DeepSeek-V2.5 vs Gemini 1.5 Pro
DeepSeek-V2.5 and Gemini 1.5 Pro are closely matched at 8.4 and 12.1 on the LLM Stats Score. DeepSeek-V2.5 is 25.0x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-V2.5 and Gemini 1.5 Pro are closely matched on the overall LLM Stats Score at 8.4 and 12.1.
The models split the 4 individual benchmarks reported for both models evenly.
On price, DeepSeek-V2.5 is roughly 25.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 1.5 Pro also accepts a larger context window (2,097,152 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-V2.5
- cost matters — it's about 25.0x 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 Pro
- you process long inputs — it offers a 2,097,152 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 23 for Gemini 1.5 Pro
DeepSeek-V2.5 outperforms in 2 benchmarks (GSM8k, HumanEval), while Gemini 1.5 Pro is better at 2 benchmarks (MATH, MMLU).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 17.9x cheaper than Gemini 1.5 Pro ($2.50/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 35.7x cheaper than Gemini 1.5 Pro ($10.00/1M tokens).
In conclusion, Gemini 1.5 Pro 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 Pro accepts 2,097,152 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Both models can generate responses up to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Gemini 1.5 Pro supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Gemini 1.5 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Gemini 1.5 Pro
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Gemini 1.5 Pro 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 Pro was released on 2024-05-01.
DeepSeek-V2.5 is 0 month newer than Gemini 1.5 Pro.
May 8, 2024
2.3 years ago
1w newerMay 1, 2024
2.3 years ago
Knowledge Cutoff
When training data ends
Gemini 1.5 Pro 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 Pro'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 Pro is available from Google.
DeepSeek-V2.5
Gemini 1.5 Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Gemini 1.5 Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Gemini 1.5 Pro.