> ## Documentation Index > Fetch the complete documentation index at: https://docs.modelstudio.console.alibabacloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Long context (Qwen-Long) > Qwen-Long handles documents up to 10 million tokens through a file upload and reference mechanism, overcoming standard model context limits. This document applies only to the Chinese mainland (Beijing) region. To use the model, you must use an API key from the[Chinese mainland (Beijing)](https://modelstudio.console.alibabacloud.com/model/settings/api-key) region. ## How to use Use Qwen-Long in two steps: upload files, then call the API. 1. File upload and parsing: - Upload a file using the API. For details about supported file formats and size limits, see [Supported formats](/en/model-studio/long-context-qwen-long). - After a successful upload, the system returns a unique `file-id` for your account and starts parsing. No fees are charged for file upload, storage, or parsing. 2. API call and billing: - When you call the model, reference one or more `file-id`s in the `system` message. - The model performs inference based on the text content associated with the `file-id`. - For each API call, the number of tokens in the referenced file content is counted as input tokens for that request. This avoids transferring large files in each request, but note that file tokens are billed per API call. ## Getting started ### Prerequisites - Obtain an [API key](/en/model-studio/get-api-key) and [configure it as an environment variable](/en/model-studio/get-api-key). - To call the model via SDK, install the [OpenAI SDK](/en/model-studio/install-sdk). ### Upload a document This example uploads [Model\_Studio\_Phone\_Product\_Introduction.docx](https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/en-US/20250805/pzubre/Bailian+Phones+Specifications.docx) to Model Studio's secure storage via the OpenAI-compatible interface and gets a `file-id`. See the [API documentation](/en/model-studio/openai-file-interface) for upload parameters. ```python Python import os from pathlib import Path from openai import OpenAI client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # If not configured, replace with your API key. # The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) file_object = client.files.create(file=Path("Model_Studio_Phone_Product_Introduction.docx"), purpose="file-extract") print(file_object.id) ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.models.files.*; import java.nio.file.Path; import java.nio.file.Paths; public class Main { public static void main(String[] args) { // Create a client and use the API key from the environment variable. OpenAIClient client = OpenAIOkHttpClient.builder() // If you have not configured the environment variable, replace the following line with: .apiKey("sk-xxx") .apiKey(System.getenv("DASHSCOPE_API_KEY")) // The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Set the file path. Modify the path and filename as needed. Path filePath = Paths.get("src/main/java/org/example/Model_Studio_Phone_Product_Introduction.docx"); // Create file upload parameters. FileCreateParams fileParams = FileCreateParams.builder() .file(filePath) .purpose(FilePurpose.of("file-extract")) .build(); // Upload the file and print the file-id. FileObject fileObject = client.files().create(fileParams); System.out.println(fileObject.id()); } } ``` ```bash curl # The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location --request POST 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/files' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --form 'file=@"Alibaba Cloud Model Studio Phone Series Product Introduction.docx"' \ --form 'purpose="file-extract"' ``` Run the code to obtain the `file-id` for the uploaded file. ### Pass information and chat using a file ID Pass the `file-id` in system messages: first message defines the role, second contains the `file-id`, then add user questions. > Longer documents need more parsing time. Wait for parsing to complete before calling. ```python Python expandable import os from openai import OpenAI, BadRequestError client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # If not configured, replace with your API key. # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) try: # Initialize messages list. completion = client.chat.completions.create( model="qwen-long", messages=[ # sys1: Role definition. {'role': 'system', 'content': 'You are a helpful assistant.'}, # sys2: Document content (plain text or file-id). # Replace '{FILE_ID}' with the file-id used in your conversation. {'role': 'system', 'content': f'fileid://{FILE_ID}'}, # When the request includes a second system message, the user message content is limited to 9,000 tokens. {'role': 'user', 'content': 'What is this article about?'} ], # All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation stream=True, stream_options={"include_usage": True} ) full_content = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content: # Concatenate the output content. full_content += chunk.choices[0].delta.content print(chunk.model_dump()) # Get token usage. if chunk.usage: print(f"Total tokens: {chunk.usage.total_tokens}") print(full_content) except BadRequestError as e: print(f"Error: {e}") print("See documentation: https://www.alibabacloud.com/help/model-studio/error-code") ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.core.http.StreamResponse; import com.openai.models.chat.completions.*; public class Main { public static void main(String[] args) { // Create a client and use the API key from the environment variable. OpenAIClient client = OpenAIOkHttpClient.builder() // If you have not configured the environment variable, replace the following line with: .apiKey("sk-xxx") .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Create a chat request. ChatCompletionCreateParams chatParams = ChatCompletionCreateParams.builder() //sys1: Role definition. .addSystemMessage("You are a helpful assistant.") //sys2: Document content (plain text or file-id). //Replace '{FILE_ID}' with the file-id used in your conversation. .addSystemMessage("fileid://{FILE_ID}") //When the request includes a second system message, the user message content is limited to 9,000 tokens. .addUserMessage("What is this article about?") .model("qwen-long") .build(); StringBuilder fullResponse = new StringBuilder(); // All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation try (StreamResponse streamResponse = client.chat().completions().createStreaming(chatParams)) { streamResponse.stream().forEach(chunk -> { // Print and concatenate the content of each chunk. System.out.println(chunk); String content = chunk.choices().get(0).delta().content().orElse(""); if (!content.isEmpty()) { fullResponse.append(content); } }); System.out.println(fullResponse); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("See documentation: https://www.alibabacloud.com/help/model-studio/error-code"); } } } ``` ```bash curl # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-long", "messages": [ {"role": "system","content": "You are a helpful assistant."}, {"role": "system","content": "fileid://file-fe-xxx"}, {"role": "user","content": "What is this article about?"} ], "stream": true, "stream_options": { "include_usage": true } }' ``` ### Pass multiple documents Pass multiple `file-id`s in one system message or add separate system messages for each document. ```python Python expandable import os from openai import OpenAI, BadRequestError client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # If not configured, replace with your API key. # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) try: # Initialize messages list. completion = client.chat.completions.create( model="qwen-long", messages=[ {'role': 'system', 'content': 'You are a helpful assistant.'}, # Replace '{FILE_ID1}' and '{FILE_ID2}' with the file-ids used in your conversation. {'role': 'system', 'content': f"fileid://{FILE_ID1},fileid://{FILE_ID2}"}, {'role': 'user', 'content': 'What are these articles about?'} ], # All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation stream=True, stream_options={"include_usage": True} ) full_content = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content: # Concatenate the output content. full_content += chunk.choices[0].delta.content print(chunk.model_dump()) print(full_content) except BadRequestError as e: print(f"Error: {e}") print("See documentation: https://www.alibabacloud.com/help/model-studio/error-code") ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.core.http.StreamResponse; import com.openai.models.chat.completions.*; public class Main { public static void main(String[] args) { // Create a client and use the API key from the environment variable. OpenAIClient client = OpenAIOkHttpClient.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Create a chat request. ChatCompletionCreateParams chatParams = ChatCompletionCreateParams.builder() .addSystemMessage("You are a helpful assistant.") //Replace '{FILE_ID1}' and '{FILE_ID2}' with the file-ids used in your conversation. .addSystemMessage("fileid://{FILE_ID1},fileid://{FILE_ID2}") .addUserMessage("What are these two articles about?") .model("qwen-long") .build(); StringBuilder fullResponse = new StringBuilder(); // All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation try (StreamResponse streamResponse = client.chat().completions().createStreaming(chatParams)) { streamResponse.stream().forEach(chunk -> { // The content of each chunk. System.out.println(chunk); String content = chunk.choices().get(0).delta().content().orElse(""); if (!content.isEmpty()) { fullResponse.append(content); } }); System.out.println("\nFull response content:"); System.out.println(fullResponse); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("See documentation: https://www.alibabacloud.com/help/model-studio/error-code"); } } } ``` ```bash curl # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-long", "messages": [ {"role": "system","content": "You are a helpful assistant."}, {"role": "system","content": "fileid://file-fe-xxx1"}, {"role": "system","content": "fileid://file-fe-xxx2"}, {"role": "user","content": "What are these two articles about?"} ], "stream": true, "stream_options": { "include_usage": true } }' ``` ```python Python expandable import os from openai import OpenAI, BadRequestError client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # If not configured, replace with your API key. # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) # Initialize the messages list. messages = [ {'role': 'system', 'content': 'You are a helpful assistant.'}, # Replace '{FILE_ID1}' with the file-id used in your conversation. {'role': 'system', 'content': f'fileid://{FILE_ID1}'}, {'role': 'user', 'content': 'What is this article about?'} ] try: # First-round response completion_1 = client.chat.completions.create( model="qwen-long", messages=messages, stream=False ) # Print first-round response. # To stream: set stream=True, concatenate segments, and pass to assistant_message content. print(f"First-round response: {completion_1.choices[0].message.model_dump()}") except BadRequestError as e: print(f"Error: {e}") print("See documentation: https://www.alibabacloud.com/help/model-studio/error-code") # Construct the assistant_message. assistant_message = { "role": "assistant", "content": completion_1.choices[0].message.content} # Add assistant_message to messages. messages.append(assistant_message) # Add the file-id of the appended document to messages. # Replace '{FILE_ID2}' with the file-id used in your conversation. system_message = {'role': 'system', 'content': f'fileid://{FILE_ID2}'} messages.append(system_message) # Add the user's question. messages.append({'role': 'user', 'content': 'What are the similarities and differences between the methods discussed in these two articles?'}) # Response after appending the document. completion_2 = client.chat.completions.create( model="qwen-long", messages=messages, # All code examples use streaming output to clearly and intuitively show the model output process. For non-streaming output examples, see https://www.alibabacloud.com/help/model-studio/text-generation stream=True, stream_options={ "include_usage": True } ) # Stream and print the response after appending the document. print("Response after appending the document:") for chunk in completion_2: print(chunk.model_dump()) ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.models.chat.completions.*; import com.openai.core.http.StreamResponse; import java.util.ArrayList; import java.util.List; public class Main { public static void main(String[] args) { OpenAIClient client = OpenAIOkHttpClient.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Initialize messages list. List messages = new ArrayList<>(); // Add information for role setting. ChatCompletionSystemMessageParam roleSet = ChatCompletionSystemMessageParam.builder() .content("You are a helpful assistant.") .build(); messages.add(ChatCompletionMessageParam.ofSystem(roleSet)); // Replace '{FILE_ID1}' with the file-id used in your conversation. ChatCompletionSystemMessageParam systemMsg1 = ChatCompletionSystemMessageParam.builder() .content("fileid://{FILE_ID1}") .build(); messages.add(ChatCompletionMessageParam.ofSystem(systemMsg1)); // User question message (USER role). ChatCompletionUserMessageParam userMsg1 = ChatCompletionUserMessageParam.builder() .content("Please summarize the article content.") .build(); messages.add(ChatCompletionMessageParam.ofUser(userMsg1)); // Construct the first-round request and handle exceptions. ChatCompletion completion1; try { completion1 = client.chat().completions().create( ChatCompletionCreateParams.builder() .model("qwen-long") .messages(messages) .build() ); } catch (Exception e) { System.err.println("Request error. See error code page:"); System.err.println("https://www.alibabacloud.com/help/model-studio/error-code"); System.err.println("Error details: " + e.getMessage()); e.printStackTrace(); return; } // First-round response. String firstResponse = completion1 != null ? completion1.choices().get(0).message().content().orElse("") : ""; System.out.println("First-round response: " + firstResponse); // Construct AssistantMessage. ChatCompletionAssistantMessageParam assistantMsg = ChatCompletionAssistantMessageParam.builder() .content(firstResponse) .build(); messages.add(ChatCompletionMessageParam.ofAssistant(assistantMsg)); // Replace '{FILE_ID2}' with the file-id used in your conversation. ChatCompletionSystemMessageParam systemMsg2 = ChatCompletionSystemMessageParam.builder() .content("fileid://{FILE_ID2}") .build(); messages.add(ChatCompletionMessageParam.ofSystem(systemMsg2)); // Second-round user question (USER role). ChatCompletionUserMessageParam userMsg2 = ChatCompletionUserMessageParam.builder() .content("Please compare the structural differences between the two articles.") .build(); messages.add(ChatCompletionMessageParam.ofUser(userMsg2)); // All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation StringBuilder fullResponse = new StringBuilder(); try (StreamResponse streamResponse = client.chat().completions().createStreaming( ChatCompletionCreateParams.builder() .model("qwen-long") .messages(messages) .build())) { streamResponse.stream().forEach(chunk -> { String content = chunk.choices().get(0).delta().content().orElse(""); if (!content.isEmpty()) { fullResponse.append(content); } }); System.out.println("\nFinal response:"); System.out.println(fullResponse.toString().trim()); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("See documentation: https://www.alibabacloud.com/help/model-studio/error-code"); } } } ``` ```bash curl # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-long", "messages": [ {"role": "system","content": "You are a helpful assistant."}, {"role": "system","content": "fileid://file-fe-xxx1"}, {"role": "user","content": "What is this article about?"}, {"role": "system","content": "fileid://file-fe-xxx2"}, {"role": "user","content": "What are the similarities and differences between the methods discussed in these two articles?"} ], "stream": true, "stream_options": { "include_usage": true } }' ``` ## Pass information as plain text Instead of using `file-id`s, pass document content directly as a string. Add role settings in the first message to prevent confusion with document content. > If document content exceeds 1 million tokens, use a file ID instead due to API size limits. You can input the document content directly into the System Message. ```python Python expandable import os from openai import OpenAI client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace your API key here if you haven't set the environment variable # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) # Initialize the messages list completion = client.chat.completions.create( model="qwen-long", messages=[ {'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'system', 'content': 'Alibaba Cloud Model Studio smartphone product introduction: Alibaba Cloud Model Studio X1 —————— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen...'}, {'role': 'user', 'content': 'What does the article talk about?'} ], # All code examples use streaming output to clearly and intuitively show the model's output process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation stream=True, stream_options={"include_usage": True} ) full_content = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content: # Append output content full_content += chunk.choices[0].delta.content print(chunk.model_dump()) print(full_content) ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.core.http.StreamResponse; import com.openai.models.chat.completions.*; public class Main { public static void main(String[] args) { // Create a client using the API key from the environment variable OpenAIClient client = OpenAIOkHttpClient.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Create a chat request ChatCompletionCreateParams chatParams = ChatCompletionCreateParams.builder() .addSystemMessage("You are a helpful assistant.") .addSystemMessage("Alibaba Cloud Model Studio smartphone product introduction: Alibaba Cloud Model Studio X1 —————— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen...") .addUserMessage("What does this article talk about?") .model("qwen-long") .build(); StringBuilder fullResponse = new StringBuilder(); // All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation try (StreamResponse streamResponse = client.chat().completions().createStreaming(chatParams)) { streamResponse.stream().forEach(chunk -> { // Print and append each chunk's content System.out.println(chunk); String content = chunk.choices().get(0).delta().content().orElse(""); if (!content.isEmpty()) { fullResponse.append(content); } }); System.out.println(fullResponse); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("For more information, see https://www.alibabacloud.com/help/model-studio/error-code"); } } } ``` ```bash curl # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-long", "messages": [ {"role": "system","content": "You are a helpful assistant."}, {"role": "system","content": "Alibaba Cloud Model Studio X1 —— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen with a 120Hz refresh rate, ..."}, {"role": "user","content": "What does this article talk about?"} ], "stream": true, "stream_options": { "include_usage": true } }' ``` To pass multiple documents in a single conversation turn, place the content of each document in a separate System Message. ```python Python expandable import os from openai import OpenAI client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace your API key here if you haven't set the environment variable # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) # Initialize the messages list completion = client.chat.completions.create( model="qwen-long", messages=[ {'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'system', 'content': 'Alibaba Cloud Model Studio X1————Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen with a 120Hz refresh rate...'}, {'role': 'system', 'content': 'Stardust S9 Pro —— A revolutionary visual feast: breakthrough 6.9-inch 1440 x 3088 pixel under-display camera design...'}, {'role': 'user', 'content': 'What are the similarities and differences between the products discussed in these two articles?'} ], # All code examples use streaming output to clearly and intuitively show the model's output process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation stream=True, stream_options={"include_usage": True} ) full_content = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content: # Append output content full_content += chunk.choices[0].delta.content print(chunk.model_dump()) print(full_content) ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.core.http.StreamResponse; import com.openai.models.chat.completions.*; public class Main { public static void main(String[] args) { // Create a client using the API key from the environment variable OpenAIClient client = OpenAIOkHttpClient.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Create a chat request ChatCompletionCreateParams chatParams = ChatCompletionCreateParams.builder() .addSystemMessage("You are a helpful assistant.") .addSystemMessage("Alibaba Cloud Model Studio smartphone product introduction: Alibaba Cloud Model Studio X1 —————— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen...") .addSystemMessage("Stardust S9 Pro —— A revolutionary visual feast: breakthrough 6.9-inch 1440 x 3088 pixel under-display camera design...") .addUserMessage("What are the similarities and differences between the products discussed in these two articles?") .model("qwen-long") .build(); StringBuilder fullResponse = new StringBuilder(); // All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation try (StreamResponse streamResponse = client.chat().completions().createStreaming(chatParams)) { streamResponse.stream().forEach(chunk -> { // Print and append each chunk's content System.out.println(chunk); String content = chunk.choices().get(0).delta().content().orElse(""); if (!content.isEmpty()) { fullResponse.append(content); } }); System.out.println(fullResponse); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("For more information, see https://www.alibabacloud.com/help/model-studio/error-code"); } } } ``` ```bash curl # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-long", "messages": [ {"role": "system","content": "You are a helpful assistant."}, {"role": "system","content": "Alibaba Cloud Model Studio X1 —— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen with a 120Hz refresh rate..."}, {"role": "system","content": "Stardust S9 Pro —— A revolutionary visual feast: breakthrough 6.9-inch 1440 x 3088 pixel under-display camera design..."}, {"role": "user","content": "What are the similarities and differences between the products discussed in these two articles?"} ], "stream": true, "stream_options": { "include_usage": true } }' ``` During your interaction with the model, you might need to add new document information. To do this, append the new document content as a System Message to the Messages array. ```python Python expandable import os from openai import OpenAI, BadRequestError client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace your API key here if you haven't set the environment variable # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) # Initialize the messages list messages = [ {'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'system', 'content': 'Alibaba Cloud Model Studio X1 —— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen with a 120Hz refresh rate...'}, {'role': 'user', 'content': 'What does this article talk about?'} ] try: # First-round response completion_1 = client.chat.completions.create( model="qwen-long", messages=messages, stream=False ) # Print the first-round response # For streaming output in the first round, set stream=True and concatenate each segment's content. Pass the concatenated string as the content when constructing assistant_message print(f"First-round response: {completion_1.choices[0].message.model_dump()}") except BadRequestError as e: print(f"Error: {e}") print("For more information, see https://www.alibabacloud.com/help/model-studio/error-code") # Construct assistant_message assistant_message = { "role": "assistant", "content": completion_1.choices[0].message.content} # Append assistant_message to messages messages.append(assistant_message) # Append new document content to messages system_message = { 'role': 'system', 'content': 'Stardust S9 Pro —— A revolutionary visual feast: breakthrough 6.9-inch 1440 x 3088 pixel under-display camera design, delivering an immersive visual experience...'} messages.append(system_message) # Add user question messages.append({ 'role': 'user', 'content': 'What are the similarities and differences between the products discussed in these two articles?' }) # Response after appending the document completion_2 = client.chat.completions.create( model="qwen-long", messages=messages, # All code examples use streaming output to clearly and intuitively show the model's output process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation stream=True, stream_options={"include_usage": True} ) # Stream and print the response after appending the document print("Response after appending the document:") for chunk in completion_2: print(chunk.model_dump()) ``` ```java Java expandable import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.models.chat.completions.*; import com.openai.core.http.StreamResponse; import java.util.ArrayList; import java.util.List; public class Main { public static void main(String[] args) { OpenAIClient client = OpenAIOkHttpClient.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1") .build(); // Initialize the messages list List messages = new ArrayList<>(); // Add role-setting information ChatCompletionSystemMessageParam roleSet = ChatCompletionSystemMessageParam.builder() .content("You are a helpful assistant.") .build(); messages.add(ChatCompletionMessageParam.ofSystem(roleSet)); // First-round content ChatCompletionSystemMessageParam systemMsg1 = ChatCompletionSystemMessageParam.builder() .content("Alibaba Cloud Model Studio X1 —— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen with a 120Hz refresh rate, 256GB storage, 12GB RAM, and a 5000mAh long-lasting battery...") .build(); messages.add(ChatCompletionMessageParam.ofSystem(systemMsg1)); // User question (USER role) ChatCompletionUserMessageParam userMsg1 = ChatCompletionUserMessageParam.builder() .content("Please summarize the article content") .build(); messages.add(ChatCompletionMessageParam.ofUser(userMsg1)); // Build the first-round request and handle exceptions ChatCompletion completion1; try { completion1 = client.chat().completions().create( ChatCompletionCreateParams.builder() .model("qwen-long") .messages(messages) .build() ); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("For more information, see https://www.alibabacloud.com/help/model-studio/error-code"); e.printStackTrace(); return; } // First-round response String firstResponse = completion1 != null ? completion1.choices().get(0).message().content().orElse("") : ""; System.out.println("First-round response: " + firstResponse); // Construct AssistantMessage ChatCompletionAssistantMessageParam assistantMsg = ChatCompletionAssistantMessageParam.builder() .content(firstResponse) .build(); messages.add(ChatCompletionMessageParam.ofAssistant(assistantMsg)); // Second-round content ChatCompletionSystemMessageParam systemMsg2 = ChatCompletionSystemMessageParam.builder() .content("Stardust S9 Pro —— A revolutionary visual feast: breakthrough 6.9-inch 1440 x 3088 pixel under-display camera design, delivering an immersive visual experience...") .build(); messages.add(ChatCompletionMessageParam.ofSystem(systemMsg2)); // Second-round user question (USER role) ChatCompletionUserMessageParam userMsg2 = ChatCompletionUserMessageParam.builder() .content("Please compare the structural differences between the two descriptions") .build(); messages.add(ChatCompletionMessageParam.ofUser(userMsg2)); // All examples use streaming output to show the model's response process. For non-streaming examples, see https://www.alibabacloud.com/help/model-studio/text-generation StringBuilder fullResponse = new StringBuilder(); try (StreamResponse streamResponse = client.chat().completions().createStreaming( ChatCompletionCreateParams.builder() .model("qwen-long") .messages(messages) .build())) { streamResponse.stream().forEach(chunk -> { String content = chunk.choices().get(0).delta().content().orElse(""); if (!content.isEmpty()) { fullResponse.append(content); } }); System.out.println("\nFinal response:"); System.out.println(fullResponse.toString().trim()); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); System.err.println("For more information, see https://www.alibabacloud.com/help/model-studio/error-code"); } } } ``` ```bash curl # Endpoint for the China (Beijing) region. Replace {WorkspaceId} with your Workspace ID. URLs vary by region. curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-long", "messages": [ {"role": "system","content": "You are a helpful assistant."}, {"role": "system","content": "Alibaba Cloud Model Studio X1 —— Enjoy an ultimate visual experience: features a 6.7-inch 1440 x 3200 pixel ultra-clear screen with a 120Hz refresh rate..."}, {"role": "user","content": "What does this article talk about?"}, {"role": "system","content": "Stardust S9 Pro —— A revolutionary visual feast: breakthrough 6.9-inch 1440 x 3088 pixel under-display camera design, delivering an immersive visual experience..."}, {"role": "user","content": "What are the similarities and differences between the products discussed in these two articles"} ], "stream": true, "stream_options": { "include_usage": true } }' ``` ## Model pricing
Model name Version Context length Max input Max output Input cost Output cost
(Tokens) (per 1 million tokens)
qwen-long-latest > Always has the same capabilities as the latest snapshot version. Latest 10,000,000 10,000,000 32,768 \$0.072 \$0.287
qwen-long-2025-01-25 > Also known as qwen-long-0125. Snapshot
## FAQ 1. Does the Qwen-Long model support submitting batch jobs? Yes. Qwen-Long supports the [OpenAI Batch API](/en/model-studio/batch-interfaces-compatible-with-openai) at 50% of real-time call rates. Submit batch jobs as files; jobs run asynchronously and return results on completion or timeout. 2. Where are files saved after they are uploaded using the OpenAI-compatible file API? Files are uploaded to your Model Studio bucket at no cost. See the [OpenAI File API](/en/model-studio/openai-file-interface) for querying and managing files. 3. What is `qwen-long-2025-01-25`? This is a version snapshot frozen at a specific point in time. More stable than `latest`, with no expiration date. 4. How can I ensure the model outputs a JSON string in a standard format? `qwen-long` and all snapshots support [structured output](/en/model-studio/qwen-structured-output). Specify a JSON Schema to ensure valid JSON that matches your structure. ## API reference Refer to [Qwen API details](/en/model-studio/qwen-api-reference) for the input and output parameters of the Qwen-Long model. ## Error codes If the model call fails and returns an error message, see [Error codes](/en/model-studio/error-code) for resolution. ## Limits - SDK dependencies: - File operations (upload, delete, query) require an OpenAI-compatible SDK. - Invoke models using an OpenAI-compatible SDK or Dashscope SDK. - File upload: - Supported formats: TXT, DOCX, PDF, XLSX, EPUB, MOBI, MD, CSV, JSON, BMP, PNG, JPG/JPEG, and GIF. - File size: The maximum size for image files is 20 MB. The maximum size for other file formats is 150 MB. - Account quota: Maximum 10,000 files or 100 GB per account. Uploads fail when either limit is reached. Delete files to free quota. See [OpenAI compatible - File](/en/model-studio/openai-file-interface). - Storage period: Currently, there is no expiration limit for stored files. - API inputs: - The first `system` message defines the role. The second contains document content or `fileid://xxx`. The `user` message contains the query. - When referencing files using a `file-id`, a single request can reference a maximum of 100 files. - With a second `system` message, `user` message limit is 9,000 tokens. No limit with only one system message. - The total context length is limited to 10 million tokens. - API outputs: - The maximum output length is 32,768 tokens. - File sharing: - `file-id`s are account-specific and cannot be used cross-account or with RAM user API keys. - Throttling: For information about model throttling conditions, see [Throttling](/en/model-studio/rate-limit).