GPT-4.1 nano vs o3 Deep Research
Compare OpenAI and OpenAI AI models
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
GPT-4.1 nano
o3 Deep Research
Cost Differences
o3 Deep Research costs more than GPT-4.1 nano
Feature Comparison
| Feature | GPT-4.1 nano | o3 Deep Research |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input Price | $0.10/1M tokens | $10.00/1M tokens |
| Output Price | $0.40/1M tokens | $40.00/1M tokens |
| Context Window | 128,000 tokens | 200,000 tokens |
| Max Output | 8,192 tokens | 32,768 tokens |
| Category | efficient | reasoning |
| Capabilities | text | textreasoningvisioncode |
| Release Date | 4/14/2025 | 6/26/2025 |
GPT-4.1 nano vs o3 Deep Research: Which Should You Choose?
Choosing between GPT-4.1 nano and o3 Deep Research depends on your priorities: cost efficiency, context length, or raw capability. GPT-4.1 nano is the more affordable option at $0.10/1M input tokens — 99% cheaper than o3 Deep Research. Meanwhile, o3 Deep Research offers a significantly larger context window at 200,000 tokens vs 128,000 for GPT-4.1 nano.
These models target different tiers: GPT-4.1 nano is a efficient model while o3 Deep Research is reasoning. This means they're optimized for different workloads. o3 Deep Research targets more demanding workloads, while GPT-4.1 nano provides a cost-effective option for everyday tasks.
Output costs matter too. GPT-4.1 nano charges $0.40/1M output tokens vs $40.00 for o3 Deep Research. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. GPT-4.1 nano has the edge here at $0.40/1M output tokens.
Multimodal capabilities: o3 Deep Research supports vision (image inputs) while GPT-4.1 nano is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose GPT-4.1 nano when:
- • Budget is a primary concern
- • You're already using OpenAI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Choose o3 Deep Research when:
- • You need a larger context window (200,000 tokens)
- • You need more capabilities (reasoning, vision, code)
- • You need longer outputs (up to 32,768 tokens)
- • You're already using OpenAI's API ecosystem
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
GPT-4.1 nano (OpenAI)
o3 Deep Research (OpenAI)
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