DeepSeek-R1 vs GPT-5 nano
A complete head-to-head: pricing, context window, benchmarks, modality coverage, and openness, with a programmatic verdict synthesized from the underlying data.
Updated
DeepSeek-R1 specs · GPT-5 nano specs- PriceGPT-5 nano
GPT-5 nano is roughly 5.5× cheaper on output tokens ($0.40 vs $2.19 per 1M).
- Context windowGPT-5 nano
GPT-5 nano accepts 400K tokens vs 128K, 3.1× the room for long documents and codebases.
- BenchmarksTie
No directly comparable public benchmarks are available for both models, check the spec sheets for individual scores.
- ModalitiesGPT-5 nano
GPT-5 nano supports 2 modalities (text, vision) vs 1 for DeepSeek-R1.
- OpennessDeepSeek-R1
DeepSeek-R1 ships open weights (MIT); GPT-5 nano is API-only.
On balance GPT-5 nano edges ahead, winning 3 of 5 categories against DeepSeek-R1's 1. GPT-5 nano is roughly 5.5× cheaper on output tokens ($0.40 vs $2.19 per 1M). GPT-5 nano accepts 400K tokens vs 128K, 3.1× the room for long documents and codebases.
No directly comparable public benchmarks are available for both models, check the spec sheets for individual scores. They differ in modality coverage, DeepSeek-R1 handles text while GPT-5 nano handles text, vision, which can be the deciding factor before you even look at benchmarks. DeepSeek-R1 ships open weights (MIT); GPT-5 nano is API-only.
GPT-5 nano is the newer of the two, released 7 months after DeepSeek-R1, which usually means a more recent knowledge cutoff and updated safety post-training. DeepSeek-R1 is usually picked for reasoning and math workloads, while GPT-5 nano sees more deployments in cheapest llm and fastest llm. If pricing matters more than every last benchmark point, run the numbers in the calculator below before committing.
Side-by-side specs
| Spec | DeepSeek-R1 | GPT-5 nano |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Released | Jan 2025 | Aug 2025 |
| Modalities | text | text, vision |
| Context window | 128K tokens | 400K tokens |
| Max output | , | 128K tokens |
| Input · 1M | $0.55 / 1M tokens | $0.050 / 1M tokens |
| Output · 1M | $2.19 / 1M tokens | $0.40 / 1M tokens |
| Knowledge cutoff | 2024-07 | 2024-09 |
| Open weights | Yes (MIT) | No |
| API available | Yes | Yes |
Pricing at scale
What you'd actually pay at typical workloads. Numbers come from each model's published per-million-token rates.
- Light usage, 100k in / 50k out per day$4.94 vs $0.750
- Heavy usage, 1M in / 500k out per day$49.35 vs $7.50
- RAG workload, 5M in / 200k out per day$95.64 vs $9.90
Light usage, 100k in / 50k out per day: $4.94 vs $0.750 per month, model B comes out ahead. Heavy usage, 1M in / 500k out per day: $49.35 vs $7.50 per month, model B comes out ahead. RAG workload, 5M in / 200k out per day: $95.64 vs $9.90 per month, model B comes out ahead.
Estimated spend for the listed models at your usage. Numbers are derived from each model's published per-million-token rates.
- DeepSeek-R1$0.165
- GPT-5 nano$0.025
Benchmarks compared
Only sourced numbers. Where a benchmark is missing for one model we show the available value rather than fabricating the other.
- GPQA71.5
DeepSeek-R1 fits when…
- Open-weight reasoning model on par with o1
- MIT license
- Cheap reasoning per token
- Self-hosting and on-prem requirements, open weights (MIT).
GPT-5 nano fits when…
- Lowest-cost OpenAI model with vision support
- Fast P99 latency
- Good enough for routing and classification
- Cost-sensitive workloads, 5.5× cheaper than DeepSeek-R1 on output tokens.
- Long-context tasks, handles 400K tokens vs 128K for DeepSeek-R1.
Consider DeepSeek-V3
DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.
Frequently asked
Is DeepSeek-R1 or GPT-5 nano cheaper?
GPT-5 nano is cheaper at $0.40 / 1M tokens per million output tokens, vs $2.19 / 1M tokens for DeepSeek-R1.Which has the larger context window?
GPT-5 nano accepts 400K tokens vs 128K for DeepSeek-R1.Is DeepSeek-R1 or GPT-5 nano better for coding?
Both DeepSeek-R1 and GPT-5 nano are competitive on coding benchmarks. See each model's individual spec page for HumanEval and SWE-bench scores where published. For an opinionated pick, consult our Best LLM for Coding ranking.Are either of these models open source?
DeepSeek-R1 ships open weights (MIT). GPT-5 nano is API-only.When were DeepSeek-R1 and GPT-5 nano released?
DeepSeek-R1 was released by DeepSeek on 2025-01-20. GPT-5 nano was released by OpenAI on 2025-08-07.
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