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Model facts & API quick start
DeepSeek-V3.2 (deepseek-ai/DeepSeek-V3.2-TEE) is served on Chutes inside a Trusted Execution Environment (TEE). Call it through the OpenAI-compatible API at https://llm.chutes.ai/v1 with a Chutes API key and "model": "deepseek-ai/DeepSeek-V3.2-TEE".
DeepSeek-V3.2 at a glance
- Model ID
deepseek-ai/DeepSeek-V3.2-TEE- Developer
- DeepSeek
- Deployed by
- chutes
- Type
- LLM
- Input price
- $1.00 per 1M tokens
- Output price
- $1.00 per 1M tokens
- Cached input price
- $0.10 per 1M tokens
- Context length
- 131,072 tokens
- Modalities
- text in → text out
- Confidential compute (TEE)
- Yes, runs in a Trusted Execution Environment
- Precision
- FP8 (e4m3 block-quantized upstream weights), served in a TEE on Chutes
- Open weights
deepseek-ai/DeepSeek-V3.2- Parameters
- 685B total (safetensors weight count)
- Architecture
- Mixture-of-Experts transformer with DeepSeek Sparse Attention (DSA); 61 layers, 256 routed experts plus 1 shared, 8 experts active per token
- License
- MIT
- Released
- 2025-12
- Experts
- 256 routed + 1 shared, 8 active per token
- Layers
- 61
- Hidden size
- 7168
- Attention heads
- 128
- Sparse attention top-k
- 2048 tokens indexed per query (index_topk)
- Vocabulary
- 129,280 tokens
- Base model
- deepseek-ai/DeepSeek-V3.2-Exp-Base (finetune)
API quick start
- Base URL
https://llm.chutes.ai/v1- model
deepseek-ai/DeepSeek-V3.2-TEE- Endpoint
POST /v1/chat/completions- Auth header
Authorization: Bearer $CHUTES_API_KEY
OpenAI-compatible: point any OpenAI SDK at the base URL above and pass your Chutes API key as the API key.
curl -X POST "https://llm.chutes.ai/v1/chat/completions" \
-H "Authorization: Bearer $CHUTES_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-ai/DeepSeek-V3.2-TEE",
"messages": [
{
"role": "user",
"content": "Hello!"
}
],
"stream": true,
"max_tokens": 1024,
"temperature": 0.7
}'DeepSeek-V3.2 FAQ
What context window does DeepSeek-V3.2 support?
This chute serves a 131,072-token context (context_length in https://llm.chutes.ai/v1/models). The upstream config sets max_position_embeddings to 163,840. DeepSeek Sparse Attention is specifically designed to keep long-context inference efficient, so the model remains practical near the top of that window.
Does DeepSeek-V3.2 support function calling and tool use?
Yes. This generation introduces a revised tool-calling format and a thinking-with-tools mode in its chat template, and the model was post-trained on large-scale synthesized agentic tasks. Send OpenAI-style tools in your chat completion request via the gateway.
Can I use DeepSeek-V3.2 commercially?
Yes. Both the repository and the model weights are released under the MIT license, which permits commercial use, modification, and redistribution.
What does the TEE suffix mean?
The chute runs inference inside a Trusted Execution Environment, i.e. attested confidential-compute hardware. Prompts and outputs are processed inside the enclave. It is a serving-level property and does not change the model's behavior or output quality.
How do I call it from the OpenAI SDK?
Point the client at base_url https://llm.chutes.ai/v1 with your Chutes API key, and set model to deepseek-ai/DeepSeek-V3.2-TEE. Both chat completions and legacy completions work, and streaming is supported.
What sampling settings should I use?
DeepSeek recommends temperature 1.0 and top_p 0.95, which also match the repo's generation_config. The chute default is temperature 0.7, so pass the recommended values explicitly if you want reference behavior.
Is DeepSeek-V3.2 a reasoning model?
Yes, it is post-trained with reinforcement learning for reasoning and emits thinking content via its chat template. DeepSeek reports performance comparable to GPT-5, with the separate high-compute Speciale variant going further on pure reasoning (but without tool calling).