Parameter | Type | Required | Description |
model | string | Yes | Name of the model to use, for example, deepseek-v4-flash. |
messages | array | Yes | A list of conversation messages, arranged in chronological order and containing the full context. The platform does not manage sessions. For multi-turn conversations, the client must carry the complete history. For details, see Message Object Details. |
system | string or array | No | System prompt. It is not within messages and is passed separately via the top-level system field. This is a key difference from the OpenAI Chat protocol. |
max_tokens | integer | Yes | The maximum number of tokens that can be generated in a single model output. Different models have their own limits. Tokens consumed by the reasoning chain are counted toward this limit. Therefore, when thinking is enabled, this value must be greater than budget_tokens. When the limit is reached, stop_reason is "max_tokens". |
stream | boolean | No | Whether to enable streaming responses (default: false). When true, responses are returned event by event in SSE format. |
system supports two forms:// String"system": "You are a friendly assistant."// Content block array (which can carry cache_control)"system": [{ "type": "text", "text": "You are a friendly assistant." },{ "type": "text", "text": "<Long Document>", "cache_control": { "type": "ephemeral" } }]
max_tokens: This field is required in the Anthropic protocol. Some models may apply lenient fallback handling for missing or invalid max_tokens. However, always explicitly pass this field according to the protocol standard and do not rely on fallback behavior.Parameter | Type | Scope | Description |
temperature | float | [0, 1] | Sampling temperature. Note that the Anthropic range is [0, 1], which differs from OpenAI's [0, 2]. |
top_p | float | (0, 1] | Nucleus sampling. Generally, adjust only one of temperature or top_p. |
top_k | integer | - | Samples only from the top K tokens with the highest probability. This is a parameter specific to Anthropic and is not present in the OpenAI Chat protocol. |
stop_sequences | string[] | - | Custom stop sequences. Stops immediately when any sequence is matched, with stop_reason set to "stop_sequence". The matched sequence is written back to the stop_sequence field in the response. |
temperature to be fixed at 1. Other models may tolerate temperature != 1. For thinking scenarios, use the default value or 1. Whether strict validation is enforced depends on the model.
Regarding stop_sequences: Some models can reliably write back stop_reason=stop_sequence and the matched stop_sequence, while others do not guarantee write-back. Services that rely on precise stop semantics should validate against the target model.input_schema (OpenAI uses function.parameters).{"name": "get_weather","description": "Query the weather information for a specified city","input_schema": {"type": "object","properties": {"location": { "type": "string", "description": "City name" }},"required": ["location"]}}
Field | Type | Required | Description |
name | string | Yes | Tool name. |
description | string | No | Describes the tool's purpose, helping the model determine when to call it. |
input_schema | object | Yes | Parameter definitions, which follow the JSON Schema format. |
type | string | No | Leave it blank for regular tools. For built-in tools, enter the type name (for example, web_search_20250305). |
max_uses | integer | No | Maximum number of calls per session for built-in tools. |
cache_control | object | No | Cache flag at the tool definition level. |
tool_choice(Anthropic uses an object format):Anthropic tool_choice | Equivalent OpenAI | Description |
{"type":"auto"} | "auto" | Default, the model decides on its own. |
{"type":"any"} | "required" | Forces the model to call any available tool. |
{"type":"none"} | "none" | Disables tool calls. |
{"type":"tool","name":"x"} | {"type":"function","function":{"name":"x"}} | Forces the model to call a specified tool. |
disable_parallel_tool_use (boolean): When set to true, it disables the parallel invocation of multiple tools within a single response.tool_choice:any and tool_choice:tool). Typically, only auto / none can be used together with thinking. If you need to force a tool call, explicitly pass thinking: {"type":"disabled"} first, and then use any or specify a tool. For specific differences among models, see Model Field Compatibility.{"thinking": {"type": "enabled","budget_tokens": 8000}}
Field | Type | Required | Description |
type | string | Yes | "enabled" for enabling / "disabled" for disabling / "adaptive" for adaptive mode. |
budget_tokens | integer | Conditional | Recommended when enabled. This is the budget for chain-of-thought tokens (recommended range: 1024 to 32000). The value must be less than max_tokens. |
display | string | No | The method for displaying the reasoning process (supported by some models). |
enabled, include budget_tokens (and optionally display). For disabled, only type is required. For adaptive, include type (and optionally display), and the budget is adaptively allocated by the model.thinking is not passed. Other models may tolerate non-strict constraints such as missing budget_tokens, budget_tokens >= max_tokens, or temperature != 1. These are model/platform compatibility behaviors and should not be treated as a universal contract for all models. If you do not want thinking content returned, explicitly pass thinking: {"type":"disabled"}.
Relationship with OpenAI-Compatible Parameters: Some vendor documentation uses fields like enable_thinking and reasoning_effort to describe thinking capabilities. In the Anthropic Messages protocol, the corresponding expressions are thinking.type, output_config.effort, and the thinking / redacted_thinking content blocks that are returned unchanged from the assistant in multi-turn history. Do not directly mix enable_thinking in the Messages request body.Hunyuan-Beta. The platform forwards this header to the model service. This request header is an extended capability. Models that do not support it will ignore the header without reporting an error. To confirm whether it actually takes effect, refer to the capability documentation of the model you are using.output_config (object): Controls the output effort level and structured format.{"output_config": {"effort": "high","format": {"type": "json_schema","schema": { "type": "object", "properties": { "answer": { "type": "string" } } }}}}
Field | Type | Description |
effort | string | Output effort level: low / medium / high / xhigh / max. |
format.type | string | Structured output type (such as json_schema). |
format.schema | object | JSON Schema definition. |
output_config.effort is accepted by most models. For structured output with output_config.format.type=json_schema, only some models can reliably generate JSON according to the schema. Some models return a 200 status code but the JSON does not fully comply with the schema, while others return a 400. Do not treat json_schema as a universal capability for all models. Before using it, verify it against your target model.cache_control to tag cacheable content. After a cache hit, this significantly reduces the billing for input_tokens from repeated prompts. Three levels of tagging are supported:Layer | Tag Location | Description |
Tool level | tools[n].cache_control | Cache tool definition |
System level | system[n].cache_control | Cache system content blocks |
Message/content block level | messages[n].content[m].cache_control | Cache historical messages or content blocks |
cache_control object structure:{ "type": "ephemeral" } // Default 5-minute TTL{ "type": "ephemeral", "ttl": "1h" } // 1-hour TTL
cache_creation_input_tokens and cache_read_input_tokens fields in the response usage. If the model does not support caching, the related tokens are counted as 0.cache_control, document block-level cache, ttl:"1h" specifics, and cache_creation.ephemeral_1h_input_tokens are generally incomplete. Interpret this as "partial support for Prompt Caching". Do not rely on complete Anthropic TTL / usage details.metadata (object): Request metadata. metadata.user_id (string) is the unique identifier for the end user, used for abuse detection and usage attribution.service_tier (string): Service tier / TPM guarantee channel identifier. The actually matched tier is written back in the response.Value | Description |
auto | Automatic selection |
standard | Standard tier |
priority | Priority tier (higher guarantee) |
batch | Batch tier |
Request header | Required | Description |
x-api-key | Yes | API Key (Anthropic's official convention); the platform also supports Authorization: Bearer <key>. |
anthropic-version | Yes | API version, fixed to 2023-06-01. |
content-type | Yes | application/json |
anthropic-beta | No | beta feature switch (such as prompt-caching and interleaved-thinking), which the platform transparently passes through to the model service as is. |
messages array represents a message in the conversation.role | Description | Usage Location |
user | Input from human users (including tool_result) | Odd-numbered conversation turns. |
assistant | Historical responses from the model (may include text / thinking / tool_use) | Used in even-numbered conversation turns. For multi-turn conversations, historical context must be carried over. |
tool role. Tool execution results are expressed as a tool_result content block within a user message, and they immediately follow the corresponding tool_use from the assistant. This differs from the OpenAI Chat protocol, which has a separate tool role. The system is not within the messages array; it is passed via the top-level system field.// String{"role": "user", "content": "Hello"}// Content block array{"role": "user","content": [{ "type": "text", "text": "What is in this picture?" },{ "type": "image", "source": { "type": "url", "url": "https://example.com/img.jpg" } }]}
block type | Key Fields | Applicable role | Description |
text | text,cache_control | user / assistant | Text Content |
image | source | user | Figure |
video | source | user | Video |
document | source,title,context,citations,cache_control | user | Document Input and Citation |
search_result | source,title,content,citations | user | Search result content block |
tool_use | id,name,input | assistant | Model requests to invoke a tool |
tool_result | tool_use_id,content | user | Tool execution result |
thinking | thinking,signature | assistant | Reasoning chain content |
redacted_thinking | data | assistant | Redacted reasoning chain |
text, tool_use, the text tool_result, and thinking are generally available on models that support the corresponding capabilities. The image block requires a model that explicitly supports vision/multimodal capabilities; pure text models return a 400 error. Most models can receive document but do not return citations; search_result is generally not supported by current models.type: "image"):// Base64{ "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQ..." } }// URL{ "type": "image", "source": { "type": "url", "url": "https://example.com/photo.jpg" } }
source Field | Type | Description |
type | string | "base64" or "url". |
media_type | string | Required when base64 is selected, for example, image/jpeg, image/png, image/gif, or image/webp. |
data | string | Image data when base64 is selected. |
url | string | Image address when url is selected. |
detail | string | low / high / auto for image analysis detail. |
type: "document"): source (supports text, base64, URL, and so on), title, context, citations, cache_control. Most models can receive document requests (returning 200), but their responses do not include citations. This makes them suitable for fallback processing as ordinary context, and you should not promise Anthropic's citation capability.type: "search_result"): It is used to provide external search results to the model as a content block with sources, typically used in conjunction with citations. Current models generally return a 400 error and do not support this content block.type: "video"): Its structure is consistent with that of the image block, and the source additionally supports the fps (frames per second) field. Video understanding is an extended capability provided by various vendors and is not natively supported by Anthropic.{ "type": "tool_use", "id": "toolu_001", "name": "get_weather", "input": { "location": "Beijing" } }
Field | Type | Description |
id | string | The unique ID for the tool call, which must be returned in the corresponding tool_result. |
name | string | Name of the tool to be called. |
input | object | Tool parameters (an object, not a JSON string, different from the arguments string in OpenAI). |
tool_result block:{ "type": "tool_result", "tool_use_id": "toolu_001", "content": "It is sunny in Beijing today, with a temperature of 25 degrees Celsius." }
Field | Type | Description |
tool_use_id | string | Corresponds to tool_use.id. |
content | string or array | The tool result, which can be plain text or an array of content blocks (supporting text+images). |
is_error | boolean | Set to true when tool execution fails. |
cache_control | object | Cache flag. |
tool_result blocks generated within the same assistant turn must be placed in a single, immediately following user message. Image-type tool_result.content is supported only by some multimodal models.{ "type": "thinking", "thinking": "Let me analyze step by step: outer loop n times, inner loop log n times...", "signature": "EqoBCkgIARgC..." }
Field | Type | Description |
thinking | string | Text of the model reasoning process |
signature | string | Integrity signature for reasoning blocks |
signature intact. Otherwise, the service returns 400 Invalid signature. The signature is an encrypted credential and must not be truncated or modified. This validation semantics primarily applies to the Claude series of models. For other models that carry the chain of thought using their own protocols, the return requirements should follow the corresponding model's documentation. Some models (such as deepseek-v4-flash and hy3) do not return a signature. In such cases, the field does not need to be returned in multi-turn conversations.{ "type": "redacted_thinking", "data": "<encrypted data>" }, containing only the data field. In multi-turn conversations, all such blocks must be returned in their original order to maintain context continuity for subsequent writing. This content block primarily appears in the Claude series of models and is typically not returned by other models.{"id": "msg_xxx","type": "message","role": "assistant","model": "<your-model-name>","content": [{ "type": "thinking", "thinking": "...", "signature": "..." },{ "type": "text", "text": "Reply content" },{ "type": "tool_use", "id": "toolu_xxx", "name": "get_weather", "input": { "location": "Beijing" } }],"stop_reason": "end_turn","stop_sequence": null,"usage": {"input_tokens": 12,"output_tokens": 6,"cache_creation_input_tokens": 0,"cache_read_input_tokens": 0},"service_tier": "standard"}
Field | Type | Description |
id | string | The unique identifier of the response (prefixed with msg_). |
type | string | Fixed as "message". |
role | string | Fixed as "assistant" |
model | string | The name of the model actually used. |
content | array | An array of content blocks, typically in the order of thinking → text → tool_use. |
stop_reason | string | The reason for stopping. See the enumeration below. |
stop_sequence | string or null | The matched stop sequence (when a stop_sequences is matched). |
usage | object | |
container | object | Information about the code execution container ( id, expires_at). Returned only when the built-in code_execution tool is used. |
service_tier | string | The service tier that was actually matched. |
id from the response body. For streaming responses, prioritize using the data.message.id from the message_start event. The platform simultaneously forwards/generates the X-Request-Id / X-Tc-Requestid from the client request headers. However, the final valid model response ID is determined by the message id.stream: true, SSE is returned with the response headers Content-Type: text/event-stream, Cache-Control: no-cache, and Connection: keep-alive.message_start→ content_block_start → content_block_delta (multiple times) → content_block_stop (Block 1)→ content_block_start → content_block_delta (multiple times) → content_block_stop (Block 2)...→ message_delta → message_stop
ping (keep-alive heartbeat) and error (error). Each SSE frame has the format event: <type> + data: {json}.Event | Description | Key Fields |
message_start | Indicates the start of a message and returns the initial message skeleton. | message (containing initial usage and input/cache) |
content_block_start | The start of a content block | index and content_block (type is text/thinking/tool_use) |
content_block_delta | Content increment | index and delta (see the table below) |
content_block_stop | The end of a content block | index |
message_delta | Message-level delta | delta.stop_reason and usage (final output_tokens) |
message_stop | Message end | - |
content_block_delta.delta type:delta.type | Field | Meaning |
text_delta | text | Text delta. |
thinking_delta | thinking | Reasoning process delta. |
signature_delta | signature | Integrity signature for reasoning blocks (delivered before the thinking block ends). |
input_json_delta | partial_json | Tool parameter JSON fragments, which need to be accumulated and then parsed as a whole. |
error event is delivered or the connection is closed directly. The client must detect this through connection exceptions.
Capability Description: Basic SSE events are available on models that support the corresponding capabilities. citations_delta is generally not returned at present. The streaming tool parameter (input_json_delta) is available on some models only after thinking is explicitly disabled.Value | Description | Handling Recommendation |
end_turn | Model ends normally. | Process as normal. |
max_tokens | The max_tokens limit is reached, and the output is truncated. | Increase the max_tokens value or generate content in segments. |
stop_sequence | A stop_sequences is matched | Check the matched stop_sequence field. |
tool_use | Model requests to invoke a tool. | Execute the tool and return the result via tool_result to continue the conversation. |
pause_turn | server tool long-turn pause | Echo the current content as is and continue the request to resume. |
refusal | Model refuses to answer due to security reasons. | Check whether the input triggers the security policy. |
Field | Type | Description |
input_tokens | integer | Net input tokens (excluding cache read/write operations) |
output_tokens | integer | Output tokens (including thinking content) |
cache_creation_input_tokens | integer | Number of tokens written to cache |
cache_read_input_tokens | integer | Number of tokens read from cache hits (billing is significantly reduced) |
cache_creation.ephemeral_5m_input_tokens | integer | 5-minute TTL cache creation tokens (breakdown by TTL) |
cache_creation.ephemeral_1h_input_tokens | integer | 1-hour TTL cache creation tokens (breakdown by TTL) |
server_tool_use.web_search_requests | integer | Number of built-in web_search calls |
server_tool_use.web_fetch_requests | integer | Number of built-in web_fetch calls |
service_tier | string | The service tier that was actually matched |
input_tokens, cache_read_input_tokens, and cache_creation_input_tokens are mutually exclusive. The actual total input = the sum of these three. When calculating costs, add all three items. Do not rely solely on input_tokens.
In streaming responses, the message_start event provides the initial input_tokens and cache tokens. The message_delta event provides the final output_tokens.POST /v1/messages. Include the anthropic-version and x-api-key headers. The ${BASE_URL} in the following examples is set to https://tokenhub-intl.tencentcloudmaas.com.curl -s -X POST "${BASE_URL}/v1/messages" \\-H 'Content-Type: application/json' \\-H 'anthropic-version: 2023-06-01' \\-H "x-api-key: ${API_KEY}" \\-d '{"model": "<your-model-name>","messages": [{"role": "user", "content": "Hello"}],"max_tokens": 1024}'
import requestsresp = requests.post("https://tokenhub-intl.tencentcloudmaas.com/v1/messages",headers={"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},json={"model": "<your-model-name>","messages": [{"role": "user", "content": "Hello"}],"max_tokens": 1024,},)print(resp.json())
const resp = await fetch("https://tokenhub-intl.tencentcloudmaas.com/v1/messages", {method: "POST",headers: {"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},body: JSON.stringify({model: "<your-model-name>","messages": [{"role": "user", "content": "Hello"}],max_tokens: 1024,}),});console.log(await resp.json());
import okhttp3.*;public class BasicMessage {public static void main(String[] args) throws Exception {String body = """{"model": "<your-model-name>","messages": [{"role": "user", "content": "Hello"}],"max_tokens": 1024}""";Request request = new Request.Builder().url("https://tokenhub-intl.tencentcloudmaas.com/v1/messages").header("Content-Type", "application/json").header("anthropic-version", "2023-06-01").header("x-api-key", "YOUR_API_KEY").post(RequestBody.create(body, MediaType.parse("application/json"))).build();try (Response response = new OkHttpClient().newCall(request).execute()) {System.out.println(response.body().string());}}}
package mainimport ("fmt""io""net/http""strings")func main() {body := `{"model": "<your-model-name>","messages": [{"role": "user", "content": "Hello"}],"max_tokens": 1024}`req, _ := http.NewRequest("POST","https://tokenhub-intl.tencentcloudmaas.com/v1/messages",strings.NewReader(body))req.Header.Set("Content-Type", "application/json")req.Header.Set("anthropic-version", "2023-06-01")req.Header.Set("x-api-key", "YOUR_API_KEY")resp, err := http.DefaultClient.Do(req)if err != nil {panic(err)}defer resp.Body.Close()data, _ := io.ReadAll(resp.Body)fmt.Println(string(data))}
curl -s -N -X POST "${BASE_URL}/v1/messages" \\-H 'Content-Type: application/json' \\-H 'anthropic-version: 2023-06-01' \\-H "x-api-key: ${API_KEY}" \\-d '{"model": "<your-model-name>","system": [{"type": "text", "text": "You are a friendly assistant."}],"messages": [{"role": "user", "content": "What is 1+1?"}],"max_tokens": 1024,"stream": true}'
import requestsresp = requests.post("https://tokenhub-intl.tencentcloudmaas.com/v1/messages",headers={"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},json={"model": "<your-model-name>","system": [{"type": "text", "text": "You are a friendly assistant."}],"messages": [{"role": "user", "content": "What is 1+1?"}],"max_tokens": 1024,"stream": True,},stream=True,)for line in resp.iter_lines(decode_unicode=True):if line:print(line)
const resp = await fetch("https://tokenhub-intl.tencentcloudmaas.com/v1/messages", {method: "POST",headers: {"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},body: JSON.stringify({model: "<your-model-name>","system": [{"type": "text", "text": "You are a friendly assistant."}],"messages": [{"role": "user", "content": "What is 1+1?"}],max_tokens: 1024,stream: true,}),});const reader = resp.body.getReader();const decoder = new TextDecoder();while (true) {const { done, value } = await reader.read();if (done) break;process.stdout.write(decoder.decode(value));}
import okhttp3.*;import java.io.BufferedReader;import java.io.InputStreamReader;public class SystemStream {public static void main(String[] args) throws Exception {String body = """{"model": "<your-model-name>","system": [{"type": "text", "text": "You are a friendly assistant."}],"messages": [{"role": "user", "content": "What is 1+1?"}],"max_tokens": 1024,"stream": true}""";Request request = new Request.Builder().url("https://tokenhub-intl.tencentcloudmaas.com/v1/messages").header("Content-Type", "application/json").header("anthropic-version", "2023-06-01").header("x-api-key", "YOUR_API_KEY").post(RequestBody.create(body, MediaType.parse("application/json"))).build();try (Response response = new OkHttpClient().newCall(request).execute();BufferedReader reader = new BufferedReader(new InputStreamReader(response.body().byteStream()))) {String line;while ((line = reader.readLine()) != null) {if (!line.isEmpty()) System.out.println(line);}}}}
package mainimport ("bufio""fmt""net/http""strings")func main() {body := `{"model": "<your-model-name>","system": [{"type": "text", "text": "You are a friendly assistant."}],"messages": [{"role": "user", "content": "What is 1+1?"}],"max_tokens": 1024,"stream": true}`req, _ := http.NewRequest("POST","https://tokenhub-intl.tencentcloudmaas.com/v1/messages",strings.NewReader(body))req.Header.Set("Content-Type", "application/json")req.Header.Set("anthropic-version", "2023-06-01")req.Header.Set("x-api-key", "YOUR_API_KEY")resp, err := http.DefaultClient.Do(req)if err != nil {panic(err)}defer resp.Body.Close()scanner := bufio.NewScanner(resp.Body)for scanner.Scan() {if line := scanner.Text(); line != "" {fmt.Println(line)}}}
message_start → content_block_start → content_block_delta → content_block_stop → message_delta → message_stop.{"model": "<your-model-name>","messages": [{"role": "user", "content": "How do I read a CSV file?"},{"role": "assistant", "content": "Using pandas: pd.read_csv('file.csv')"},{"role": "user", "content": "What should I do if the file is too large to fit in memory?"}],"max_tokens": 1024}
{"model": "<your-model-name>","messages": [{"role": "user","content": [{"type": "text", "text": "Please describe the scene in the picture in detail"},{ "type": "image", "source": { "type": "url", "url": "https://example.com/photo.jpg" } }]}],"max_tokens": 1024}
{"role": "user","content": [{"type": "text", "text": "Recognize the text in the picture"},{ "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQSkZJRgAB..." } }]}
curl -s -X POST "${BASE_URL}/v1/messages" \\-H 'Content-Type: application/json' \\-H 'anthropic-version: 2023-06-01' \\-H "x-api-key: ${API_KEY}" \\-d '{"model": "<your-model-name>","messages": [{"role": "user", "content": "What is the weather like in Beijing today?"}],"max_tokens": 1024,"tools": [{"name": "get_weather","description": "Query the weather information for a specified city","input_schema": {"type": "object","properties": { "location": { "type": "string", "description": "City name" } },"required": ["location"]}}],"tool_choice": { "type": "auto" }}'
import requestsresp = requests.post("https://tokenhub-intl.tencentcloudmaas.com/v1/messages",headers={"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},json={"model": "<your-model-name>","messages": [{"role": "user", "content": "What is the weather like in Beijing today?"}],"max_tokens": 1024,"tools": [{"name": "get_weather","description": "Query the weather information for a specified city","input_schema": {"type": "object","properties": {"location": {"type": "string", "description": "City name"}},"required": ["location"],},}],"tool_choice": {"type": "auto"},},)print(resp.json())
const resp = await fetch("https://tokenhub-intl.tencentcloudmaas.com/v1/messages", {method: "POST",headers: {"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},body: JSON.stringify({model: "<your-model-name>",messages: [{role: "user", content: "What is the weather like in Beijing today?"}],max_tokens: 1024,tools: [{name: "get_weather",description: "Query the weather information for a specified city",input_schema: {type: "object",properties: { location: { type: "string", description: "City name" } },required: ["location"],},},],tool_choice: { type: "auto" },}),});console.log(await resp.json());
import okhttp3.*;public class ToolUse {public static void main(String[] args) throws Exception {String body = """{"model": "<your-model-name>","messages": [{"role": "user", "content": "What is the weather like in Beijing today?"}],"max_tokens": 1024,"tools": [{"name": "get_weather","description": "Query the weather information for a specified city","input_schema": {"type": "object","properties": { "location": { "type": "string", "description": "City name" } },"required": ["location"]}}],"tool_choice": { "type": "auto" }}""";Request request = new Request.Builder().url("https://tokenhub-intl.tencentcloudmaas.com/v1/messages").header("Content-Type", "application/json").header("anthropic-version", "2023-06-01").header("x-api-key", "YOUR_API_KEY").post(RequestBody.create(body, MediaType.parse("application/json"))).build();try (Response response = new OkHttpClient().newCall(request).execute()) {System.out.println(response.body().string());}}}
package mainimport ("fmt""io""net/http""strings")func main() {body := `{"model": "<your-model-name>","messages": [{"role": "user", "content": "What is the weather like in Beijing today?"}],"max_tokens": 1024,"tools": [{"name": "get_weather","description": "Query the weather information for a specified city","input_schema": {"type": "object","properties": { "location": { "type": "string", "description": "City name" } },"required": ["location"]}}],"tool_choice": { "type": "auto" }}`req, _ := http.NewRequest("POST","https://tokenhub-intl.tencentcloudmaas.com/v1/messages",strings.NewReader(body))req.Header.Set("Content-Type", "application/json")req.Header.Set("anthropic-version", "2023-06-01")req.Header.Set("x-api-key", "YOUR_API_KEY")resp, err := http.DefaultClient.Do(req)if err != nil {panic(err)}defer resp.Body.Close()data, _ := io.ReadAll(resp.Body)fmt.Println(string(data))}
tool_use, the client executes the tool and returns the result as a tool_result (placed into the user message):{"model": "<your-model-name>","messages": [{ "role": "user", "content": "What is the weather like in Beijing today?" },{ "role": "assistant", "content": [{ "type": "tool_use", "id": "toolu_001", "name": "get_weather", "input": { "location": "Beijing" } }]},{ "role": "user", "content": [{ "type": "tool_result", "tool_use_id": "toolu_001", "content": "It is sunny in Beijing today, with a temperature of 25 degrees Celsius." }]}],"max_tokens": 1024,"tools": [{"name": "get_weather", "description": "Query the weather","input_schema": { "type": "object", "properties": { "location": { "type": "string" } }, "required": ["location"] }}]}
curl -s -X POST "${BASE_URL}/v1/messages" \\-H 'Content-Type: application/json' \\-H 'anthropic-version: 2023-06-01' \\-H "x-api-key: ${API_KEY}" \\-d '{"model": "<your-model-name>","messages": [{"role": "user", "content": "Please explain what recursion is"}],"max_tokens": 4096,"thinking": { "type": "enabled", "budget_tokens": 2048 }}'
import requestsresp = requests.post("https://tokenhub-intl.tencentcloudmaas.com/v1/messages",headers={"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},json={"model": "<your-model-name>","messages": [{"role": "user", "content": "Please explain what recursion is"}],"max_tokens": 4096,"thinking": {"type": "enabled", "budget_tokens": 2048},},)print(resp.json())
const resp = await fetch("https://tokenhub-intl.tencentcloudmaas.com/v1/messages", {method: "POST",headers: {"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},body: JSON.stringify({model: "<your-model-name>",messages: [{ role: "user", content: "Please explain what recursion is" }],max_tokens: 4096,thinking: { type: "enabled", budget_tokens: 2048 },}),});console.log(await resp.json());
import okhttp3.*;public class Thinking {public static void main(String[] args) throws Exception {String body = """{"model": "<your-model-name>","messages": [{"role": "user", "content": "Please explain what recursion is"}],"max_tokens": 4096,"thinking": { "type": "enabled", "budget_tokens": 2048 }}""";Request request = new Request.Builder().url("https://tokenhub-intl.tencentcloudmaas.com/v1/messages").header("Content-Type", "application/json").header("anthropic-version", "2023-06-01").header("x-api-key", "YOUR_API_KEY").post(RequestBody.create(body, MediaType.parse("application/json"))).build();try (Response response = new OkHttpClient().newCall(request).execute()) {System.out.println(response.body().string());}}}
package mainimport ("fmt""io""net/http""strings")func main() {body := `{"model": "<your-model-name>","messages": [{"role": "user", "content": "Please explain what recursion is"}],"max_tokens": 4096,"thinking": { "type": "enabled", "budget_tokens": 2048 }}`req, _ := http.NewRequest("POST","https://tokenhub-intl.tencentcloudmaas.com/v1/messages",strings.NewReader(body))req.Header.Set("Content-Type", "application/json")req.Header.Set("anthropic-version", "2023-06-01")req.Header.Set("x-api-key", "YOUR_API_KEY")resp, err := http.DefaultClient.Do(req)if err != nil {panic(err)}defer resp.Body.Close()data, _ := io.ReadAll(resp.Body)fmt.Println(string(data))}
content contains a thinking block (with a signature) and a text block. Note that max_tokens must be greater than budget_tokens.thinking block from the previous assistant's turn, along with the signature, must be returned unchanged:{"model": "<your-model-name>","messages": [{"role": "user", "content": "Analyze the complexity of this code."},{ "role": "assistant", "content": [{ "type": "thinking", "thinking": "outer loop n times, inner loop log n times...", "signature": "EqoBCkgIARgC..." },{ "type": "text", "text": "O(n log n)" }]},{"role": "user", "content": "How can it be optimized to O(n)?"}],"max_tokens": 4096,"thinking": { "type": "enabled", "budget_tokens": 5000 }}
signature must be retained and returned unchanged. Otherwise, the service returns 400 Invalid signature.curl -s -X POST "${BASE_URL}/v1/messages" \\-H 'Content-Type: application/json' \\-H 'anthropic-version: 2023-06-01' \\-H "x-api-key: ${API_KEY}" \\-d '{"model": "<your-model-name>","messages": [{"role": "user", "content": "What new large models will be available in 2026?"}],"max_tokens": 2048,"tools": [{ "type": "web_search_20250305", "name": "web_search", "max_uses": 3 }]}'
import requestsresp = requests.post("https://tokenhub-intl.tencentcloudmaas.com/v1/messages",headers={"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},json={"model": "<your-model-name>","messages": [{"role": "user", "content": "What new large models will be available in 2026?"}],"max_tokens": 2048,"tools": [{"type": "web_search_20250305", "name": "web_search", "max_uses": 3}],},)print(resp.json())
const resp = await fetch("https://tokenhub-intl.tencentcloudmaas.com/v1/messages", {method: "POST",headers: {"Content-Type": "application/json","anthropic-version": "2023-06-01","x-api-key": "YOUR_API_KEY",},body: JSON.stringify({model: "<your-model-name>",messages: [{ role: "user", content: "What new large models will be available in 2026?" }],max_tokens: 2048,tools: [{ type: "web_search_20250305", name: "web_search", max_uses: 3 }],}),});console.log(await resp.json());
import okhttp3.*;public class BuiltinTool {public static void main(String[] args) throws Exception {String body = """{"model": "<your-model-name>","messages": [{"role": "user", "content": "What new large models will be available in 2026?"}],"max_tokens": 2048,"tools": [{ "type": "web_search_20250305", "name": "web_search", "max_uses": 3 }]}""";Request request = new Request.Builder().url("https://tokenhub-intl.tencentcloudmaas.com/v1/messages").header("Content-Type", "application/json").header("anthropic-version", "2023-06-01").header("x-api-key", "YOUR_API_KEY").post(RequestBody.create(body, MediaType.parse("application/json"))).build();try (Response response = new OkHttpClient().newCall(request).execute()) {System.out.println(response.body().string());}}}
package mainimport ("fmt""io""net/http""strings")func main() {body := `{"model": "<your-model-name>","messages": [{"role": "user", "content": "What new large models will be available in 2026?"}],"max_tokens": 2048,"tools": [{ "type": "web_search_20250305", "name": "web_search", "max_uses": 3 }]}`req, _ := http.NewRequest("POST","https://tokenhub-intl.tencentcloudmaas.com/v1/messages",strings.NewReader(body))req.Header.Set("Content-Type", "application/json")req.Header.Set("anthropic-version", "2023-06-01")req.Header.Set("x-api-key", "YOUR_API_KEY")resp, err := http.DefaultClient.Do(req)if err != nil {panic(err)}defer resp.Body.Close()data, _ := io.ReadAll(resp.Body)fmt.Println(string(data))}
type and do not require input_schema. Support for built-in tools varies by model. For details, see Supported Protocols by Model.cache_control tag to long system documents or tool definitions. After a cache hit, this significantly reduces the billing for input_tokens:{"model": "<your-model-name>","system": [{ "type": "text", "text": "You are a document Q&A assistant." },{ "type": "text", "text": "<Long document content...>", "cache_control": { "type": "ephemeral", "ttl": "1h" } }],"messages": [{"role": "user", "content": "What does Chapter 3 of the document cover?"}],"max_tokens": 1024}
cache_creation_input_tokens (first-time cache write) and cache_read_input_tokens (subsequent cache hit reads) can be seen in the response usage.Apakah halaman ini membantu?
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