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DeepSeek-V3.2 - AI Language Models Tool

Overview

DeepSeek-V3.2 is an open-weight, reasoning-optimized large language model from DeepSeek designed for efficient long-context reasoning and agentic tool use. The release centers on DeepSeek Sparse Attention (DSA), a sparse-attention mechanism engineered to cut computational cost for long-context workloads while maintaining output quality, and a scaled reinforcement-learning post‑training pipeline that the authors say brings V3.2 close to contemporary closed‑source frontier models. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)) The distribution on Hugging Face includes BF16, FP8 (F8_E4M3), and F32 weight formats with an MIT license, plus a new chat template that explicitly supports “thinking with tools” and structured tool-calls for agent workflows. The project supplies encoding helpers and examples to convert OpenAI‑style messages into the model’s input format, and guidance for local inference and sampling defaults. Community uptake has been rapid (see downloads and likes on Hugging Face), and the launch prompted wide coverage about its price‑performance and support for non‑CUDA hardware. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3.2))

Model Statistics

  • Downloads: 107,141
  • Likes: 1088
  • Pipeline: text-generation
  • Parameters: 685.4B

License: mit

Model Details

Architecture and scale: DeepSeek‑V3.2 is described as a very large Mixture‑of‑Experts (MoE) transformer-style family with approximately 685 billion total parameters; MoE routing means only a subset of experts are activated per token to reduce inference cost. Weight formats released include BF16, FP8 (F8_E4M3), and F32 safetensors. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)) Sparse attention and long context: The model introduces DeepSeek Sparse Attention (DSA), a fine‑grained sparse attention variant intended to preserve quality while lowering compute and memory for long sequences; the family is documented to support large context windows (reported in the ecosystem as up to 128K tokens for recent V3/R1 models). ([huggingface.co](https://huggingface.co/papers/2512.02556)) Training & post‑training: DeepSeek reports extensive post‑training with a scaled reinforcement‑learning framework and a large agentic task synthesis pipeline used to generate tool‑use training data. The team states that the high‑compute Speciale variant was tuned for deep reasoning (and is claimed to exceed GPT‑5 on their internal evaluations). The main V3.2 model supports tool calling and the new “thinking with tools” chat mode; the Speciale variant is stated to focus on reasoning only and to not support tool‑calling. ([huggingface.co](https://huggingface.co/papers/2512.02556)) Chat / tool interface: The distributed repo includes an encoding folder with Python scripts (encode_messages / parse_message_from_completion_text) to translate OpenAI‑compatible message lists to the model’s prompt format. A new role named "developer" is reserved for search‑agent scenarios in the template; the model authors caution that the parsing helpers assume well‑formatted output and are not production‑grade without additional error handling. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)) Deployment notes: Hugging Face and vendor docs provide local inference guidance and recommend sampling settings (example: temperature=1.0, top_p=0.95 for local runs). Multiple inference providers and quantizations are available in the model tree for practical deployment. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3.2))

Key Features

  • DeepSeek Sparse Attention (DSA) — sparse attention for long‑context efficiency.
  • Large‑scale MoE family with ~685B total parameters and lower per‑token activation cost.
  • 128K-scale long‑context support reported across V3 family for document and code tasks.
  • New chat template with "thinking with tools" and OpenAI‑compatible message encoding helpers.
  • Weights released under MIT license in BF16, FP8 (F8_E4M3), and F32 safetensors.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load tokenizer (encoding helpers are provided in the model repo)
# See the model's encoding folder for encode_messages/parse_message_from_completion_text
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.2")

# Load model (device_map="auto" requires accelerate or transformers 4.x with device mapper support)
model = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/DeepSeek-V3.2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True  # required for some vendor-specific model implementations
)

# Example using the repository's encoding helper (pseudo-call; the repo contains encoding_dsv32.py)
# from encoding_dsv32 import encode_messages, parse_message_from_completion_text

messages = [
    {"role": "user", "content": "Explain the Collatz conjecture in two sentences."}
]
# prompt = encode_messages(messages, thinking_mode="non-thinking", add_default_bos_token=True)
# Tokenize and generate (fallback to simple prompt if encoding helper not available)
prompt = "<|User|> Explain the Collatz conjecture in two sentences.\n<|Assistant|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**{k: v.to(model.device) for k, v in inputs.items()}, max_new_tokens=256, temperature=1.0, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Notes: check the model repo for official encode_messages / parsing utilities and for recommended sampling parameters.
# See Hugging Face model card and repo for exact usage and production guidance.

Pricing

DeepSeek publishes pay‑as‑you‑go API pricing (per 1M tokens): example rates listed in official docs include $0.028 (input cache hit), $0.28 (input cache miss), and $0.42 (output) for DeepSeek‑V3 models. A free web/chat tier with fair‑use limits is also available; enterprise or private deployments may have separate pricing. Verify live rates and regional taxes on DeepSeek's API docs before production use. ([api-docs.deepseek.com](https://api-docs.deepseek.com/quick_start/pricing/?utm_source=openai))

Benchmarks

Model size (parameters): ≈685 billion parameters (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.2)

Hugging Face downloads (last 30 days): 107,141 downloads (last month on model page) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.2)

License & weight formats: MIT license; BF16, FP8 (F8_E4M3), F32 safetensors (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.2)

Long-context capability (reported): Support for long contexts (ecosystem reports up to 128K tokens for V3 family) (Source: https://huggingface.co/papers/2512.02556)

High‑level reasoning claims: Authors report parity or superiority vs GPT‑5 on internal reasoning benchmarks; Speciale variant reported to reach gold‑medal performance on IMO & IOI 2025 case sets. (Source: https://huggingface.co/papers/2512.02556)

Last Refreshed: 2026-01-09

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
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