Open-weight reasoning model that integrates thinking directly into tool-use, competitive with frontier models at a fraction of the cost.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Reasoning That Stays Coherent Through Tool Calls
Benchmarks
How DeepSeek V3.2 Scores on Independent Reasoning and Coding Benchmarks
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"stream": False,
"tool_choice": "auto",
"logprobs": False,
"model": "deepseek/deepseek-v3.2",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer your_api_key",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer your_api_key', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"deepseek/deepseek-v3.2","messages":[{"role":"user","content":"Hello"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer your_api_key' \
--header 'Content-Type: application/json' \
--data '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "deepseek/deepseek-v3.2",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'
Comparison
DeepSeek V3.2 vs V3.2-Exp: What the Official Release Changes
V3.2-Exp was the experimental predecessor that introduced DeepSeek Sparse Attention as an intermediate step, benchmarking roughly on par with V3.1-Terminus. DeepSeek V3.2 uses an identical architecture but adds substantial post-training: a scalable reinforcement learning framework and a large-scale agentic task synthesis pipeline. Both share the 163K context window and the DSA efficiency profile. The practical decision is whether you want the validated experimental checkpoint or the production successor that delivers materially higher reasoning and agentic scores while keeping the same efficient long-context behavior.
Choose DeepSeek V3.2 when you need production-grade reasoning and agentic tool-use—its Intelligence Index rose to 66 from V3.2-Exp's 57, with notable uplifts in tool use and long context. It is the default for coding agents and multi-step pipelines. Choose V3.2-Exp only if you are specifically reproducing earlier research comparisons or already pinned to that checkpoint for controlled evaluation against V3.1-Terminus. For any new production workload, V3.2 is the better choice.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Coding Agents
DeepSeek V3.2 is well suited to autonomous coding agents that plan, edit, run tools, and iterate. DeepSeek reported SWE-bench Verified scores in the 72–74 range across frameworks, and the model significantly outperformed open-source peers on SWE-bench Verified and Terminal Bench 2.0 in its technical report. The thinking-with-tools integration keeps reasoning coherent across shell and file operations, reducing broken tool-call sequences. Its low cost per token makes iterative agent loops economical at scale.
Multi-Step Reasoning Tasks
For analytical, mathematical, and scientific problems requiring long chain-of-thought, V3.2's reasoning mode scored 66 on the Artificial Analysis Intelligence Index—near the GPT-5 class among open-weight models. This fits research assistants, complex problem decomposition, and planning tasks. Expect higher token usage and latency in thinking mode, so budget for that on high-volume deployments and fall back to non-thinking mode when depth is unnecessary.
Long-Context Processing
DeepSeek Sparse Attention makes the ~163K-token window practical without the quadratic cost of dense attention, and Artificial Analysis found DSA introduced no measurable intelligence cost on their long-context reasoning benchmark. This suits large-document analysis, multi-file code review, and RAG over sizeable context. For workloads exceeding roughly 160K tokens, you will need chunking or a larger-context model.