What is DeepSeek-Chat?
DeepSeek-Chat refers to DeepSeek’s chat-oriented deployments built on the DeepSeek V3 series (most recently DeepSeek-V3.2 and the higher-performance variant DeepSeek-V3.2-Speciale). These models are “reasoning-first” large language models (LLMs) optimized for long-context reasoning, tool use (agentic workflows), code and math tasks.
Main features and architectural highlights
- Reasoning-first design & hybrid inference: DeepSeek emphasizes a “think / non-think” dual mode so the same weights can behave as a fast generator or as a deliberative agent that internally composes multi-step plans before calling tools (their marketing calls this “thinking in tool-use”). This is baked into training data and product UX.
- Long-context and sparse attention: DeepSeek implements a sparse/efficient attention variant (marketed as DeepSeek Sparse Attention / NSA) intended to make 100k+ token windows practical and cheaper to run than dense attention at the same length. This is core to their claim of supporting very large documents/agent histories.
Benchmark performance (selected, reproducible metrics)
Below are representative numbers drawn from the DeepSeek V3 public benchmark tables (Hugging Face / vendor results). When quoting benchmarks, note vendor pages typically control evaluation settings (temperature, prompt settings, output length limits) and evaluate many metrics; the numbers below are representative highlights rather than an exhaustive table.
- Mathematics:
- MATH-500 (EM): ~90.2% (DeepSeek-V3 reported).
- GSM8K: ~89.3% (8-shot math accuracy reported in vendor tables).
- Code: Code HumanEval (Pass@1): vendor tables show 65.2% (0-shot) in one evaluation table and higher pass rates in integrated chat/code-generation settings (different evaluation variants yield Pass@1 values up to the low-80s when using specialized chat/code configs). (See vendor benchmark pages for the exact evaluation variant.)
- General reasoning & benchmarks: MMLU / BBH / AGIEval: DeepSeek V3 ranks highly vs. other open-weight models and is reported to be competitive with or approaching frontier closed models on selected reasoning and problem-solving benchmarks in vendor tables. The vendor materials highlight strong wins on math and code categories.
How to access deepseek-chat API
Step 1: Sign Up for API Key
Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
Step 2: Send Requests to deepseek-chat API
Select the “deepseek-chat\ \” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. base url is chat format.
Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.
Step 3: Retrieve and Verify Results
Process the API response to get the generated answer. After processing, the API responds with the task status and output data.