Model Comparison
DeepSeek-R1 vs DeepSeek-V2.5Which is better in 2026?
Comparing DeepSeek-R1 and DeepSeek-V2.5 across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-R1 vs DeepSeek-V2.5 — which is better?
DeepSeek-R1 (by DeepSeek) and DeepSeek-V2.5 (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
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.
Choose DeepSeek-R1 if…
- 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 if…
- cost matters — it's about 5.5x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and DeepSeek-V2.5don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
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
1.4 years ago
8mo newerMay 8, 2024
2.1 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
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
DeepSeek-V2.5
View detailsDeepSeek
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1 vs DeepSeek-V2.5.