per token (1M tokens ≈ $0.32 at 50/50 IO). Choose GPT-4.1 if your product requires top-tier tool calling, constrained rewriting, classification, multi-modal inputs (text+image+file→text), or you rely on the external SWE-bench/MATH signals; be prepared to pay roughly $5 per 1M tokens (50/50 split) or more for those capabilities.