DeepSeek-V2.5 vs Gemini 1.5 Flash
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks. DeepSeek-V2.5 is 1.5x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-V2.5 outperforms in 3 benchmarks (GSM8k, HumanEval, MMLU), while Gemini 1.5 Flash is better at 1 benchmark (MATH). DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
On price, DeepSeek-V2.5 is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 1.5 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-V2.5
- you want the strongest raw capability — it leads on 3 of 4 shared benchmarks
- cost matters — it's about 1.5x cheaper per token
- you want the most recent training data — it shipped May 2024
- you need open weights you can self-host or fine-tune
Choose Gemini 1.5 Flash
- 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-V2.5 outperforms in 3 benchmarks (GSM8k, HumanEval, MMLU), while Gemini 1.5 Flash is better at 1 benchmark (MATH).
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 1.1x cheaper than Gemini 1.5 Flash ($0.15/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 2.1x cheaper than Gemini 1.5 Flash ($0.60/1M tokens).
In conclusion, Gemini 1.5 Flash is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 1.5 Flash accepts 1,048,576 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Both models can generate responses up to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Gemini 1.5 Flash supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Gemini 1.5 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Gemini 1.5 Flash
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Gemini 1.5 Flash uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Gemini 1.5 Flash was released on 2024-05-01.
DeepSeek-V2.5 is 0 month newer than Gemini 1.5 Flash.
May 8, 2024
2.3 years ago
1w newerMay 1, 2024
2.3 years ago
Knowledge Cutoff
When training data ends
Gemini 1.5 Flash has a documented knowledge cutoff of 2023-11-01, while DeepSeek-V2.5's cutoff date is not specified.
We can confirm Gemini 1.5 Flash's training data extends to 2023-11-01, but cannot make a direct comparison without DeepSeek-V2.5's cutoff date.
—
Nov 2023
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Gemini 1.5 Flash is available from Google.
DeepSeek-V2.5
Gemini 1.5 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Gemini 1.5 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Gemini 1.5 Flash.