# DeepSeek-V2.5 ![Organization](https://img.shields.io/badge/Org-DeepSeek-3b82f6) ![Context](https://img.shields.io/badge/Context-128K%20tokens-1f6feb) ![License](https://img.shields.io/badge/License-DeepSeek-d29922) ![Weights](https://img.shields.io/badge/Weights-Open-2da44e) ![Released](https://img.shields.io/badge/Released-Sep%202024-555) > **Quick answer:** DeepSeek-V2.5 is DeepSeek's September 2024 MoE model — an intermediate release between V2 and V3. It scores 76.2% on Arena Hard and 74.7% on MATH. Available with open weights under the DeepSeek Model License, it was the state-of-the-art cost-efficient open model at launch, superseded by DeepSeek-V3 (December 2024). ## At a Glance **Where DeepSeek-V2.5 leads** - 76.2% Arena Hard — strong general instruction following - 74.7% MATH — good competition mathematics performance - Open weights available on Hugging Face - MoE architecture: cost-efficient inference relative to parameter count - Predecessor that informed DeepSeek-V3's improvements **Where it lags** - Superseded by DeepSeek-V3 (90.2% MATH, 88.5% MMLU, MIT license) in all dimensions - DeepSeek Model License (not MIT) — more restrictive than V3 - July 2024 knowledge cutoff - Text-only **Best for:** Historical comparisons; existing production integrations on V2.5; teams that have validated V2.5 for specific tasks and haven't migrated to V3. ## What DeepSeek-V2.5 Is DeepSeek-V2.5 is an intermediate release in DeepSeek's development roadmap, launched September 5, 2024. It combined the capabilities of DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct into a single model, improving both general language capability and coding performance. The model represents DeepSeek's state-of-the-art at Q3 2024, demonstrating strong Arena Hard (76.2%) and MATH (74.7%) scores competitive with leading models at the time. It was a key step in DeepSeek's trajectory toward V3, which launched December 2024 with dramatically improved scores across all benchmarks. For new deployments, DeepSeek-V3 is strongly preferred: MIT license (vs DeepSeek Model License), significantly better benchmarks (90.2% MATH vs 74.7%), and similarly cost-efficient MoE architecture. DeepSeek-V2.5 is archived but remains available for teams with production integrations. ## Specifications | Field | Value | |---|---| | Organization | DeepSeek | | Total parameters | ~236B (MoE) | | Context window | 128,000 tokens | | License | DeepSeek Model License | | HuggingFace | [deepseek-ai/DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5) | | Release date | September 5, 2024 | | Knowledge cutoff | July 2024 | | Modality | Text only | ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [Arena Hard](/benchmarks/arena-hard-v2) | 76.2% | Benchgen evaluation | 2024-09 | | [MATH](/benchmarks/math) | 74.7% | Benchgen evaluation | 2024-09 | ## DeepSeek-V2.5 vs Alternatives | Model | MATH | Arena Hard | License | |---|---|---|---| | **DeepSeek-V2.5** | 74.7% | 76.2% | DeepSeek ML | | [DeepSeek-V3](/models/deepseek/deepseek-v3) | 90.2% | — | MIT | | [Llama 3.1 405B Instruct](/models/meta/llama-3-1-405b-instruct) | — | — | Llama 3.1 | DeepSeek-V3 strictly dominates: 90.2% MATH vs 74.7%, MIT vs DeepSeek Model License, more recent knowledge cutoff. Migrate to V3 for all new deployments. ## Related Models & Benchmarks - Compare: [DeepSeek-V3](/models/deepseek/deepseek-v3) · [Llama 3.1 70B Instruct](/models/meta/llama-3-1-70b-instruct) - Benchmarks: [MATH](/benchmarks/math) · [Arena Hard](/benchmarks/arena-hard-v2) ## Frequently Asked Questions

What is DeepSeek-V2.5?

DeepSeek-V2.5 is DeepSeek's September 2024 MoE model scoring 76.2% Arena Hard and 74.7% MATH. It has been superseded by DeepSeek-V3, which achieves significantly better scores under a more permissive MIT license.

Should I use DeepSeek-V2.5 or DeepSeek-V3?

DeepSeek-V3 for all new deployments: better benchmarks (90.2% MATH vs 74.7%), MIT license vs DeepSeek Model License, and more recent knowledge cutoff.

--- *Specs from DeepSeek's official V2.5 release (September 2024) and Benchgen evaluations. Last updated 2026-07-24.*

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