Curiosity · AI Model

DeepSeek V2.5

DeepSeek V2.5 is DeepSeek's September 2024 open-weights MoE — a 236B total / 21B active model that unified the earlier V2 Chat and V2 Coder lines into a single general-purpose assistant. It was the proving ground for the Multi-Head Latent Attention and MoE design that V3 later scaled up.

Model specs

Vendor
DeepSeek
Family
DeepSeek V2
Released
2024-09
Context window
128,000 tokens
Modalities
text, code
Input price
$0.14/M tok
Output price
$0.28/M tok
Pricing as of
2026-04-20

Strengths

  • Open weights under DeepSeek custom license — commercial use permitted
  • MLA + MoE — drastically cheaper inference than dense equivalents
  • Strong combined chat and code quality in a single model
  • Widely adopted in 2024 open-source LLM toolchains

Limitations

  • Superseded by DeepSeek V3 on nearly every benchmark
  • 236B footprint still requires multi-GPU hosting for BF16 serving
  • Custom license is permissive but not OSI-standard MIT / Apache
  • Limited native tool-use fine-tuning versus 2026 frontier models

Use cases

  • Legacy DeepSeek V2 production deployments
  • Research into MoE efficiency and Multi-Head Latent Attention
  • Cost-efficient self-host where V3 compute is not yet justified
  • Teacher model for distilling smaller Qwen or Llama base derivatives

Benchmarks

BenchmarkScoreAs of
MMLU≈80%2024-09
HumanEval≈89%2024-09
MATH≈74%2024-09

Frequently asked questions

What is DeepSeek V2.5?

A unified chat and coding open-weights MoE LLM from DeepSeek released September 2024. It merged DeepSeek V2 Chat and DeepSeek V2 Coder into a single 236B total / 21B active model.

Should I use V2.5 or V3?

Use V3 for new deployments — it's stronger on essentially every benchmark and uses the same MLA+MoE blueprint. V2.5 is mainly for legacy compatibility or research comparisons.

What is Multi-Head Latent Attention (MLA)?

MLA is DeepSeek's attention compression technique that projects keys and values into a low-rank latent space, slashing KV-cache memory and enabling much cheaper long-context inference. V2.5 was where MLA hit production.

Sources

  1. DeepSeek — DeepSeek V2.5 announcement — accessed 2026-04-20
  2. Hugging Face — deepseek-ai/DeepSeek-V2.5 — accessed 2026-04-20

Related

  • DeepSeek Coder V2 — DeepSeek Coder V2 is the open-weights coding SOTA — a 236B parameter MoE (21B active) that matched closed-frontier coding models on HumanEval and LiveCodeBench.
  • DeepSeek V3 — DeepSeek V3 is a 671B parameter open-weights Mixture-of-Experts model from Chinese AI lab DeepSeek — it matched GPT-4-class quality at a fraction of the training cost, reshaping open-source LLM expectations.
  • Llama 3.1 405B Instruct — Llama 3.1 405B is Meta's open-weights flagship dense model — the first open release to credibly challenge closed-frontier GPT-4-class quality on reasoning and knowledge.
  • All AI Models — Browse the complete ai models library
  • VSET — AICTE IDEA Lab — Innovation & prototyping lab