DeepSeek-V2.5 vs o1
o1 leads the LLM Stats Score 21.1 to 8.4. DeepSeek-V2.5 is 150.0x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
o1 leads the overall LLM Stats Score 21.1 to 8.4, ranking #182 overall.
In the 5 individual benchmarks reported for both models, o1 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V2.5 is roughly 150.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o1 also accepts a larger context window (200,000 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 150.0x cheaper per token
- you need open weights you can self-host or fine-tune
Choose o1
- overall performance matters — it scores 21.1 and ranks #182 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
- you process long inputs — it offers a 200,000 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 · 19 for o1
DeepSeek-V2.5 outperforms in 1 benchmarks (HumanEval), while o1 is better at 4 benchmarks (GSM8k, MATH, MMLU, SWE-Bench Verified).
o1 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 107.1x cheaper than o1 ($15.00/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 214.3x cheaper than o1 ($60.00/1M tokens).
In conclusion, o1 is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
o1 accepts 200,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. o1 can generate longer responses up to 100,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while o1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while o1 was released on 2024-12-17.
o1 is 7 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Dec 17, 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. o1 is available from Azure, OpenAI.
DeepSeek-V2.5
o1
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and o1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs o1.