4c:I[510592,["/_next/static/chunks/27gl9k2703ugd.js","/_next/static/chunks/3pd35vh8xvq70.js","/_next/static/chunks/0ru0lffoswy0r.js","/_next/static/chunks/3ku5jwxt0m0j7.js","/_next/static/chunks/24mqrfe34h1_g.js","/_next/static/chunks/1la1pzs9j8idc.js","/_next/static/chunks/3-axx3uk1c0-h.js","/_next/static/chunks/14bn3mp7hfp6x.js","/_next/static/chunks/1_xj2c5utin8v.js","/_next/static/chunks/2tdv_pqf9hlyu.js","/_next/static/chunks/44n8gpf9x1nn2.js","/_next/static/chunks/1e7h6z92_w2-c.js","/_next/static/chunks/1p9dpv--te6vb.js","/_next/static/chunks/1geiruffics9x.js","/_next/static/chunks/1lfygqrocy8iw.js","/_next/static/chunks/0q47y720ck5-9.js","/_next/static/chunks/287gwk2yxvb9p.js","/_next/static/chunks/3mi_myijg06x1.js","/_next/static/chunks/2_c926k09c1th.js","/_next/static/chunks/0bvim9_g0hu68.js","/_next/static/chunks/3xav1ddquf5gq.js","/_next/static/chunks/0ayh-bmdnfery.js","/_next/static/chunks/34kl4tv6yifcg.js","/_next/static/chunks/0nm6za7z70maq.js","/_next/static/chunks/44e22u2t839t0.js","/_next/static/chunks/0czpc-f8wjri8.js","/_next/static/chunks/2aq42x9vabhfb.js","/_next/static/chunks/24_gg9g_4g22i.js","/_next/static/chunks/00yjb-caoi49j.js","/_next/static/chunks/1l0z2zsen9vs2.js","/_next/static/chunks/0lt_qhq2z_c0z.js","/_next/static/chunks/01auidv8wbc03.js","/_next/static/chunks/434amnen8yqdi.js","/_next/static/chunks/1yd7p-515l2jc.js","/_next/static/chunks/2pqhypz2vo8mu.js","/_next/static/chunks/2tbc_4m20jg4-.js","/_next/static/chunks/2-18150gleij4.js","/_next/static/chunks/295ffwyuqa4b6.js"],"PlaygroundContextProvider"] 4d:T223a,# DeepSeek-V3.2 ## Description DeepSeek-V3.2 is a state-of-the-art large language model that harmonizes high computational efficiency with superior reasoning and agentic AI performance through DeepSeek Sparse Attention (DSA) and scalable reinforcement learning. The model achieves gold-medal performance in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI), performing comparably to GPT-5. *This model is ready for commercial/non-commercial use.* ## Third-Party Community Consideration: This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [DeepSeek-V3.2 Model Card](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) ## License and Terms of Use: **GOVERNING TERMS:** This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [MIT License](https://huggingface.co/deepseek-ai/DeepSeek-V3.2/blob/main/LICENSE). ## Deployment Geography: Global ## Use Case: **Use Case:** DeepSeek-V3.2 is designed for advanced reasoning tasks, agentic AI applications, tool use scenarios, and complex problem-solving in domains requiring high computational reasoning such as mathematics, programming competitions, and enterprise AI assistants. The model integrates reasoning into tool-use scenarios through a large-scale agentic task synthesis pipeline. ## Release Date: **Build.NVIDIA.com:** 12/16/2025 via [link](https://build.nvidia.com/deepseek-ai/deepseek-v3_2) **Huggingface:** 12/01/2025 via [link](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) ## Reference(s): **References:** - [DeepSeek-V3.2 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) - [DeepSeek-V3.2-Exp Base Model](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp-Base) - [DeepSeek Chat Interface](https://chat.deepseek.com/) - [DeepSeek Discord Community](https://discord.gg/deepseek) - [Olympiad Case Files](https://huggingface.co/deepseek-ai/DeepSeek-V3.2/tree/main/assets/olympiad_cases) - IMO 2025, IOI 2025, ICPC World Finals, CMO 2025 submissions

DeepSeek-V3.2 Variants

The DeepSeek-V3.2 family includes multiple specialized variants: | Variant | Description | Primary Use | |---------|-------------|-------------| | **DeepSeek-V3.2** | Standard version optimized for general reasoning and agentic tasks | Balanced reasoning and tool use | | **DeepSeek-V3.2-Speciale** | High-compute variant with enhanced reasoning capabilities, surpassing GPT-5 | Deep reasoning tasks only (no tool calling) | | **DeepSeek-V3.2-Exp** | Experimental version | Research and development |

## Model Architecture: **Architecture Type:** Transformer **Network Architecture:** DeepSeek Sparse Attention MoE **Total Parameters:** 685B **Base Model:** DeepSeek-V3.2-Exp-Base ### Input: **Input Types:** Text **Input Formats:** String **Input Parameters:** One Dimensional (1D) **Other Input Properties:** Supports multi-turn conversations with system prompts, user messages, and assistant responses. Includes a new "developer" role exclusively for search agent scenarios. Utilizes an updated chat template with "thinking with tools" capability. ### Output: **Output Types:** Text **Output Format:** String **Output Parameters:** One Dimensional (1D) **Other Output Properties:** Supports structured JSON output, function/tool calling, and reasoning content. Output can include explicit "thinking" traces when enabled via `reasoning_content` parameter. __Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.__ ## Software Integration: **Runtime Engines:** - **Other (Transformers):** Compatible with Hugging Face Transformers library - **Other (vLLM):** Recommended for efficient inference with DSA support **Supported Hardware:** - **NVIDIA Blackwell:** B200 - **NVIDIA Hopper:** H100, H200 **Operating Systems:** Linux **Additional Testing Statement:** __The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__ ## Model Version(s) DeepSeek-V3.2 (2025) ## Training, Testing, and Evaluation Datasets: ### Training Dataset **Data Modality:** Text **Training Data Collection:** Undisclosed **Training Labeling:** Undisclosed **Training Properties:** The model was trained using a scalable reinforcement learning framework with robust RL protocol and post-training compute scaling. ### Testing Dataset **Testing Data Collection:** Undisclosed **Testing Labeling:** Undisclosed **Testing Properties:** Undisclosed ### Evaluation Dataset **Evaluation Benchmark Score:** Undisclosed **Evaluation Data Collection:** Automated **Evaluation Labeling:** Human **Evaluation Properties:** Evaluated on competitive programming (IOI 2025, ICPC World Finals), mathematical reasoning (IMO 2025, CMO 2025), and general reasoning benchmarks comparing against frontier models like GPT-5 and Gemini-3.0-Pro. ## Inference **Acceleration Engine:** Transformers, vLLM with DeepSeek Sparse Attention optimization **Test Hardware:** The model is deployable on NVIDIA H100 and H200 GPUs. Precision formats available include FP8, BF16, F32, and F8_E4M3 for optimized inference. ## Additional Details ### Recommended Deployment Settings For local deployment, recommended sampling parameters: - **Temperature:** 1.0 - **Top_p:** 0.95 ### Key Technical Innovations **1. DeepSeek Sparse Attention (DSA)** An efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios. **2. Scalable Reinforcement Learning Framework** Robust RL protocol with scaled post-training compute enables performance comparable to GPT-5. The high-compute variant (DeepSeek-V3.2-Speciale) surpasses GPT-5. **3. Large-Scale Agentic Task Synthesis Pipeline** Novel synthesis pipeline that systematically generates training data at scale to integrate reasoning into tool-use scenarios, improving compliance and generalization in complex interactive environments. ### Chat Template DeepSeek-V3.2 introduces significant updates to its chat template compared to prior versions: - Revised format for tool calling - Introduction of "thinking with tools" capability - New `developer` role exclusively for search agent scenarios (not accepted in official API) **Important Note:** This release does not include a Jinja-format chat template. Refer to the Python encoding scripts in the `encoding/` folder of the model repository for message encoding and parsing. ### Model Variants **DeepSeek-V3.2-Speciale** A high-compute variant designed exclusively for deep reasoning tasks. Important limitations: - Does **not** support tool-calling functionality - Optimized solely for reasoning-intensive applications - Surpasses GPT-5 in reasoning benchmarks ### Olympiad Performance The model includes verified final submissions for: - **IOI 2025** (International Olympiad in Informatics) - Gold medal - **IMO 2025** (International Mathematical Olympiad) - Gold medal - **ICPC World Finals** (International Collegiate Programming Contest) - **CMO 2025** (Chinese Mathematical Olympiad) Submission files are available in the repository's `assets/olympiad_cases` folder for community verification. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)4e:T95d,from openai import OpenAI client = OpenAI( base_url = "https://integrate.api.nvidia.com/v1", api_key = "$NVIDIA_API_KEY" ) <% if (request.tools) { %> completion = client.chat.completions.create( model="<%- request.model %>", messages=<%- JSON.stringify(request.messages) %>, temperature=<%- request.temperature %>, top_p=<%- request.top_p %>, max_tokens=<%- request.max_tokens %>, <%- request.chat_template_kwargs ? 'extra_body={"chat_template_kwargs": ' + JSON.stringify(request.chat_template_kwargs).replaceAll("true", "True").replaceAll("false", "False").replaceAll("null", "None") + '},' : '' %> stream=<%- request.stream?.toString()[0].toUpperCase() + request.stream?.toString().slice(1) %>, tools=<%- JSON.stringify(request.tools) %>, <% if (request.tool_choice) { %>tool_choice=<%- JSON.stringify(request.tool_choice) %><% } %> )<% } else { %> completion = client.chat.completions.create( model="<%- request.model %>", messages=<%- JSON.stringify(request.messages) %>, temperature=<%- request.temperature %>, top_p=<%- request.top_p %>, max_tokens=<%- request.max_tokens %>, <%- request.chat_template_kwargs ? 'extra_body={"chat_template_kwargs": ' + JSON.stringify(request.chat_template_kwargs).replaceAll("true", "True").replaceAll("false", "False").replaceAll("null", "None") + '},' : '' %> stream=<%- request.stream?.toString()[0].toUpperCase() + request.stream?.toString().slice(1) %> )<% } %> <% if (request.stream) { %> for chunk in completion: if not getattr(chunk, "choices", None): continue <% if (request.chat_template_kwargs && request.chat_template_kwargs.thinking) { -%> reasoning = getattr(chunk.choices[0].delta, "reasoning_content", None) if reasoning: print(reasoning, end="") <% } -%> if chunk.choices and chunk.choices[0].delta.content is not None: print(chunk.choices[0].delta.content, end="") <% if (request.tools) { %>if chunk.choices[0].delta.tool_calls: print(chunk.choices[0].delta.tool_calls)<% } %> <% } else { %> <% if (request.chat_template_kwargs && request.chat_template_kwargs.thinking) { -%> reasoning = getattr(completion.choices[0].message, "reasoning_content", None) if reasoning: print(reasoning) <% } -%> print(completion.choices[0].message.content) <% if (request.tools) { %>if completion.choices[0].message.tool_calls: print(completion.choices[0].message.tool_calls)<% } %> <% } %> 4f:T4aa,from langchain_nvidia_ai_endpoints import ChatNVIDIA client = ChatNVIDIA( model="<%- request.model %>", api_key="$NVIDIA_API_KEY", temperature=<%- request.temperature %>, top_p=<%- request.top_p %>, max_tokens=<%- request.max_tokens %>, <%- request.chat_template_kwargs ? 'extra_body={"chat_template_kwargs": ' + JSON.stringify(request.chat_template_kwargs).replaceAll("true", "True").replaceAll("false", "False").replaceAll("null", "None") + '},' : '' %> ) <% if (request.stream) { %> for chunk in client.stream(<%- JSON.stringify(request.messages) %>): <% if (request.chat_template_kwargs && request.chat_template_kwargs.thinking) { %> if chunk.additional_kwargs and "reasoning_content" in chunk.additional_kwargs: print(chunk.additional_kwargs["reasoning_content"], end="") <% } %> print(chunk.content, end="") <% } else { %> response = client.invoke(<%- JSON.stringify(request.messages) %>) <% if (request.chat_template_kwargs && request.chat_template_kwargs.thinking) { -%> if response.additional_kwargs and "reasoning_content" in response.additional_kwargs: print(response.additional_kwargs["reasoning_content"]) <% } -%> print(response.content) <% } %> 50:T8cb,import OpenAI from 'openai'; const openai = new OpenAI({ apiKey: '$NVIDIA_API_KEY', baseURL: 'https://integrate.api.nvidia.com/v1', }) <% if (request.tools) { %> async function main() { const completion = await openai.chat.completions.create({ model: "<%- request.model %>", messages: <%- JSON.stringify(request.messages) %>, temperature: <%- request.temperature %>, top_p: <%- request.top_p %>, max_tokens: <%- request.max_tokens %>, <%- request.chat_template_kwargs ? 'chat_template_kwargs: ' + JSON.stringify(request.chat_template_kwargs) + ',' : '' %> stream: <%- request.stream %>, <% if (request.tools) { %>tools: <%- JSON.stringify(request.tools) %>,<% } %> <% if (request.tool_choice) { %>tool_choice: <%- JSON.stringify(request.tool_choice) %>,<% } %> })<% } else { %> async function main() { const completion = await openai.chat.completions.create({ model: "<%- request.model %>", messages: <%- JSON.stringify(request.messages) %>, temperature: <%- request.temperature %>, top_p: <%- request.top_p %>, max_tokens: <%- request.max_tokens %>, <%- request.chat_template_kwargs ? 'chat_template_kwargs: ' + JSON.stringify(request.chat_template_kwargs) + ',' : '' %> stream: <%- request.stream %> })<% } %> <% if (request.stream) { %> for await (const chunk of completion) { <% if (request.chat_template_kwargs && request.chat_template_kwargs.thinking) { -%> const reasoning = chunk.choices[0]?.delta?.reasoning_content; if (reasoning) process.stdout.write(reasoning); <% } -%> process.stdout.write(chunk.choices[0]?.delta?.content || '') <% if (request.tools) { %>if (chunk.choices[0]?.delta?.tool_calls) console.log(chunk.choices[0]?.delta?.tool_calls)<% } %> } <% } else { %> <% if (request.chat_template_kwargs && request.chat_template_kwargs.thinking) { -%> const reasoning = completion.choices[0]?.message?.reasoning_content; if (reasoning) process.stdout.write(reasoning + "\n"); <% } -%> process.stdout.write(completion.choices[0]?.message?.content || ''); <% if (request.tools) { %>if (completion.choices[0]?.message?.tool_calls) console.log(completion.choices[0]?.message?.tool_calls);<% } %> <% } %> } main();51:T6c3,<% if (request.tools) { %> "curl https://integrate.api.nvidia.com/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $NVIDIA_API_KEY\" \\\n -d '{\n \"model\": \"deepseek-ai/deepseek-v3.1\",\n \"messages\": <%- JSON.stringify(request.messages).replaceAll(\"'\", \"'\\\"'\\\"'\") %>,\n \"temperature\": <%- request.temperature %>,\n \"top_p\": <%- request.top_p %>,\n \"max_tokens\": <%- request.max_tokens %>,\n <%- request.chat_template_kwargs ? '\"chat_template_kwargs\": ' + JSON.stringify(request.chat_template_kwargs).replaceAll(\"'\", \"'\\\"'\\\"'\") + ',' + '\\n ' : '' %> \"stream\": <%- request.stream %> <% if (request.tools) { %>,\n \"tools\": <%- JSON.stringify(request.tools).replaceAll(\"'\", \"'\\\"'\\\"'\") %><% } %> <% if (request.tool_choice) { %>,\n \"tool_choice\": <%- JSON.stringify(request.tool_choice).replaceAll(\"'\", \"'\\\"'\\\"'\") %><% } %> }'\n"<% } else { %> "curl https://integrate.api.nvidia.com/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $NVIDIA_API_KEY\" \\\n -d '{\n \"model\": \"deepseek-ai/deepseek-v3.1\",\n \"messages\": <%- JSON.stringify(request.messages).replaceAll(\"'\", \"'\\\"'\\\"'\") %>,\n \"temperature\": <%- request.temperature %>,\n \"top_p\": <%- request.top_p %>,\n \"max_tokens\": <%- request.max_tokens %>,\n <%- request.chat_template_kwargs ? '\"chat_template_kwargs\": ' + JSON.stringify(request.chat_template_kwargs).replaceAll(\"'\", \"'\\\"'\\\"'\") + ',' + '\\n ' : '' %> \"stream\": <%- request.stream %> }'\n"<% } %>4b:["$","$L4c",null,{"initialState":{"artifact":{"artifactType":"ENDPOINT","name":"deepseek-v3_2","displayName":"deepseek-v3.2","publisher":"deepseek-ai","shortDescription":"State-of-the-art 685B reasoning LLM with sparse attention, long context, and integrated agentic tools.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/deepseek-v3_2.jpg","labels":["cloudPartnerType:endpoint:cloud_partner_type_bitdeer","cloudPartnerType:endpoint:cloud_partner_type_deepinfra","cloudPartnerType:endpoint:cloud_partner_type_gmicloud","cloudPartnerType:endpoint:cloud_partner_type_vultr","long context","nimType:endpoint:nim_type_preview","nimType:endpoint:nim_type_upgrade_available","playgroundType:endpoint:playgroundtype_chat","reasoning","text-to-text"],"attributes":[{"key":"AVAILABLE","value":"false"},{"key":"DEPRECATION","value":"05/04/2026"},{"key":"PREVIEW","value":"true"}],"updatedDate":"2025-12-16T17:25:38.554Z","canGuestDownload":true,"createdDate":"2025-12-16T17:25:38.554Z","description":"$4d","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy"},"specAttributes":"$44:props:spec:attributes","modelPath":"/deepseek-ai/deepseek-v3_2","openApiSpec":{"openapi":"3.1.0","info":{"title":"NVIDIA NIM API for deepseek-ai/deepseek-v3.2","description":"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/deepseek-ai-deepseek-v3_2 for more details.","version":"1.0.0","termsOfService":"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/","contact":{"name":"NVIDIA Enterprise Support","url":"https://www.nvidia.com/en-us/support/enterprise/"},"license":{"name":"Jamba Open License Agreement","url":"https://assets.ngc.nvidia.com/products/api-catalog/legal/Jamba_Open_Model_License_Agreement.pdf"}},"servers":[{"url":"https://integrate.api.nvidia.com/v1/"}],"tags":[{"name":"NVCF API","description":"Run inference on the model"}],"paths":{"/chat/completions":{"post":{"operationId":"create_chat_completion_v1_chat_completions_post","tags":["Chat"],"summary":"Creates a model response for the given chat conversation.","description":"Given a list of messages comprising a conversation, the model will return a response. Compatible with OpenAI. See https://platform.openai.com/docs/api-reference/chat/create","requestBody":{"content":{"application/json":{"schema":{"additionalProperties":false,"properties":{"model":{"type":"string","title":"Model","default":"deepseek-ai/deepseek-v3.2"},"messages":{"description":"A list of messages comprising the conversation so far. The roles of the messages must be alternating between `user` and `assistant`. The last input message should have role `user`. A message with the the `system` role is optional, and must be the very first message if it is present; `context` is also optional, but must come before a user question.","examples":[[{"content":"I am going to Paris, what should I see?","role":"user"}]],"items":{"additionalProperties":false,"properties":{"role":{"description":"The role of the message author.","enum":["system","context","user","assistant"],"title":"Role","type":"string"},"content":{"description":"The contents of the message.","title":"Content","type":"string"}},"required":["role","content"],"title":"Message","type":"object"},"title":"Messages","type":"array"},"temperature":{"default":1,"description":"The sampling temperature to use for text generation. The higher the temperature value is, the less deterministic the output text will be. It is not recommended to modify both temperature and top_p in the same call.","maximum":1,"exclusiveMinimum":0,"title":"Temperature","type":"number"},"top_p":{"default":0.95,"description":"The top-p sampling mass used for text generation. The top-p value determines the probability mass that is sampled at sampling time. For example, if top_p = 0.2, only the most likely tokens (summing to 0.2 cumulative probability) will be sampled. It is not recommended to modify both temperature and top_p in the same call.","maximum":1,"exclusiveMinimum":0,"title":"Top P","type":"number"},"max_tokens":{"default":8192,"description":"The maximum number of tokens to generate in any given call. Note that the model is not aware of this value, and generation will simply stop at the number of tokens specified.","maximum":16384,"minimum":1,"title":"Max Tokens","type":"integer"},"seed":{"anyOf":[{"maximum":18446744073709552000,"minimum":0,"type":"integer"},{"type":"null"}],"default":null,"description":"If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.","examples":[42],"title":"Seed"},"stream":{"default":false,"description":"If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events (SSE) as they become available (JSON responses are prefixed by `data: `), with the stream terminated by a `data: [DONE]` message.","title":"Stream","type":"boolean"}},"required":["messages"],"title":"ChatRequest","type":"object"}}},"required":true},"responses":{"200":{"description":"Invocation is fulfilled","content":{"application/json":{"schema":{"properties":{"id":{"description":"A unique identifier for the completion.","format":"uuid","title":"Id","type":"string"},"choices":{"description":"The list of completion choices the model generated for the input prompt.","items":{"properties":{"index":{"description":"The index of the choice in the list of choices (always 0).","title":"Index","type":"integer"},"message":{"allOf":[{"additionalProperties":false,"properties":{"role":{"description":"The role of the message author.","enum":["system","context","user","assistant"],"title":"Role","type":"string"},"content":{"description":"The contents of the message.","title":"Content","type":"string"}},"required":["role","content"],"title":"Message","type":"object"}],"description":"A chat completion message generated by the model.","examples":[{"content":"Ah, Paris, the City of Light! There are so many amazing things to see and do in this beautiful city ...","role":"assistant"}]},"finish_reason":{"anyOf":[{"enum":["stop","length"],"type":"string"},{"type":"null"}],"default":null,"description":"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached.","examples":["stop"],"title":"Finish Reason"}},"required":["index","message"],"title":"Choice","type":"object"},"title":"Choices","type":"array"},"usage":{"allOf":[{"properties":{"completion_tokens":{"description":"Number of tokens in the generated completion.","examples":[25],"title":"Completion Tokens","type":"integer"},"prompt_tokens":{"description":"Number of tokens in the prompt.","examples":[9],"title":"Prompt Tokens","type":"integer"},"total_tokens":{"description":"Total number of tokens used in the request (prompt + completion).","examples":[34],"title":"Total Tokens","type":"integer"}},"required":["completion_tokens","prompt_tokens","total_tokens"],"title":"Usage","type":"object"}],"description":"Usage statistics for the completion request."}},"required":["id","choices","usage"],"title":"ChatCompletion","type":"object"}},"text/event-stream":{"schema":{"properties":{"id":{"description":"A unique identifier for the completion.","format":"uuid","title":"Id","type":"string"},"choices":{"description":"The list of completion choices the model generated for the input prompt.","items":{"properties":{"index":{"description":"The index of the choice in the list of choices (always 0).","title":"Index","type":"integer"},"delta":{"allOf":[{"additionalProperties":false,"properties":{"role":{"description":"The role of the message author.","enum":["system","context","user","assistant"],"title":"Role","type":"string"},"content":{"description":"The contents of the message.","title":"Content","type":"string"}},"required":["role","content"],"title":"Message","type":"object"}],"description":"A chat completion delta generated by streamed model responses.","examples":[{"content":"Ah,","role":"assistant"}]},"finish_reason":{"anyOf":[{"enum":["stop","length"],"type":"string"},{"type":"null"}],"default":null,"description":"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached. Will be `null` if the model has not finished generating.","title":"Finish Reason"}},"required":["index","delta"],"title":"ChoiceChunk","type":"object"},"title":"Choices","type":"array"}},"required":["id","choices"],"title":"ChatCompletionChunk","type":"object"}}}},"202":{"description":"Result is pending. 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