curl https://api.apimart.ai/v1/messages \
-H "x-api-key: $API_KEY" \
-H "anthropic-version: 2025-10-01" \
-H "content-type: application/json" \
-d '{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{"role": "user", "content": "Hello, world"}
]
}'
import anthropic
client = anthropic.Anthropic(
api_key="YOUR_API_KEY",
base_url="https://api.apimart.ai"
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Hello, world"}
]
)
print(message.content)
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env.API_KEY,
baseURL: 'https://api.apimart.ai'
});
const message = await client.messages.create({
model: 'claude-sonnet-4-6',
max_tokens: 1024,
messages: [
{ role: 'user', content: 'Hello, world' }
]
});
console.log(message.content);
package main
import (
"bytes"
"encoding/json"
"fmt"
"io/ioutil"
"net/http"
"os"
)
func main() {
url := "https://api.apimart.ai/v1/messages"
payload := map[string]interface{}{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": []map[string]string{
{
"role": "user",
"content": "Hello, world",
},
},
}
jsonData, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
req.Header.Set("x-api-key", os.Getenv("API_KEY"))
req.Header.Set("anthropic-version", "2025-10-01")
req.Header.Set("Content-Type", "application/json")
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
panic(err)
}
defer resp.Body.Close()
body, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(body))
}
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;
public class Main {
public static void main(String[] args) throws Exception {
String url = "https://api.apimart.ai/v1/messages";
String apiKey = System.getenv("API_KEY");
String payload = """
{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello, world"
}
]
}
""";
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(url))
.header("x-api-key", apiKey)
.header("anthropic-version", "2025-10-01")
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(payload))
.build();
HttpResponse<String> response = client.send(request,
HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
}
}
<?php
$url = "https://api.apimart.ai/v1/messages";
$apiKey = getenv('API_KEY');
$payload = [
"model" => "claude-sonnet-4-6",
"max_tokens" => 1024,
"messages" => [
[
"role" => "user",
"content" => "Hello, world"
]
]
];
$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, [
"x-api-key: " . $apiKey,
"anthropic-version: 2025-10-01",
"Content-Type: application/json"
]);
$response = curl_exec($ch);
curl_close($ch);
echo $response;
?>
require 'net/http'
require 'json'
require 'uri'
url = URI("https://api.apimart.ai/v1/messages")
api_key = ENV['API_KEY']
payload = {
model: "claude-sonnet-4-6",
max_tokens: 1024,
messages: [
{
role: "user",
content: "Hello, world"
}
]
}
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = api_key
request["anthropic-version"] = "2025-10-01"
request["Content-Type"] = "application/json"
request.body = payload.to_json
response = http.request(request)
puts response.body
import Foundation
let url = URL(string: "https://api.apimart.ai/v1/messages")!
let apiKey = ProcessInfo.processInfo.environment["API_KEY"] ?? ""
let payload: [String: Any] = [
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
[
"role": "user",
"content": "Hello, world"
]
]
]
var request = URLRequest(url: url)
request.httpMethod = "POST"
request.setValue(apiKey, forHTTPHeaderField: "x-api-key")
request.setValue("2025-10-01", forHTTPHeaderField: "anthropic-version")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try? JSONSerialization.data(withJSONObject: payload)
let task = URLSession.shared.dataTask(with: request) { data, response, error in
if let error = error {
print("Error: \(error)")
return
}
if let data = data, let responseString = String(data: data, encoding: .utf8) {
print(responseString)
}
}
task.resume()
using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
class Program
{
static async Task Main(string[] args)
{
var url = "https://api.apimart.ai/v1/messages";
var apiKey = Environment.GetEnvironmentVariable("API_KEY");
var payload = @"{
""model"": ""claude-sonnet-4-6"",
""max_tokens"": 1024,
""messages"": [
{
""role"": ""user"",
""content"": ""Hello, world""
}
]
}";
using var client = new HttpClient();
client.DefaultRequestHeaders.Add("x-api-key", apiKey);
client.DefaultRequestHeaders.Add("anthropic-version", "2025-10-01");
var content = new StringContent(payload, Encoding.UTF8, "application/json");
var response = await client.PostAsync(url, content);
var result = await response.Content.ReadAsStringAsync();
Console.WriteLine(result);
}
}
#include <stdio.h>
#include <curl/curl.h>
#include <stdlib.h>
int main(void) {
CURL *curl;
CURLcode res;
const char *api_key = getenv("API_KEY");
curl_global_init(CURL_GLOBAL_DEFAULT);
curl = curl_easy_init();
if(curl) {
const char *url = "https://api.apimart.ai/v1/messages";
const char *payload = "{"
"\"model\":\"claude-sonnet-4-6\","
"\"max_tokens\":1024,"
"\"messages\":[{\"role\":\"user\",\"content\":\"Hello, world\"}]"
"}";
char auth_header[256];
snprintf(auth_header, sizeof(auth_header), "x-api-key: %s", api_key);
struct curl_slist *headers = NULL;
headers = curl_slist_append(headers, auth_header);
headers = curl_slist_append(headers, "anthropic-version: 2025-10-01");
headers = curl_slist_append(headers, "Content-Type: application/json");
curl_easy_setopt(curl, CURLOPT_URL, url);
curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payload);
curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
res = curl_easy_perform(curl);
if(res != CURLE_OK) {
fprintf(stderr, "curl_easy_perform() failed: %s\n",
curl_easy_strerror(res));
}
curl_slist_free_all(headers);
curl_easy_cleanup(curl);
}
curl_global_cleanup();
return 0;
}
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[]) {
@autoreleasepool {
NSURL *url = [NSURL URLWithString:@"https://api.apimart.ai/v1/messages"];
NSString *apiKey = [NSProcessInfo processInfo].environment[@"API_KEY"];
NSDictionary *payload = @{
@"model": @"claude-sonnet-4-6",
@"max_tokens": @1024,
@"messages": @[
@{
@"role": @"user",
@"content": @"Hello, world"
}
]
};
NSError *error;
NSData *jsonData = [NSJSONSerialization dataWithJSONObject:payload
options:0
error:&error];
NSMutableURLRequest *request = [NSMutableURLRequest requestWithURL:url];
[request setHTTPMethod:@"POST"];
[request setValue:apiKey forHTTPHeaderField:@"x-api-key"];
[request setValue:@"2025-10-01" forHTTPHeaderField:@"anthropic-version"];
[request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
[request setHTTPBody:jsonData];
NSURLSessionDataTask *task = [[NSURLSession sharedSession]
dataTaskWithRequest:request
completionHandler:^(NSData *data, NSURLResponse *response, NSError *error) {
if (error) {
NSLog(@"Error: %@", error);
return;
}
NSString *result = [[NSString alloc] initWithData:data
encoding:NSUTF8StringEncoding];
NSLog(@"%@", result);
}];
[task resume];
[[NSRunLoop mainRunLoop] run];
}
return 0;
}
(* Requires cohttp and yojson libraries *)
open Lwt
open Cohttp
open Cohttp_lwt_unix
let url = "https://api.apimart.ai/v1/messages"
let api_key = Sys.getenv "API_KEY"
let payload = {|{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello, world"
}
]
}|}
let () =
let headers = Header.init ()
|> fun h -> Header.add h "x-api-key" api_key
|> fun h -> Header.add h "anthropic-version" "2025-10-01"
|> fun h -> Header.add h "Content-Type" "application/json"
in
let body = Cohttp_lwt.Body.of_string payload in
let response = Client.post ~headers ~body (Uri.of_string url) >>= fun (resp, body) ->
body |> Cohttp_lwt.Body.to_string >|= fun body_str ->
print_endline body_str
in
Lwt_main.run response
import 'dart:convert';
import 'dart:io';
import 'package:http/http.dart' as http;
void main() async {
final url = Uri.parse('https://api.apimart.ai/v1/messages');
final apiKey = Platform.environment['API_KEY'];
final payload = {
'model': 'claude-sonnet-4-6',
'max_tokens': 1024,
'messages': [
{
'role': 'user',
'content': 'Hello, world'
}
]
};
final response = await http.post(
url,
headers: {
'x-api-key': apiKey!,
'anthropic-version': '2025-10-01',
'Content-Type': 'application/json',
},
body: jsonEncode(payload),
);
print(response.body);
}
library(httr)
library(jsonlite)
url <- "https://api.apimart.ai/v1/messages"
api_key <- Sys.getenv("API_KEY")
payload <- list(
model = "claude-sonnet-4-6",
max_tokens = 1024,
messages = list(
list(
role = "user",
content = "Hello, world"
)
)
)
response <- POST(
url,
add_headers(
`x-api-key` = api_key,
`anthropic-version` = "2025-10-01",
`Content-Type` = "application/json"
),
body = toJSON(payload, auto_unbox = TRUE),
encode = "raw"
)
cat(content(response, "text"))
{
"model": "claude-sonnet-4-6",
"id": "msg_011CdfeHuC728oxqaLrRNbcB",
"type": "message",
"role": "assistant",
"content": [
{
"type": "text",
"text": "Hello! I'm Claude. Nice to meet you."
}
],
"stop_reason": "end_turn",
"stop_sequence": null,
"stop_details": null,
"usage": {
"input_tokens": 12,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 0
},
"output_tokens": 18,
"service_tier": "standard",
"inference_geo": "global"
}
}
{
"error": {
"code": "model_not_found",
"message": "model not found (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Invalid API key (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Insufficient balance (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Too many requests (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Internal server error (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
Text Series
Claude Messages API
- Fully compatible with the native Anthropic Claude Messages protocol (
POST /v1/messages) - Supports multi-turn conversations, streaming SSE, tool use, and extended thinking
- Supports multimodal content including text and images
- Responses are upstream passthrough with no
{code, data}wrapper
POST
/
v1
/
messages
curl https://api.apimart.ai/v1/messages \
-H "x-api-key: $API_KEY" \
-H "anthropic-version: 2025-10-01" \
-H "content-type: application/json" \
-d '{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{"role": "user", "content": "Hello, world"}
]
}'
import anthropic
client = anthropic.Anthropic(
api_key="YOUR_API_KEY",
base_url="https://api.apimart.ai"
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Hello, world"}
]
)
print(message.content)
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env.API_KEY,
baseURL: 'https://api.apimart.ai'
});
const message = await client.messages.create({
model: 'claude-sonnet-4-6',
max_tokens: 1024,
messages: [
{ role: 'user', content: 'Hello, world' }
]
});
console.log(message.content);
package main
import (
"bytes"
"encoding/json"
"fmt"
"io/ioutil"
"net/http"
"os"
)
func main() {
url := "https://api.apimart.ai/v1/messages"
payload := map[string]interface{}{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": []map[string]string{
{
"role": "user",
"content": "Hello, world",
},
},
}
jsonData, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
req.Header.Set("x-api-key", os.Getenv("API_KEY"))
req.Header.Set("anthropic-version", "2025-10-01")
req.Header.Set("Content-Type", "application/json")
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
panic(err)
}
defer resp.Body.Close()
body, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(body))
}
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;
public class Main {
public static void main(String[] args) throws Exception {
String url = "https://api.apimart.ai/v1/messages";
String apiKey = System.getenv("API_KEY");
String payload = """
{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello, world"
}
]
}
""";
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(url))
.header("x-api-key", apiKey)
.header("anthropic-version", "2025-10-01")
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(payload))
.build();
HttpResponse<String> response = client.send(request,
HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
}
}
<?php
$url = "https://api.apimart.ai/v1/messages";
$apiKey = getenv('API_KEY');
$payload = [
"model" => "claude-sonnet-4-6",
"max_tokens" => 1024,
"messages" => [
[
"role" => "user",
"content" => "Hello, world"
]
]
];
$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, [
"x-api-key: " . $apiKey,
"anthropic-version: 2025-10-01",
"Content-Type: application/json"
]);
$response = curl_exec($ch);
curl_close($ch);
echo $response;
?>
require 'net/http'
require 'json'
require 'uri'
url = URI("https://api.apimart.ai/v1/messages")
api_key = ENV['API_KEY']
payload = {
model: "claude-sonnet-4-6",
max_tokens: 1024,
messages: [
{
role: "user",
content: "Hello, world"
}
]
}
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = api_key
request["anthropic-version"] = "2025-10-01"
request["Content-Type"] = "application/json"
request.body = payload.to_json
response = http.request(request)
puts response.body
import Foundation
let url = URL(string: "https://api.apimart.ai/v1/messages")!
let apiKey = ProcessInfo.processInfo.environment["API_KEY"] ?? ""
let payload: [String: Any] = [
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
[
"role": "user",
"content": "Hello, world"
]
]
]
var request = URLRequest(url: url)
request.httpMethod = "POST"
request.setValue(apiKey, forHTTPHeaderField: "x-api-key")
request.setValue("2025-10-01", forHTTPHeaderField: "anthropic-version")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try? JSONSerialization.data(withJSONObject: payload)
let task = URLSession.shared.dataTask(with: request) { data, response, error in
if let error = error {
print("Error: \(error)")
return
}
if let data = data, let responseString = String(data: data, encoding: .utf8) {
print(responseString)
}
}
task.resume()
using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
class Program
{
static async Task Main(string[] args)
{
var url = "https://api.apimart.ai/v1/messages";
var apiKey = Environment.GetEnvironmentVariable("API_KEY");
var payload = @"{
""model"": ""claude-sonnet-4-6"",
""max_tokens"": 1024,
""messages"": [
{
""role"": ""user"",
""content"": ""Hello, world""
}
]
}";
using var client = new HttpClient();
client.DefaultRequestHeaders.Add("x-api-key", apiKey);
client.DefaultRequestHeaders.Add("anthropic-version", "2025-10-01");
var content = new StringContent(payload, Encoding.UTF8, "application/json");
var response = await client.PostAsync(url, content);
var result = await response.Content.ReadAsStringAsync();
Console.WriteLine(result);
}
}
#include <stdio.h>
#include <curl/curl.h>
#include <stdlib.h>
int main(void) {
CURL *curl;
CURLcode res;
const char *api_key = getenv("API_KEY");
curl_global_init(CURL_GLOBAL_DEFAULT);
curl = curl_easy_init();
if(curl) {
const char *url = "https://api.apimart.ai/v1/messages";
const char *payload = "{"
"\"model\":\"claude-sonnet-4-6\","
"\"max_tokens\":1024,"
"\"messages\":[{\"role\":\"user\",\"content\":\"Hello, world\"}]"
"}";
char auth_header[256];
snprintf(auth_header, sizeof(auth_header), "x-api-key: %s", api_key);
struct curl_slist *headers = NULL;
headers = curl_slist_append(headers, auth_header);
headers = curl_slist_append(headers, "anthropic-version: 2025-10-01");
headers = curl_slist_append(headers, "Content-Type: application/json");
curl_easy_setopt(curl, CURLOPT_URL, url);
curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payload);
curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
res = curl_easy_perform(curl);
if(res != CURLE_OK) {
fprintf(stderr, "curl_easy_perform() failed: %s\n",
curl_easy_strerror(res));
}
curl_slist_free_all(headers);
curl_easy_cleanup(curl);
}
curl_global_cleanup();
return 0;
}
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[]) {
@autoreleasepool {
NSURL *url = [NSURL URLWithString:@"https://api.apimart.ai/v1/messages"];
NSString *apiKey = [NSProcessInfo processInfo].environment[@"API_KEY"];
NSDictionary *payload = @{
@"model": @"claude-sonnet-4-6",
@"max_tokens": @1024,
@"messages": @[
@{
@"role": @"user",
@"content": @"Hello, world"
}
]
};
NSError *error;
NSData *jsonData = [NSJSONSerialization dataWithJSONObject:payload
options:0
error:&error];
NSMutableURLRequest *request = [NSMutableURLRequest requestWithURL:url];
[request setHTTPMethod:@"POST"];
[request setValue:apiKey forHTTPHeaderField:@"x-api-key"];
[request setValue:@"2025-10-01" forHTTPHeaderField:@"anthropic-version"];
[request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
[request setHTTPBody:jsonData];
NSURLSessionDataTask *task = [[NSURLSession sharedSession]
dataTaskWithRequest:request
completionHandler:^(NSData *data, NSURLResponse *response, NSError *error) {
if (error) {
NSLog(@"Error: %@", error);
return;
}
NSString *result = [[NSString alloc] initWithData:data
encoding:NSUTF8StringEncoding];
NSLog(@"%@", result);
}];
[task resume];
[[NSRunLoop mainRunLoop] run];
}
return 0;
}
(* Requires cohttp and yojson libraries *)
open Lwt
open Cohttp
open Cohttp_lwt_unix
let url = "https://api.apimart.ai/v1/messages"
let api_key = Sys.getenv "API_KEY"
let payload = {|{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello, world"
}
]
}|}
let () =
let headers = Header.init ()
|> fun h -> Header.add h "x-api-key" api_key
|> fun h -> Header.add h "anthropic-version" "2025-10-01"
|> fun h -> Header.add h "Content-Type" "application/json"
in
let body = Cohttp_lwt.Body.of_string payload in
let response = Client.post ~headers ~body (Uri.of_string url) >>= fun (resp, body) ->
body |> Cohttp_lwt.Body.to_string >|= fun body_str ->
print_endline body_str
in
Lwt_main.run response
import 'dart:convert';
import 'dart:io';
import 'package:http/http.dart' as http;
void main() async {
final url = Uri.parse('https://api.apimart.ai/v1/messages');
final apiKey = Platform.environment['API_KEY'];
final payload = {
'model': 'claude-sonnet-4-6',
'max_tokens': 1024,
'messages': [
{
'role': 'user',
'content': 'Hello, world'
}
]
};
final response = await http.post(
url,
headers: {
'x-api-key': apiKey!,
'anthropic-version': '2025-10-01',
'Content-Type': 'application/json',
},
body: jsonEncode(payload),
);
print(response.body);
}
library(httr)
library(jsonlite)
url <- "https://api.apimart.ai/v1/messages"
api_key <- Sys.getenv("API_KEY")
payload <- list(
model = "claude-sonnet-4-6",
max_tokens = 1024,
messages = list(
list(
role = "user",
content = "Hello, world"
)
)
)
response <- POST(
url,
add_headers(
`x-api-key` = api_key,
`anthropic-version` = "2025-10-01",
`Content-Type` = "application/json"
),
body = toJSON(payload, auto_unbox = TRUE),
encode = "raw"
)
cat(content(response, "text"))
{
"model": "claude-sonnet-4-6",
"id": "msg_011CdfeHuC728oxqaLrRNbcB",
"type": "message",
"role": "assistant",
"content": [
{
"type": "text",
"text": "Hello! I'm Claude. Nice to meet you."
}
],
"stop_reason": "end_turn",
"stop_sequence": null,
"stop_details": null,
"usage": {
"input_tokens": 12,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 0
},
"output_tokens": 18,
"service_tier": "standard",
"inference_geo": "global"
}
}
{
"error": {
"code": "model_not_found",
"message": "model not found (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Invalid API key (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Insufficient balance (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Too many requests (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Internal server error (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
Do not mix the two APIs:
/v1/* is the inference API (this document — upstream passthrough, no wrapper); /api/* is the management API (balance/logs, etc., response shape {success, message, data}). If you see docs claiming /v1/messages returns {code, data}, this document takes precedence.curl https://api.apimart.ai/v1/messages \
-H "x-api-key: $API_KEY" \
-H "anthropic-version: 2025-10-01" \
-H "content-type: application/json" \
-d '{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{"role": "user", "content": "Hello, world"}
]
}'
import anthropic
client = anthropic.Anthropic(
api_key="YOUR_API_KEY",
base_url="https://api.apimart.ai"
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Hello, world"}
]
)
print(message.content)
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env.API_KEY,
baseURL: 'https://api.apimart.ai'
});
const message = await client.messages.create({
model: 'claude-sonnet-4-6',
max_tokens: 1024,
messages: [
{ role: 'user', content: 'Hello, world' }
]
});
console.log(message.content);
package main
import (
"bytes"
"encoding/json"
"fmt"
"io/ioutil"
"net/http"
"os"
)
func main() {
url := "https://api.apimart.ai/v1/messages"
payload := map[string]interface{}{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": []map[string]string{
{
"role": "user",
"content": "Hello, world",
},
},
}
jsonData, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
req.Header.Set("x-api-key", os.Getenv("API_KEY"))
req.Header.Set("anthropic-version", "2025-10-01")
req.Header.Set("Content-Type", "application/json")
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
panic(err)
}
defer resp.Body.Close()
body, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(body))
}
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;
public class Main {
public static void main(String[] args) throws Exception {
String url = "https://api.apimart.ai/v1/messages";
String apiKey = System.getenv("API_KEY");
String payload = """
{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello, world"
}
]
}
""";
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(url))
.header("x-api-key", apiKey)
.header("anthropic-version", "2025-10-01")
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(payload))
.build();
HttpResponse<String> response = client.send(request,
HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
}
}
<?php
$url = "https://api.apimart.ai/v1/messages";
$apiKey = getenv('API_KEY');
$payload = [
"model" => "claude-sonnet-4-6",
"max_tokens" => 1024,
"messages" => [
[
"role" => "user",
"content" => "Hello, world"
]
]
];
$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, [
"x-api-key: " . $apiKey,
"anthropic-version: 2025-10-01",
"Content-Type: application/json"
]);
$response = curl_exec($ch);
curl_close($ch);
echo $response;
?>
require 'net/http'
require 'json'
require 'uri'
url = URI("https://api.apimart.ai/v1/messages")
api_key = ENV['API_KEY']
payload = {
model: "claude-sonnet-4-6",
max_tokens: 1024,
messages: [
{
role: "user",
content: "Hello, world"
}
]
}
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = api_key
request["anthropic-version"] = "2025-10-01"
request["Content-Type"] = "application/json"
request.body = payload.to_json
response = http.request(request)
puts response.body
import Foundation
let url = URL(string: "https://api.apimart.ai/v1/messages")!
let apiKey = ProcessInfo.processInfo.environment["API_KEY"] ?? ""
let payload: [String: Any] = [
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
[
"role": "user",
"content": "Hello, world"
]
]
]
var request = URLRequest(url: url)
request.httpMethod = "POST"
request.setValue(apiKey, forHTTPHeaderField: "x-api-key")
request.setValue("2025-10-01", forHTTPHeaderField: "anthropic-version")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try? JSONSerialization.data(withJSONObject: payload)
let task = URLSession.shared.dataTask(with: request) { data, response, error in
if let error = error {
print("Error: \(error)")
return
}
if let data = data, let responseString = String(data: data, encoding: .utf8) {
print(responseString)
}
}
task.resume()
using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
class Program
{
static async Task Main(string[] args)
{
var url = "https://api.apimart.ai/v1/messages";
var apiKey = Environment.GetEnvironmentVariable("API_KEY");
var payload = @"{
""model"": ""claude-sonnet-4-6"",
""max_tokens"": 1024,
""messages"": [
{
""role"": ""user"",
""content"": ""Hello, world""
}
]
}";
using var client = new HttpClient();
client.DefaultRequestHeaders.Add("x-api-key", apiKey);
client.DefaultRequestHeaders.Add("anthropic-version", "2025-10-01");
var content = new StringContent(payload, Encoding.UTF8, "application/json");
var response = await client.PostAsync(url, content);
var result = await response.Content.ReadAsStringAsync();
Console.WriteLine(result);
}
}
#include <stdio.h>
#include <curl/curl.h>
#include <stdlib.h>
int main(void) {
CURL *curl;
CURLcode res;
const char *api_key = getenv("API_KEY");
curl_global_init(CURL_GLOBAL_DEFAULT);
curl = curl_easy_init();
if(curl) {
const char *url = "https://api.apimart.ai/v1/messages";
const char *payload = "{"
"\"model\":\"claude-sonnet-4-6\","
"\"max_tokens\":1024,"
"\"messages\":[{\"role\":\"user\",\"content\":\"Hello, world\"}]"
"}";
char auth_header[256];
snprintf(auth_header, sizeof(auth_header), "x-api-key: %s", api_key);
struct curl_slist *headers = NULL;
headers = curl_slist_append(headers, auth_header);
headers = curl_slist_append(headers, "anthropic-version: 2025-10-01");
headers = curl_slist_append(headers, "Content-Type: application/json");
curl_easy_setopt(curl, CURLOPT_URL, url);
curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payload);
curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
res = curl_easy_perform(curl);
if(res != CURLE_OK) {
fprintf(stderr, "curl_easy_perform() failed: %s\n",
curl_easy_strerror(res));
}
curl_slist_free_all(headers);
curl_easy_cleanup(curl);
}
curl_global_cleanup();
return 0;
}
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[]) {
@autoreleasepool {
NSURL *url = [NSURL URLWithString:@"https://api.apimart.ai/v1/messages"];
NSString *apiKey = [NSProcessInfo processInfo].environment[@"API_KEY"];
NSDictionary *payload = @{
@"model": @"claude-sonnet-4-6",
@"max_tokens": @1024,
@"messages": @[
@{
@"role": @"user",
@"content": @"Hello, world"
}
]
};
NSError *error;
NSData *jsonData = [NSJSONSerialization dataWithJSONObject:payload
options:0
error:&error];
NSMutableURLRequest *request = [NSMutableURLRequest requestWithURL:url];
[request setHTTPMethod:@"POST"];
[request setValue:apiKey forHTTPHeaderField:@"x-api-key"];
[request setValue:@"2025-10-01" forHTTPHeaderField:@"anthropic-version"];
[request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
[request setHTTPBody:jsonData];
NSURLSessionDataTask *task = [[NSURLSession sharedSession]
dataTaskWithRequest:request
completionHandler:^(NSData *data, NSURLResponse *response, NSError *error) {
if (error) {
NSLog(@"Error: %@", error);
return;
}
NSString *result = [[NSString alloc] initWithData:data
encoding:NSUTF8StringEncoding];
NSLog(@"%@", result);
}];
[task resume];
[[NSRunLoop mainRunLoop] run];
}
return 0;
}
(* Requires cohttp and yojson libraries *)
open Lwt
open Cohttp
open Cohttp_lwt_unix
let url = "https://api.apimart.ai/v1/messages"
let api_key = Sys.getenv "API_KEY"
let payload = {|{
"model": "claude-sonnet-4-6",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello, world"
}
]
}|}
let () =
let headers = Header.init ()
|> fun h -> Header.add h "x-api-key" api_key
|> fun h -> Header.add h "anthropic-version" "2025-10-01"
|> fun h -> Header.add h "Content-Type" "application/json"
in
let body = Cohttp_lwt.Body.of_string payload in
let response = Client.post ~headers ~body (Uri.of_string url) >>= fun (resp, body) ->
body |> Cohttp_lwt.Body.to_string >|= fun body_str ->
print_endline body_str
in
Lwt_main.run response
import 'dart:convert';
import 'dart:io';
import 'package:http/http.dart' as http;
void main() async {
final url = Uri.parse('https://api.apimart.ai/v1/messages');
final apiKey = Platform.environment['API_KEY'];
final payload = {
'model': 'claude-sonnet-4-6',
'max_tokens': 1024,
'messages': [
{
'role': 'user',
'content': 'Hello, world'
}
]
};
final response = await http.post(
url,
headers: {
'x-api-key': apiKey!,
'anthropic-version': '2025-10-01',
'Content-Type': 'application/json',
},
body: jsonEncode(payload),
);
print(response.body);
}
library(httr)
library(jsonlite)
url <- "https://api.apimart.ai/v1/messages"
api_key <- Sys.getenv("API_KEY")
payload <- list(
model = "claude-sonnet-4-6",
max_tokens = 1024,
messages = list(
list(
role = "user",
content = "Hello, world"
)
)
)
response <- POST(
url,
add_headers(
`x-api-key` = api_key,
`anthropic-version` = "2025-10-01",
`Content-Type` = "application/json"
),
body = toJSON(payload, auto_unbox = TRUE),
encode = "raw"
)
cat(content(response, "text"))
{
"model": "claude-sonnet-4-6",
"id": "msg_011CdfeHuC728oxqaLrRNbcB",
"type": "message",
"role": "assistant",
"content": [
{
"type": "text",
"text": "Hello! I'm Claude. Nice to meet you."
}
],
"stop_reason": "end_turn",
"stop_sequence": null,
"stop_details": null,
"usage": {
"input_tokens": 12,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 0
},
"output_tokens": 18,
"service_tier": "standard",
"inference_geo": "global"
}
}
{
"error": {
"code": "model_not_found",
"message": "model not found (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Invalid API key (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Insufficient balance (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Too many requests (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
{
"error": {
"code": "",
"message": "Internal server error (request id: 20260803181903172761862QzohKPXF)",
"param": "",
"type": "apimart_error"
}
}
Authorizations
Authentication supports two methods — use either one:string
Anthropic-style API key headerVisit the API Key Management Page to get your API Key
x-api-key: YOUR_API_KEY
string
Bearer token authentication (alternative to
x-api-key)Authorization: Bearer YOUR_API_KEY
string
API version (optional — requests work without it)Recommended for easier future migration to Anthropic’s official endpoint:Example:
2025-10-01Body
string
default:"claude-sonnet-4-6"
required
Model name
claude-opus-4-8- Claude Opus 4.8 flagship modelclaude-opus-4-7- Claude Opus 4.7 flagship modelclaude-opus-4-6- Claude Opus 4.6 flagship modelclaude-sonnet-4-6- Claude Sonnet 4.6 balanced versionclaude-opus-4-5-20251101- Claude Opus 4.5 model
array
required
List of messagesArray of messages for the model to generate the next response. Each message contains
Single user message:Multi-turn conversation:Prefilled assistant response:
role and content fields.💡 Quick fill (Try it area):- Click ”+ Add an item” to add a message
roleinput:user(user message) orassistant(AI response, for multi-turn)contentinput: your message text
Show Field details
Show Field details
[{"role": "user", "content": "Hello, Claude"}]
[
{"role": "user", "content": "Hello there."},
{"role": "assistant", "content": "Hi, I'm Claude. How can I help you?"},
{"role": "user", "content": "Can you explain LLMs in plain English?"}
]
[
{"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"},
{"role": "assistant", "content": "The best answer is ("}
]
integer
required
Maximum tokens to generate (required, same as Anthropic official)Maximum number of tokens to generate before stopping. The model may stop before reaching this limit.Different models have different maximum values. See model docs. Minimum: 1
object
Extended thinking configurationWhen enabled, the response
content may include thinking blocks. Prefer the standard model name plus this parameter over platform-side -thinking model aliases, so you can migrate to the official endpoint without code changes.If multi-turn conversations need to pass thinking blocks back, you must return the signature unchanged, or the upstream will reject the request.string | array
System promptSystem prompts set Claude’s role, personality, goals, and instructions.String format:Structured format:
{
"system": "You are a professional Python programming tutor"
}
{
"system": [
{
"type": "text",
"text": "You are a professional Python programming tutor"
}
]
}
number
Temperature parameter, range 0-1Controls randomness of output:
- Low values (e.g., 0.2): More deterministic, conservative
- High values (e.g., 0.8): More random, creative
number
Nucleus sampling parameter, range 0-1Uses nucleus sampling. Recommend using either
temperature or top_p, not both.Default: 1.0integer
Top-K samplingSample from top K options only, removes “long tail” low probability responses.Recommended for advanced use cases only.
boolean
Enable streamingWhen
true, uses Server-Sent Events (SSE) to stream responses.Default: falsearray
Stop sequencesCustom text sequences that cause the model to stop generating.Maximum 4 sequences.Example:
["\n\nHuman:", "\n\nAssistant:"]object
MetadataMetadata object for the request.Includes:
user_id: User identifier
array
Tool definitionsList of tools the model can use to complete tasks.Function tool example:Supported tool types:
{
"tools": [
{
"name": "get_weather",
"description": "Get the current weather in a given location",
"input_schema": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "Temperature unit"
}
},
"required": ["location"]
}
}
]
}
- Custom function tools
- Computer use tool (computer_20241022)
- Text editor tool (text_editor_20241022)
- Bash tool (bash_20241022)
object
Tool choice strategyControls how the model uses tools:
{"type": "auto"}: Auto-decide (default){"type": "any"}: Must use a tool{"type": "tool", "name": "tool_name"}: Use specific tool
Response
string
Unique message identifierExample:
"msg_013Zva2CMHLNnXjNJJKqJ2EF"string
Object typeAlways
"message"string
RoleAlways
"assistant"array
Content blocks arraytool_use block:thinking block (present when the request body includes the
content is an array of blocks distinguished by type. A single response may contain multiple blocks (for example, with thinking enabled: a thinking block plus a text block).text block:{ "type": "text", "text": "OK" }
{
"type": "tool_use",
"id": "toolu_01QgsazxKXSfQVj9Q1XxjYXo",
"name": "get_weather",
"input": { "city": "Beijing" },
"caller": { "type": "direct" }
}
caller is a newer upstream field not yet documented officially; ignore it when parsing.thinking parameter):{
"type": "thinking",
"thinking": "Reasoning process text...",
"signature": "<signature string, typically 500+ characters>"
}
When returning thinking blocks in multi-turn conversations, you must pass
signature back unchanged, or the upstream will reject the request.Do not assume
content[0] is text. With thinking enabled, content[0] may be a thinking block. Iterate and filter:text = "".join(b.text for b in resp.content if b.type == "text")
string
Model that handled the requestExample:
"claude-sonnet-4-6"string
Stop reasonPossible values:
end_turn: Natural completionmax_tokens: Reached maximum tokensstop_sequence: Hit stop sequencetool_use: Invoked a tool
string | null
Stop sequence triggeredThe stop sequence that was generated, if any; otherwise
nullobject | null
Newer Anthropic field;
null for typical requestsobject
Token usage statistics (full structure for non-streaming)
Show Properties
Show Properties
integer
Number of input tokens
integer
Number of output tokens (already includes thinking tokens; do not double-count for billing)
integer
Cache write tokens
integer
Cache hit tokens
object
{ ephemeral_5m_input_tokens, ephemeral_1h_input_tokens }string
e.g.
"standard"string
e.g.
"global"object
May appear when thinking is enabled:
{ thinking_tokens: int }Usage Examples
Basic Conversation
import anthropic
client = anthropic.Anthropic(
api_key="YOUR_API_KEY",
base_url="https://api.apimart.ai"
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain quantum computing basics"}
]
)
print(message.content[0].text)
Multi-turn Conversation
messages = [
{"role": "user", "content": "What is machine learning?"},
{"role": "assistant", "content": "Machine learning is a branch of AI..."},
{"role": "user", "content": "Can you give a practical example?"}
]
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=messages
)
Using System Prompts
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system="You are a senior Python developer expert in code review and optimization.",
messages=[
{"role": "user", "content": "How to optimize this code?\n\n[code]"}
]
)
Streaming Response
with client.messages.stream(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Write a short essay about AI"}
]
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
Tool Use
tools = [
{
"name": "get_stock_price",
"description": "Get real-time stock price",
"input_schema": {
"type": "object",
"properties": {
"ticker": {
"type": "string",
"description": "Stock ticker symbol, e.g., AAPL"
}
},
"required": ["ticker"]
}
}
]
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
tools=tools,
messages=[
{"role": "user", "content": "What's Tesla's stock price?"}
]
)
# Handle tool calls
if message.stop_reason == "tool_use":
tool_use = next(block for block in message.content if block.type == "tool_use")
print(f"Calling tool: {tool_use.name}")
print(f"Arguments: {tool_use.input}")
Vision Understanding
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "url",
"url": "https://example.com/image.jpg"
}
},
{
"type": "text",
"text": "Describe this image"
}
]
}
]
)
Base64 Image
import base64
with open("image.jpg", "rb") as image_file:
image_data = base64.b64encode(image_file.read()).decode("utf-8")
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": image_data
}
},
{
"type": "text",
"text": "Analyze this image"
}
]
}
]
)
Best Practices
1. Prompt Engineering
Clear role definition:system = """You are an experienced data scientist specializing in:
- Statistical analysis and data visualization
- Machine learning model development
- Python and R programming
Provide professional, accurate advice."""
message = "Please return the analysis results in JSON format with summary, key_findings, and recommendations fields."
2. Error Handling
from anthropic import APIError, RateLimitError
try:
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello"}]
)
except RateLimitError:
print("Rate limit exceeded, please retry later")
except APIError as e:
print(f"API error: {e}")
3. Token Optimization
# Use shorter prompts
messages = [
{"role": "user", "content": "Summarize key points:\n\n[long text]"}
]
# Limit output length
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=500, # Limit output
messages=messages
)
4. Prefilling Responses
# Guide model to specific format
messages = [
{"role": "user", "content": "List 5 Python best practices"},
{"role": "assistant", "content": "Here are 5 Python best practices:\n\n1."}
]
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=messages
)
Streaming Response Handling
Python Streaming
import anthropic
client = anthropic.Anthropic(
api_key="YOUR_API_KEY",
base_url="https://api.apimart.ai"
)
with client.messages.stream(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Write a Python decorator example"}
]
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
JavaScript Streaming
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env.API_KEY,
baseURL: 'https://api.apimart.ai'
});
const stream = await client.messages.stream({
model: 'claude-sonnet-4-6',
max_tokens: 1024,
messages: [
{ role: 'user', content: 'Write a React component example' }
]
});
for await (const chunk of stream) {
if (chunk.type === 'content_block_delta' &&
chunk.delta.type === 'text_delta') {
process.stdout.write(chunk.delta.text);
}
}
Platform Differences & Integration Notes
Unwrapped response
SuccessfulPOST /v1/messages responses return the Anthropic message object directly, with no {code, data} wrapper. This is required for 1:1 compatibility with official SDKs, Claude Code, Cline, and similar tools.
Error format (only real incompatibility with official)
{
"error": {
"code": "model_not_found",
"message": "... (request id: ...)",
"param": "",
"type": "apimart_error"
}
}
"type": "error"; error.type is always apimart_error, not semantic types like invalid_request_error.
Integration guidance: do not branch retries on error.type; use HTTP status + error.code instead:
| Status | Meaning | Suggested action |
|---|---|---|
| 400 | Bad request parameters | Do not retry; fix the request body |
| 401 | Invalid key | Do not retry |
| 402 | Insufficient balance | Do not retry; prompt top-up |
| 429 | Rate limited | Retry with backoff |
| 5xx | Upstream/gateway error | Retry with exponential backoff |
error.message and the response header x-oneapi-request-id.
Streaming SSE
Send"stream": true. Event sequence matches official:
message_start → content_block_start → ping → content_block_delta (multiple) → content_block_stop → message_delta → message_stop
⚠️ Stream vs non-stream usage differs: message_delta.usage typically has only 4 token fields and does not include cache_creation, service_tier, or inference_geo. Parse them separately or treat all as optional.
Unimplemented endpoint
POST /v1/messages/count_tokens is not implemented and returns 404. Official SDK client.messages.count_tokens() will fail. Estimate tokens locally, or read usage.input_tokens from responses.
Ignore unknown fields
This endpoint passes through upstream fields. Anthropic may add fields at any time (e.g.stop_details, inference_geo, caller, output_tokens_details). Do not use strict schemas:
- Go: do not use
DisallowUnknownFields() - Pydantic: do not use
extra="forbid" - TypeScript / Zod: use
.passthrough()instead of.strict()
Model name recommendation
Same-name models with a-thinking suffix are platform extension aliases. Prefer the standard model name without the suffix plus the request-body thinking parameter for easier migration to the official endpoint.
Other request-body fields match official: model, messages, max_tokens (required), system, temperature, top_p, top_k, stop_sequences, stream, tools, tool_choice, thinking, metadata. Semantics follow the Anthropic Messages API.
Important Notes
-
API Key Security:
- Store API keys in environment variables
- Never hardcode keys in source code
- Rotate keys regularly
-
Rate Limiting:
- Be aware of API rate limits
- Implement retry mechanisms (by HTTP status code)
- Use exponential backoff
-
Token Management:
- Monitor token usage (read
usage) - Optimize prompt length
- Use appropriate
max_tokensvalues - With thinking enabled,
output_tokensalready includes thinking tokens — do not double-count for billing
- Monitor token usage (read
-
Model Selection:
- Opus: Complex tasks, deep thinking required
- Sonnet: Balanced performance and cost
- Haiku: Fast response, simple tasks
-
Content parsing:
- Iterate
contentfortype == "text"; do not hardcodecontent[0].text - If the model returns JSON wrapped in Markdown code fences, that is model output — not an API wrapper (see FAQ below)
- Iterate
-
Content Filtering:
- Validate user input
- Filter sensitive information
- Implement content moderation
FAQ
The response content text is a ```json ... ``` code fence — how do I strip it?
This is not an API structure issue. The text field holds the raw model-generated content: if the model decides you want JSON, it may wrap it in a Markdown code fence. The API does not and should not rewrite model output.
To get clean structured data, use one of these three approaches (recommended from highest to lowest reliability):
- Use tools to force structured output — most reliable; the
inputfield is already a parsed object:
{
"tools": [{
"name": "emit_result",
"input_schema": {
"type": "object",
"properties": { "answer": { "type": "string" } }
}
}],
"tool_choice": { "type": "tool", "name": "emit_result" }
}
- Prefill the assistant message so the model continues from
{:
{
"messages": [
{ "role": "user", "content": "..." },
{ "role": "assistant", "content": "{" }
]
}
- In the system prompt, explicitly require “output JSON only, with no Markdown code fences.”