OpenRouter 对话模型 API 文档

Z.ai: GLM 5

import { OpenRouter } from "@openrouter/sdk";

const openrouter = new OpenRouter({
  apiKey: "<OPENROUTER_API_KEY>"
});

// Stream the response to get reasoning tokens in usage
const stream = await openrouter.chat.send({
  model: "z-ai/glm-5",
  messages: [
    {
      role: "user",
      content: "How many r's are in the word 'strawberry'?"
    }
  ],
  stream: true
});

let response = "";
for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    response += content;
    process.stdout.write(content);
  }

  // Usage information comes in the final chunk
  if (chunk.usage) {
    console.log("\nReasoning tokens:", chunk.usage.reasoningTokens);
  }
}

xAI: Grok 4 Fast

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

completion = client.chat.completions.create(
  extra_headers={
    "HTTP-Referer": "<YOUR_SITE_URL>", # Optional. Site URL for rankings on openrouter.ai.
    "X-OpenRouter-Title": "<YOUR_SITE_NAME>", # Optional. Site title for rankings on openrouter.ai.
  },
  extra_body={},
  model="x-ai/grok-4-fast",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What is in this image?"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://live.staticflickr.com/3851/14825276609_098cac593d_b.jpg"
          }
        }
      ]
    }
  ]
)
print(completion.choices[0].message.content)

Google: Gemini 3.1 Flash Lite Preview

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

# First API call with reasoning
response = client.chat.completions.create(
  model="google/gemini-3.1-flash-lite-preview",
  messages=[
          {
            "role": "user",
            "content": "How many r's are in the word 'strawberry'?"
          }
        ],
  extra_body={"reasoning": {"enabled": True}}
)

# Extract the assistant message with reasoning_details
response = response.choices[0].message

# Preserve the assistant message with reasoning_details
messages = [
  {"role": "user", "content": "How many r's are in the word 'strawberry'?"},
  {
    "role": "assistant",
    "content": response.content,
    "reasoning_details": response.reasoning_details  # Pass back unmodified
  },
  {"role": "user", "content": "Are you sure? Think carefully."}
]

# Second API call - model continues reasoning from where it left off
response2 = client.chat.completions.create(
  model="google/gemini-3.1-flash-lite-preview",
  messages=messages,
  extra_body={"reasoning": {"enabled": True}}
)

Qwen: Qwen3.5-9B

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

# First API call with reasoning
response = client.chat.completions.create(
  model="qwen/qwen3.5-9b",
  messages=[
          {
            "role": "user",
            "content": "How many r's are in the word 'strawberry'?"
          }
        ],
  extra_body={"reasoning": {"enabled": True}}
)

# Extract the assistant message with reasoning_details
response = response.choices[0].message

# Preserve the assistant message with reasoning_details
messages = [
  {"role": "user", "content": "How many r's are in the word 'strawberry'?"},
  {
    "role": "assistant",
    "content": response.content,
    "reasoning_details": response.reasoning_details  # Pass back unmodified
  },
  {"role": "user", "content": "Are you sure? Think carefully."}
]

# Second API call - model continues reasoning from where it left off
response2 = client.chat.completions.create(
  model="qwen/qwen3.5-9b",
  messages=messages,
  extra_body={"reasoning": {"enabled": True}}
)

ByteDance Seed: Seed-2.0-Lite

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

# First API call with reasoning
response = client.chat.completions.create(
  model="bytedance-seed/seed-2.0-lite",
  messages=[
          {
            "role": "user",
            "content": "How many r's are in the word 'strawberry'?"
          }
        ],
  extra_body={"reasoning": {"enabled": True}}
)

# Extract the assistant message with reasoning_details
response = response.choices[0].message

# Preserve the assistant message with reasoning_details
messages = [
  {"role": "user", "content": "How many r's are in the word 'strawberry'?"},
  {
    "role": "assistant",
    "content": response.content,
    "reasoning_details": response.reasoning_details  # Pass back unmodified
  },
  {"role": "user", "content": "Are you sure? Think carefully."}
]

# Second API call - model continues reasoning from where it left off
response2 = client.chat.completions.create(
  model="bytedance-seed/seed-2.0-lite",
  messages=messages,
  extra_body={"reasoning": {"enabled": True}}
)

OpenAI: GPT-4o

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

completion = client.chat.completions.create(
  extra_headers={
    "HTTP-Referer": "<YOUR_SITE_URL>", # Optional. Site URL for rankings on openrouter.ai.
    "X-OpenRouter-Title": "<YOUR_SITE_NAME>", # Optional. Site title for rankings on openrouter.ai.
  },
  extra_body={},
  model="openai/gpt-4o",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What is in this image?"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://live.staticflickr.com/3851/14825276609_098cac593d_b.jpg"
          }
        }
      ]
    }
  ]
)
print(completion.choices[0].message.content)