DeepSeek-V2.5 vs DeepSeek-V3
DeepSeek-V3 leads the LLM Stats Score 15.8 to 8.4. DeepSeek-V2.5 is 2.7x cheaper per token.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-V3 leads the overall LLM Stats Score 15.8 to 8.4, ranking #212 overall.
In the 3 individual benchmarks reported for both models, DeepSeek-V3 wins 3; this is a narrower head-to-head signal than the composite indexes.
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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V2.5
- cost matters — it's about 2.7x cheaper per token
Choose DeepSeek-V3
- overall performance matters — it scores 15.8 and ranks #212 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Dec 2024
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 20 for DeepSeek-V3
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.
Human preference
Blind head-to-head votes and playground preference scores
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
2.3 years ago
Dec 25, 2024
1.7 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
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and DeepSeek-V3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs DeepSeek-V3.