DeepSeek-V2.5 vs o1-mini
DeepSeek-V2.5 and o1-mini are closely matched at 8.8 and 10.5 on the LLM Stats Score. DeepSeek-V2.5 is 30.0x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V2.5 and o1-mini are closely matched on the overall LLM Stats Score at 8.8 and 10.5.
In the 2 individual benchmarks reported for both models, o1-mini wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V2.5 is roughly 30.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o1-mini also accepts a larger context window (128,000 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 30.0x cheaper per token
- you need open weights you can self-host or fine-tune
Choose o1-mini
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Sep 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 · 6 for o1-mini
DeepSeek-V2.5 outperforms in 0 benchmarks, while o1-mini is better at 2 benchmarks (HumanEval, MMLU).
o1-mini 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 21.4x cheaper than o1-mini ($3.00/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 42.9x cheaper than o1-mini ($12.00/1M tokens).
In conclusion, o1-mini 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-mini accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. o1-mini can generate longer responses up to 65,536 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-mini 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-mini was released on 2024-09-12.
o1-mini is 4 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Sep 12, 2024
2.0 years ago
4mo 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-mini is available from OpenAI, Azure.
DeepSeek-V2.5
o1-mini
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and o1-mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs o1-mini.