Model Comparison
DeepSeek-V2.5 vs DeepSeek-V3Which is better in 2026?
DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 2.7x cheaper per token.
Verdict: DeepSeek-V2.5 vs DeepSeek-V3 — which is better?
DeepSeek-V2.5 (by DeepSeek) and DeepSeek-V3 (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-V3 is better at 3 benchmarks (HumanEval-Mul, MMLU, SWE-Bench Verified). DeepSeek-V3 significantly outperforms across most benchmarks.
On price, DeepSeek-V2.5 is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3 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 2.7x cheaper per token
Choose DeepSeek-V3 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Dec 2024
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 0 benchmarks, while DeepSeek-V3 is better at 3 benchmarks (HumanEval-Mul, MMLU, SWE-Bench Verified).
DeepSeek-V3 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 1.9x cheaper than DeepSeek-V3 ($0.27/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 3.9x cheaper than DeepSeek-V3 ($1.10/1M tokens).
In conclusion, DeepSeek-V3 is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3 has 435.0B more parameters than DeepSeek-V2.5, making it 184.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3 accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-V3 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-V3 uses MIT + Model License (Commercial use allowed).
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
MIT + Model License (Commercial use allowed)
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while DeepSeek-V3 was released on 2024-12-25.
DeepSeek-V3 is 8 months newer than DeepSeek-V2.5.
May 8, 2024
2.2 years ago
Dec 25, 2024
1.5 years ago
7mo 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-V3 is available from DeepSeek.
DeepSeek-V2.5
DeepSeek-V3
Outputs Comparison
Key Takeaways
DeepSeek-V2.5
View detailsDeepSeek
DeepSeek-V3
View detailsDeepSeek
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and DeepSeek-V3 side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about DeepSeek-V2.5 vs DeepSeek-V3.