Model Comparison
DeepSeek-V2.5 vs DeepSeek-R1-0528Which is better in 2026?
DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 5.2x cheaper per token.
Verdict: DeepSeek-V2.5 vs DeepSeek-R1-0528 — which is better?
DeepSeek-V2.5 (by DeepSeek) and DeepSeek-R1-0528 (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.
DeepSeek-V2.5 outperforms in 0 benchmarks, while DeepSeek-R1-0528 is better at 1 benchmark (SWE-Bench Verified). DeepSeek-R1-0528 significantly outperforms across most benchmarks.
On price, DeepSeek-V2.5 is roughly 5.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-R1-0528 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V2.5 if…
- cost matters — it's about 5.2x cheaper per token
Choose DeepSeek-R1-0528 if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped May 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 0 benchmarks, while DeepSeek-R1-0528 is better at 1 benchmark (SWE-Bench Verified).
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 3.6x cheaper than DeepSeek-R1-0528 ($0.50/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 7.7x cheaper than DeepSeek-R1-0528 ($2.15/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 435.0B more parameters than DeepSeek-V2.5, making it 184.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-R1-0528 accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-R1-0528 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-V2.5 is licensed under deepseek, while DeepSeek-R1-0528 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while DeepSeek-R1-0528 was released on 2025-05-28.
DeepSeek-R1-0528 is 13 months newer than DeepSeek-V2.5.
May 8, 2024
2.1 years ago
May 28, 2025
1.1 years ago
1.1yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita.
DeepSeek-V2.5
DeepSeek-R1-0528
Outputs Comparison
Key Takeaways
DeepSeek-V2.5
View detailsDeepSeek
DeepSeek-R1-0528
View detailsDeepSeek
Detailed Comparison
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FAQ
Common questions about DeepSeek-V2.5 vs DeepSeek-R1-0528.