DeepSeek-R1 vs Gemini 2.5 Pro
Comparing DeepSeek-R1 and Gemini 2.5 Pro across benchmarks, pricing, and capabilities.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-R1 and Gemini 2.5 Pro trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-R1 is roughly 3.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 2.5 Pro also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1
- cost matters — it's about 3.6x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Gemini 2.5 Pro
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped May 2025
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 16 for Gemini 2.5 Pro
DeepSeek-R1 and Gemini 2.5 Prodon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 2.3x cheaper than Gemini 2.5 Pro ($1.25/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 4.6x cheaper than Gemini 2.5 Pro ($10.00/1M tokens).
In conclusion, Gemini 2.5 Pro is more expensive than DeepSeek-R1.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 2.5 Pro accepts 1,000,000 input tokens compared to DeepSeek-R1's 131,072 tokens. Gemini 2.5 Pro can generate longer responses up to 1,000,000 tokens, while DeepSeek-R1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Gemini 2.5 Pro supports multimodal inputs, whereas DeepSeek-R1 does not.
Gemini 2.5 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Gemini 2.5 Pro
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Gemini 2.5 Pro uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Gemini 2.5 Pro was released on 2025-05-20.
Gemini 2.5 Pro is 4 months newer than DeepSeek-R1.
Jan 20, 2025
1.7 years ago
May 20, 2025
1.4 years ago
4mo newerKnowledge Cutoff
When training data ends
Gemini 2.5 Pro has a documented knowledge cutoff of 2025-01-31, while DeepSeek-R1's cutoff date is not specified.
We can confirm Gemini 2.5 Pro's training data extends to 2025-01-31, but cannot make a direct comparison without DeepSeek-R1's cutoff date.
—
Jan 2025
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. Gemini 2.5 Pro is available from DeepInfra, Google.
DeepSeek-R1
Gemini 2.5 Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Gemini 2.5 Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Gemini 2.5 Pro.