Model Comparison
DeepSeek-V3 vs DeepSeek-V2.5Which is better in 2026?
DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 2.7x cheaper per token.
Verdict: DeepSeek-V3 vs DeepSeek-V2.5 — which is better?
DeepSeek-V3 (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.
DeepSeek-V3 outperforms in 3 benchmarks (HumanEval-Mul, MMLU, SWE-Bench Verified), while DeepSeek-V2.5 is better at 0 benchmarks. 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-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
Choose DeepSeek-V2.5 if…
- cost matters — it's about 2.7x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3 outperforms in 3 benchmarks (HumanEval-Mul, MMLU, SWE-Bench Verified), while DeepSeek-V2.5 is better at 0 benchmarks.
DeepSeek-V3 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3 ($0.27/1M tokens) is 1.9x more expensive than DeepSeek-V2.5 ($0.14/1M tokens).
For output processing, DeepSeek-V3 ($1.10/1M tokens) is 3.9x more expensive than DeepSeek-V2.5 ($0.28/1M 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-V3 is licensed under MIT + Model License (Commercial use allowed), while DeepSeek-V2.5 uses deepseek.
License differences may affect how you can use these models in commercial or open-source projects.
MIT + Model License (Commercial use allowed)
Open weights
deepseek
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while DeepSeek-V2.5 was released on 2024-05-08.
DeepSeek-V3 is 8 months newer than DeepSeek-V2.5.
Dec 25, 2024
1.5 years ago
7mo 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-V3 is available from DeepSeek. DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic.
DeepSeek-V3
DeepSeek-V2.5
Outputs Comparison
Key Takeaways
DeepSeek-V3
View detailsDeepSeek
DeepSeek-V2.5
View detailsDeepSeek
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3 vs DeepSeek-V2.5.