DeepSeek-R1 vs DeepSeek-V2.5
Comparing DeepSeek-R1 and DeepSeek-V2.5 across benchmarks, pricing, and capabilities.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-R1 and DeepSeek-V2.5 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V2.5 is roughly 5.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-R1 also accepts a larger context window (131,072 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
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Jan 2025
Choose DeepSeek-V2.5
- cost matters — it's about 5.5x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 15 for DeepSeek-V2.5
DeepSeek-R1 and DeepSeek-V2.5don'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 3.9x more expensive than DeepSeek-V2.5 ($0.14/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 7.8x more expensive than DeepSeek-V2.5 ($0.28/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 435.0B more parameters than DeepSeek-V2.5, making it 184.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-R1 accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-R1 can generate longer responses up to 131,072 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while DeepSeek-V2.5 uses deepseek.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
deepseek
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while DeepSeek-V2.5 was released on 2024-05-08.
DeepSeek-R1 is 9 months newer than DeepSeek-V2.5.
Jan 20, 2025
1.7 years ago
8mo newerMay 8, 2024
2.4 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic.
DeepSeek-R1
DeepSeek-V2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and DeepSeek-V2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs DeepSeek-V2.5.