DeepSeek V3.2 vs GPT-4.1 Nano
In our 12-test suite DeepSeek V3.2 is the better all-around pick for complex reasoning and long-context work, winning 6 tests to GPT-4.1 Nano's 1 with 5 ties. GPT-4.1 Nano is preferable when tool-calling and multimodal inputs matter or when you need slightly lower total cost at high volume.
deepseek
DeepSeek V3.2
Benchmark Scores
External Benchmarks
Pricing
Input
$0.260/MTok
Output
$0.380/MTok
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openai
GPT-4.1 Nano
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.400/MTok
modelpicker.net
Benchmark Analysis
Summary of our 12-test suite (scores listed as DeepSeek V3.2 / GPT-4.1 Nano):
- Strategic analysis: 5 vs 2 — DeepSeek wins. This test measures nuanced tradeoff reasoning; DeepSeek is tied for 1st on strategic_analysis ("tied for 1st with 25 other models out of 54 tested").
- Creative problem solving: 4 vs 2 — DeepSeek wins (rank 9 of 54 for creativity vs GPT-4.1 Nano rank 47).
- Long context: 5 vs 4 — DeepSeek wins and is tied for 1st ("tied for 1st with 36 other models out of 55 tested"); GPT-4.1 Nano supports a larger raw window (1,047,576 tokens) but scores 4 and ranks 38 in our long_context retrieval test.
- Persona consistency: 5 vs 4 — DeepSeek wins and is tied for 1st (persona_consistency "tied for 1st with 36 other models"); GPT-4.1 Nano ranks 38.
- Agentic planning: 5 vs 4 — DeepSeek wins and is tied for 1st (agentic_planning "tied for 1st with 14 other models"); GPT-4.1 Nano ranks 16.
- Multilingual: 5 vs 4 — DeepSeek wins and is tied for 1st; GPT-4.1 Nano ranks 36 on multilingual.
- Tool calling: 3 vs 4 — GPT-4.1 Nano wins. GPT-4.1 Nano ranks 18 of 54 on tool_calling ("rank 18 of 54 (29 models share this score)"), while DeepSeek ranks 47. Ties (both models): structured_output 5/5 (both tied for 1st on JSON/schema compliance), constrained_rewriting 4/4 (both rank 6 of 53), faithfulness 5/5 (both tied for 1st), classification 3/3, safety_calibration 2/2. Supplementary external benchmarks (Epoch AI): GPT-4.1 Nano scores 70% on MATH Level 5 and 28.9% on AIME 2025 (Epoch AI); DeepSeek has no external math scores in the payload. These external math results are separate signals and should be considered alongside our internal 12-test suite.
Pricing Analysis
Per 1k tokens (mTok) combined input+output costs: DeepSeek V3.2 = $0.26 + $0.38 = $0.64/mTok; GPT-4.1 Nano = $0.10 + $0.40 = $0.50/mTok. At 1M tokens/month (1,000 mTok) that's $640 vs $500; at 10M tokens it's $6,400 vs $5,000; at 100M tokens it's $64,000 vs $50,000. The gap grows linearly: switching to GPT-4.1 Nano saves $140 per 1M tokens. Teams running millions of tokens monthly (SaaS, analytics, large-scale chat) should prioritize GPT-4.1 Nano for cost savings; teams that need DeepSeek's higher scores in long-context and strategic tasks may justify the $0.14/mTok premium.
Real-World Cost Comparison
Bottom Line
Choose DeepSeek V3.2 if you need: long-document retrieval and reasoning (long_context 5/5, tied for 1st), strategic analysis (5/5, tied for 1st), agentic planning (5/5) or strong multilingual and persona consistency. Choose GPT-4.1 Nano if you need: better tool calling (4 vs 3; ranks 18 of 54), multimodal inputs (text+image+file->text), a much larger context window (1,047,576 tokens), or lower cost at scale (saves $140 per 1M tokens compared with DeepSeek).
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.