DeepSeek-R1-0528 vs Gemini 2.0 Flash
DeepSeek-R1-0528 significantly outperforms across most benchmarks. Gemini 2.0 Flash is 5.2x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-R1-0528 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Gemini 2.0 Flash is better at 0 benchmarks. DeepSeek-R1-0528 significantly outperforms across most benchmarks.
On price, Gemini 2.0 Flash is roughly 5.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 2.0 Flash also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you want the most recent training data — it shipped May 2025
- you need open weights you can self-host or fine-tune
Choose Gemini 2.0 Flash
- cost matters — it's about 5.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1-0528 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Gemini 2.0 Flash is better at 0 benchmarks.
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 5.0x more expensive than Gemini 2.0 Flash ($0.10/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 5.4x more expensive than Gemini 2.0 Flash ($0.40/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than Gemini 2.0 Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 2.0 Flash accepts 1,048,576 input tokens compared to DeepSeek-R1-0528's 131,072 tokens. DeepSeek-R1-0528 can generate longer responses up to 131,072 tokens, while Gemini 2.0 Flash is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Gemini 2.0 Flash supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Gemini 2.0 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Gemini 2.0 Flash
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while Gemini 2.0 Flash 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-R1-0528 was released on 2025-05-28, while Gemini 2.0 Flash was released on 2024-12-01.
DeepSeek-R1-0528 is 6 months newer than Gemini 2.0 Flash.
May 28, 2025
1.2 years ago
5mo newerDec 1, 2024
1.7 years ago
Knowledge Cutoff
When training data ends
Gemini 2.0 Flash has a documented knowledge cutoff of 2024-08-01, while DeepSeek-R1-0528's cutoff date is not specified.
We can confirm Gemini 2.0 Flash's training data extends to 2024-08-01, but cannot make a direct comparison without DeepSeek-R1-0528's cutoff date.
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Aug 2024
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Gemini 2.0 Flash is available from Google.
DeepSeek-R1-0528
Gemini 2.0 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Gemini 2.0 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Gemini 2.0 Flash.