--- description: Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support. title: qwen3-30b-a3b-fp8 image: https://developers.cloudflare.com/og-docs.png --- [Skip to content](#main-content) > Documentation Index > Fetch the complete documentation index at: https://developers.cloudflare.com/llms.txt > Use this file to discover all available pages before exploring further. ![Qwen logo](https://developers.cloudflare.com/_astro/qwen.ByCZjtXU.svg) # qwen3-30b-a3b-fp8 Text Generation • Qwen Copy as Markdown|[View as Markdown](https://developers.cloudflare.com/workers-ai/models/qwen3-30b-a3b-fp8/index.md)|[Agent setup](https://developers.cloudflare.com/agent-setup/) `@cf/qwen/qwen3-30b-a3b-fp8` * Cloudflare-hosted * Batch * Function calling * Reasoning Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support. | Model Info | | | ------------------------------------------------------------------------------------- | ---------------------------------------------------- | | Context Window[ ↗](https://developers.cloudflare.com/workers-ai/platform/glossary/) | 32,768 tokens | | Function calling [ ↗](https://developers.cloudflare.com/workers-ai/function-calling/) | Yes | | Reasoning | Yes | | Batch | Yes | | Unit Pricing | $0.051 per M input tokens, $0.34 per M output tokens | ## Playground Try out this model with Workers AI LLM Playground. It does not require any setup or authentication and is an instant way to preview and test a model directly in the browser. [Launch the LLM Playground](https://playground.ai.cloudflare.com/?model=@cf/qwen/qwen3-30b-a3b-fp8) ## Usage ```ts export interface Env { AI: Ai; } export default { async fetch(request, env): Promise { const messages = [ { role: "system", content: "You are a friendly assistant" }, { role: "user", content: "What is the origin of the phrase Hello, World", }, ]; const stream = await env.AI.run("@cf/qwen/qwen3-30b-a3b-fp8", { messages, stream: true, }); return new Response(stream, { headers: { "content-type": "text/event-stream" }, }); }, } satisfies ExportedHandler; ``` ```ts export interface Env { AI: Ai; } export default { async fetch(request, env): Promise { const messages = [ { role: "system", content: "You are a friendly assistant" }, { role: "user", content: "What is the origin of the phrase Hello, World", }, ]; const response = await env.AI.run("@cf/qwen/qwen3-30b-a3b-fp8", { messages }); return Response.json(response); }, } satisfies ExportedHandler; ``` ```py import os import requests ACCOUNT_ID = "your-account-id" AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN") prompt = "Tell me all about PEP-8" response = requests.post( f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/qwen/qwen3-30b-a3b-fp8", headers={"Authorization": f"Bearer {AUTH_TOKEN}"}, json={ "messages": [ {"role": "system", "content": "You are a friendly assistant"}, {"role": "user", "content": prompt} ] } ) result = response.json() print(result) ``` ```sh curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/qwen/qwen3-30b-a3b-fp8 \ -X POST \ -H "Authorization: Bearer $CLOUDFLARE_AUTH_TOKEN" \ -d '{ "messages": [{ "role": "system", "content": "You are a friendly assistant" }, { "role": "user", "content": "Why is pizza so good" }]}' ``` OpenAI compatible endpoints Workers AI also supports OpenAI compatible API endpoints for `/v1/chat/completions` and `/v1/embeddings`. For more details, refer to [Configurations](https://developers.cloudflare.com/workers-ai/configuration/open-ai-compatibility/). ## Parameters Synchronous — Send a request and receive a complete response prompt `string`requiredminLength: 1The input text prompt for the model to generate a response. lora `string`Name of the LoRA (Low-Rank Adaptation) model to fine-tune the base model. ▶response\_format{} `object` raw `boolean`default: falseIf true, a chat template is not applied and you must adhere to the specific model's expected formatting. stream `boolean`default: falseIf true, the response will be streamed back incrementally using SSE, Server Sent Events. max\_tokens `integer`default: 2000The maximum number of tokens to generate in the response. temperature `number`default: 0.6minimum: 0maximum: 5Controls the randomness of the output; higher values produce more random results. top\_p `number`minimum: 0.001maximum: 1Adjusts the creativity of the AI's responses by controlling how many possible words it considers. Lower values make outputs more predictable; higher values allow for more varied and creative responses. top\_k `integer`minimum: 1maximum: 50Limits the AI to choose from the top 'k' most probable words. Lower values make responses more focused; higher values introduce more variety and potential surprises. seed `integer`minimum: 1maximum: 9999999999Random seed for reproducibility of the generation. repetition\_penalty `number`minimum: 0maximum: 2Penalty for repeated tokens; higher values discourage repetition. frequency\_penalty `number`minimum: \-2maximum: 2Decreases the likelihood of the model repeating the same lines verbatim. presence\_penalty `number`minimum: \-2maximum: 2Increases the likelihood of the model introducing new topics. id `string`Unique identifier for the completion object `string`enum: chat.completionObject type identifier created `number`Unix timestamp of when the completion was created model `string`Model used for the completion ▶choices\[\] `array`List of completion choices ▶usage{} `object`Usage statistics for the inference request prompt\_logprobs{} `object`Log probabilities for the prompt (if requested) Streaming — Send a request with \`stream: true\` and receive server-sent events prompt `string`requiredminLength: 1The input text prompt for the model to generate a response. lora `string`Name of the LoRA (Low-Rank Adaptation) model to fine-tune the base model. ▶response\_format{} `object` raw `boolean`default: falseIf true, a chat template is not applied and you must adhere to the specific model's expected formatting. stream `boolean`default: falseIf true, the response will be streamed back incrementally using SSE, Server Sent Events. max\_tokens `integer`default: 2000The maximum number of tokens to generate in the response. temperature `number`default: 0.6minimum: 0maximum: 5Controls the randomness of the output; higher values produce more random results. top\_p `number`minimum: 0.001maximum: 1Adjusts the creativity of the AI's responses by controlling how many possible words it considers. Lower values make outputs more predictable; higher values allow for more varied and creative responses. top\_k `integer`minimum: 1maximum: 50Limits the AI to choose from the top 'k' most probable words. Lower values make responses more focused; higher values introduce more variety and potential surprises. seed `integer`minimum: 1maximum: 9999999999Random seed for reproducibility of the generation. repetition\_penalty `number`minimum: 0maximum: 2Penalty for repeated tokens; higher values discourage repetition. frequency\_penalty `number`minimum: \-2maximum: 2Decreases the likelihood of the model repeating the same lines verbatim. presence\_penalty `number`minimum: \-2maximum: 2Increases the likelihood of the model introducing new topics. type `string` contentType `text/event-stream` format `binary` Batch — Send multiple requests in a single API call ▶requests\[\] `array`required id `string`Unique identifier for the completion object `string`enum: chat.completionObject type identifier created `number`Unix timestamp of when the completion was created model `string`Model used for the completion ▶choices\[\] `array`List of completion choices ▶usage{} `object`Usage statistics for the inference request prompt\_logprobs{} `object`Log probabilities for the prompt (if requested) ## API Schemas (Raw) SynchronousInput SynchronousOutput StreamingInput StreamingOutput BatchInput BatchOutput Was this helpful? 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