4d:I[510592,["/_next/static/chunks/0410xx54a0zpq.js","/_next/static/chunks/3pd35vh8xvq70.js","/_next/static/chunks/32q37d2vm9h_s.js","/_next/static/chunks/14sl9ye0a60vb.js","/_next/static/chunks/1la1pzs9j8idc.js","/_next/static/chunks/24mqrfe34h1_g.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/2g-r6rokjl78a.js","/_next/static/chunks/1geiruffics9x.js","/_next/static/chunks/1lfygqrocy8iw.js","/_next/static/chunks/0q47y720ck5-9.js","/_next/static/chunks/1ttnpl_ih9zxl.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/2drnqf6qy04sf.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/1i60vi-4s7c4l.js","/_next/static/chunks/23fi4qujvabmc.js","/_next/static/chunks/295ffwyuqa4b6.js"],"PlaygroundContextProvider"] 4e:T2a21,# GLM-5.1 ## Description:
GLM-5.1 is a next-generation flagship large language model designed for agentic engineering, featuring significantly stronger coding capabilities than its predecessor GLM-5. It sustains optimization over hundreds of reasoning rounds and thousands of tool calls, enabling it to solve complex software engineering tasks with extended reasoning horizons and iterative refinement.
GLM-5.1 was developed by Z.ai (zai-org) as a part of the GLM model family.
*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 [GLM-5.1 Model Card](https://huggingface.co/zai-org/GLM-5.1).
### License/Terms of Use:
**GOVERNING TERMS:** The 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 Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). **ADDITIONAL INFORMATION:** [MIT License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md). ### Deployment Geography:
Global ### Use Case:
Developers and researchers can use GLM-5.1 for complex software engineering tasks, agentic workflows requiring long-horizon planning and tool use, mathematical reasoning, terminal-based automation, code generation and debugging, and general conversational AI applications.
## Release Date: **Build.NVIDIA.com:** 4/17/2026 via [link](https://build.nvidia.com/z-ai/glm-5.1) **NGC:** 04/15/2026 via [link](https://catalog.ngc.nvidia.com/orgs/nim/teams/zai-org/containers/glm-51?version=1.7.0-variant)
**HuggingFace:** 04/08/2026 via [link](https://huggingface.co/zai-org/GLM-5.1)
## Reference(s): **References:** - [GLM-5.1 on Hugging Face](https://huggingface.co/zai-org/GLM-5.1)
- [GLM-5 Technical Report (arXiv:2602.15763)](https://arxiv.org/abs/2602.15763)
- [GLM-5.1 Blog Post](https://z.ai/blog/glm-5.1)
- [Z.ai API Platform](https://docs.z.ai/guides/llm/glm-5.1)
- [GitHub Repository](https://github.com/zai-org/GLM-5)
## Model Architecture: **Architecture Type:** Mixture of Experts (MoE)
**Network Architecture:** GLM (General Language Model) with DSA (Dense-Sparse-Alternating)
**This model was developed based on GLM-5.**
**Number of model parameters:** 754B
## Input:
**Input Type(s):** Text
**Input Format(s):** String
**Input Parameters:** One Dimensional (1D)
**Other Properties Related to Input:** Supports multi-turn conversations, tool calling, system prompts, and extended agentic sessions. Input context length: 131,072 tokens.
## Output:
**Output Type(s):** Text
**Output Format:** String
**Output Parameters:** One Dimensional (1D)
**Other Properties Related to Output:** Supports streaming, structured output, reasoning traces, and tool call responses. Output context length: 131,072 tokens.
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 Engine(s):**
* vLLM
**Supported Hardware Microarchitecture Compatibility:**
* NVIDIA Blackwell
* NVIDIA Hopper
**Supported Operating System(s):**
* Linux
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): GLM v5.1
## Training, Testing, and Evaluation Datasets: ### Training Dataset **Data Modality:** Text (English, Chinese, multilingual) **Text Training Data Size:** Undisclosed **Training Data Collection:** Undisclosed **Training Labeling:** Undisclosed **Training Properties:** Undisclosed ### Testing Dataset **Testing Data Collection:** Undisclosed **Testing Labeling:** Undisclosed **Testing Properties:** Undisclosed ### Evaluation Dataset **Evaluation Benchmark Score:** Evaluated on multiple benchmarks including SWE-Bench Pro, Terminal-Bench 2.0, CyberGym, NL2Repo, AIME 2026, HMMT, GPQA-Diamond, IMOAnswerBench, HLE w/ Tools, BrowseComp, τ³-Bench, MCP-Atlas, and Tool-Decathlon. **Evaluation Data Collection:** Automated **Evaluation Labeling:** Human **Evaluation Properties:** GLM-5.1 achieves state-of-the-art results across coding, mathematical reasoning, and agentic benchmarks, outperforming its predecessor GLM-5 on all major evaluations. ### Evaluation Results

Detailed Benchmark Results

### Coding & Engineering | Benchmark | Dataset | GLM-5.1 | GLM-5 | |-----------|---------|---------|-------| | SWE-Bench Pro | [ScaleAI/SWE-bench_Pro](https://huggingface.co/datasets/ScaleAI/SWE-bench_Pro) | **58.4%** | 55.1% | | NL2Repo | — | **42.7%** | 35.9% | | Terminal-Bench 2.0 | [harborframework/terminal-bench-2.0](https://huggingface.co/datasets/harborframework/terminal-bench-2.0) | **63.5%** | 56.2% | | Terminal-Bench 2.0 (Claude Code) | [harborframework/terminal-bench-2.0](https://huggingface.co/datasets/harborframework/terminal-bench-2.0) | **69.0%** | — | | CyberGym | — | **68.7%** | — | ### Math & Reasoning | Benchmark | Dataset | GLM-5.1 | |-----------|---------|---------| | AIME 2026 | [MathArena/aime_2026](https://huggingface.co/datasets/MathArena/aime_2026) | 95.3% | | HMMT Nov. 2025 | — | 94.0% | | HMMT Feb. 2026 | [MathArena/hmmt_feb_2026](https://huggingface.co/datasets/MathArena/hmmt_feb_2026) | 82.6% | | GPQA-Diamond | [Idavidrein/gpqa](https://huggingface.co/datasets/Idavidrein/gpqa) | 86.2% | | IMOAnswerBench | — | 83.8% | ### Tool Use & Agentic | Benchmark | Dataset | GLM-5.1 | |-----------|---------|---------| | HLE | [cais/hle](https://huggingface.co/datasets/cais/hle) | 31.0% | | HLE (w/ Tools) | [cais/hle](https://huggingface.co/datasets/cais/hle) | 52.3% | | BrowseComp | — | 68.0% | | BrowseComp (w/ Context Manage) | — | 79.3% | | Tool-Decathlon | — | 40.7% | | τ³-Bench | — | 70.6% | | MCP-Atlas (Public Set) | — | 71.8% |

Detailed Benchmark Results

## Inference: **Acceleration Engine:** vLLM
**Test Hardware:**
* NVIDIA GB200x4
## 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/).
4f:Tac6,from openai import OpenAI import os import sys _USE_COLOR = sys.stdout.isatty() and os.getenv("NO_COLOR") is None _REASONING_COLOR = "\033[90m" if _USE_COLOR else "" _RESET_COLOR = "\033[0m" if _USE_COLOR else "" client = OpenAI( base_url = "https://integrate.api.nvidia.com/v1", api_key = "$NVIDIA_API_KEY" ) <% const extra_body = {}; if (request.chat_template_kwargs) { extra_body["chat_template_kwargs"] = request.chat_template_kwargs; } const extra_body_py = Object.keys(extra_body).length ? JSON.stringify(extra_body) .replaceAll("true", "True") .replaceAll("false", "False") .replaceAll("null", "None") : null; %> <% 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 %>, <%- extra_body_py ? 'extra_body=' + extra_body_py + ',' : '' %> 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 %>, <%- extra_body_py ? 'extra_body=' + extra_body_py + ',' : '' %> 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 len(chunk.choices) == 0 or getattr(chunk.choices[0], "delta", None) is None: continue delta = chunk.choices[0].delta <% if (request.chat_template_kwargs && request.chat_template_kwargs.enable_thinking) { -%> reasoning = getattr(delta, "reasoning_content", None) if reasoning: print(f"{_REASONING_COLOR}{reasoning}{_RESET_COLOR}", end="") <% } -%> if getattr(delta, "content", None) is not None: print(delta.content, end="") <% if (request.tools) { %>if getattr(delta, "tool_calls", None): print(delta.tool_calls)<% } %> <% } else { %> <% if (request.chat_template_kwargs && request.chat_template_kwargs.enable_thinking) { -%> reasoning = getattr(completion.choices[0].message, "reasoning_content", None) if reasoning: print(f"{_REASONING_COLOR}{reasoning}{_RESET_COLOR}") <% } -%> print(completion.choices[0].message.content) <% if (request.tools) { %>if completion.choices[0].message.tool_calls: print(completion.choices[0].message.tool_calls)<% } %> <% } %> 50:T580,from langchain_nvidia_ai_endpoints import ChatNVIDIA <% const lc_extra_body = {}; if (request.chat_template_kwargs) { lc_extra_body["chat_template_kwargs"] = request.chat_template_kwargs; } const lc_extra_body_py = Object.keys(lc_extra_body).length ? JSON.stringify(lc_extra_body) .replaceAll("true", "True") .replaceAll("false", "False") .replaceAll("null", "None") : null; %> client = ChatNVIDIA( model="<%- request.model %>", api_key="$NVIDIA_API_KEY", temperature=<%- request.temperature %>, top_p=<%- request.top_p %>, max_tokens=<%- request.max_tokens %>, <%- lc_extra_body_py ? 'extra_body=' + lc_extra_body_py + ',' : '' %> ) <% if (request.stream) { %> for chunk in client.stream(<%- JSON.stringify(request.messages) %>): <% if (request.chat_template_kwargs && request.chat_template_kwargs.enable_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.enable_thinking) { -%> if response.additional_kwargs and "reasoning_content" in response.additional_kwargs: print(response.additional_kwargs["reasoning_content"]) <% } -%> print(response.content) <% } %> 51:T8d9,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.enable_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.enable_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();52:T713,<% if (request.tools) { %> "authorization_header=\"Authorization: Bearer $NVIDIA_API_KEY\"\n\n curl https://integrate.api.nvidia.com/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"$authorization_header\" \\\n -d '{\n \"model\": \"z-ai/glm-5.1\",\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 { %> "authorization_header=\"Authorization: Bearer $NVIDIA_API_KEY\"\n\n curl https://integrate.api.nvidia.com/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"$authorization_header\" \\\n -d '{\n \"model\": \"z-ai/glm-5.1\",\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"<% } %>4c:["$","$L4d",null,{"initialState":{"artifact":{"artifactType":"ENDPOINT","name":"glm-5.1","displayName":"glm-5.1","publisher":"z-ai","shortDescription":"GLM-5.1 is a flagship LLM for agentic workflows, coding, and long-horizon reasoning tasks.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/glm5_1.jpg","labels":["Agentic AI","Coding","Reasoning","Tool Use","cloudPartnerType:endpoint:cloud_partner_type_bitdeer","cloudPartnerType:endpoint:cloud_partner_type_deepinfra","cloudPartnerType:endpoint:cloud_partner_type_openrouter","cloudPartnerType:endpoint:cloud_partner_type_together","nimType:endpoint:nim_type_preview","nimType:endpoint:nim_type_run_anywhere","nimType:endpoint:nim_type_upgrade_available","playgroundType:endpoint:playgroundtype_chat"],"attributes":[{"key":"AVAILABLE","value":"true"},{"key":"DEPRECATION","value":"07/02/2026"},{"key":"PREVIEW","value":"true"}],"updatedDate":"2026-04-18T02:44:50.349Z","canGuestDownload":true,"createdDate":"2026-04-18T02:44:50.349Z","description":"$4e","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy"},"specAttributes":"$44:props:spec:attributes","modelPath":"/z-ai/glm-5.1","openApiSpec":{"openapi":"3.1.0","info":{"title":"NVIDIA NIM API for z-ai/glm5.1","description":"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/z-ai-glm5-1 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":"Model license","url":"https://huggingface.co/zai-org/GLM-5.1"}},"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":"z-ai/glm-5.1"},"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":1,"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":16384,"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":32768,"minimum":1,"title":"Max Tokens","type":"integer"},"seed":{"anyOf":[{"maximum":18446744073709552000,"minimum":0,"type":"integer"},{"type":"null"}],"default":42,"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":true,"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. 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