OpenAI 兼容接口升级：深度支持 Claude

我们针对 Claude 系列模型对 OpenAI 兼容接口进行了更深入的优化。现在你可以更精确、更便捷地控制思考和缓存。多轮对话中的交错思考也更加易用，无需额外参数即可无缝集成。同时，还支持启用 Anthropic 提供的 beta 功能。

1. 模型思考（Extended Thinking）

1.1 交错思考的优势

未启用交错思考时，模型只会在助手回合开始时进行一次思考；收到工具结果后，后续回复会直接生成，不会产生新的思考块：

User → [Thinking] → Tool Call → Tool Result → Response

启用交错思考后，模型每次收到工具结果时都会插入一个新的思考块，形成推理链：

User → [Thinking] → Tool Call → Tool Result → [Thinking] → Response
                                                ↑ Interleaved Thinking

这使模型能够：

• 基于工具结果进行二次推理，而不是简单拼接输出。
• 在多次工具调用之间进行链式推理，每个决策都基于上一步的分析。

参考：Anthropic 交错思考

1.2 启用思考

你可以通过以下四种方式之一启用思考，任选其一即可：

优先级（同时使用多种方式时）：reasoning_effort > reasoning.max_tokens > reasoning.effort > -think 后缀

effort 可选值： minimal / low / medium / high / xhigh

1.3 思考内容返回

响应消息将包含两个新字段：

• reasoning_content：思考内容（字符串），便于直接展示。
• reasoning_details：关于思考的完整结构化信息，在多轮对话中需要原样返回；不同提供商的内部结构可能有所不同。

非流式响应示例（省略无关字段）：

{
  "choices": [{
    "message": {
      "role": "assistant",
      "content": "Hello! How can I help you today?",
      "reasoning_content": "The user is just saying hello...",
      "reasoning_details": {
        "type": "thinking",
        "thinking": "The user is just saying hello...",
        "signature": "Er8CCkYI..."
      }
    }
  }]
}

在流式响应中，思考内容会通过 delta.reasoning_content 和 delta.reasoning_details 分块发送。完整的流式拼接逻辑请参考下方的完整示例。

1.4 在多轮对话中保留思考 （交错思考已内置，无需额外参数）

要让模型在多轮对话中延续其推理能力，只需将之前返回的 reasoning_details 原样放入下一轮的助手消息中：

messages = [
    {"role": "user", "content": "What's the weather like in Boston?"},
    {
        "role": "assistant",
        "content": response.choices[0].message.content,
        "tool_calls": response.choices[0].message.tool_calls,
        "reasoning_details": response.choices[0].message.reasoning_details,
    },
    {
        "role": "tool",
        "tool_call_id": "toolu_xxx",
        "content": '{"temperature": 45, "condition": "rainy"}',
    }
]

当检测到请求中包含历史思考信息时，AIHubMix 将自动启用交错思考，使模型在收到工具调用结果后能够继续深入推理，无需额外参数。

1.5 完整示例

以下两个示例展示了完整的多轮工具调用 + 交错思考流程：用户提问 → 模型思考并调用工具 → 注入工具结果（保留 reasoning_details）→ 模型交错思考后给出最终回复。

非流式 · 交错思考

import os
import json
from openai import OpenAI

client = OpenAI(
    base_url="https://aihubmix.com/v1",
    api_key=os.environ.get("AIHUBMIX_API_KEY", "sk-***"),
)

# ── Tool definition ───────────────────────────────────────────
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string", "description": "City name"}},
            "required": ["location"]
        }
    }
}]

# ── Mock tool execution ───────────────────────────────────────
WEATHER_DB = {
    "boston": {"temperature": "45°F (7°C)", "condition": "rainy", "humidity": "85%", "wind": "15 mph NE"},
    "tokyo":  {"temperature": "72°F (22°C)", "condition": "sunny", "humidity": "45%", "wind": "5 mph S"},
}

def execute_tool(name: str, args: dict) -> str:
    if name == "get_weather":
        key = next((k for k in WEATHER_DB if k in args.get("location", "").lower()), None)
        return json.dumps(WEATHER_DB.get(key, {"temperature": "65°F", "condition": "clear"}))
    return "{}"

# ── Multi-turn conversation loop ─────────────────────────────
messages = [
    {"role": "user", "content": "What's the weather like in Boston? Then recommend what to wear."}
]

turn = 0
while True:
    turn += 1
    print(f"\n── Turn {turn} ──")

    response = client.chat.completions.create(
        model="claude-sonnet-4-5",
        messages=messages,
        tools=tools,
        extra_body={"reasoning": {"max_tokens": 2000}},
    )
    msg = response.choices[0].message

    # Print thinking process
    if msg.reasoning_content:
        label = "Interleaved Thinking" if turn > 1 else "Thinking"
        print(f"[{label}] {msg.reasoning_content}")

    # Print response content
    if msg.content:
        print(f"[Response] {msg.content}")

    # Print tool calls
    if msg.tool_calls:
        for tc in msg.tool_calls:
            print(f"[Tool Call: {tc.function.name}] {tc.function.arguments}")

    # Build assistant message, preserve reasoning_details (critical!)
    assistant_msg = {"role": "assistant", "content": msg.content}
    if msg.tool_calls:
        assistant_msg["tool_calls"] = msg.tool_calls
    if msg.reasoning_details:
        assistant_msg["reasoning_details"] = msg.reasoning_details  # pass back unmodified
    messages.append(assistant_msg)

    # No tool_calls means conversation is done
    if not msg.tool_calls:
        break

    # Execute tools and append results to messages
    for tc in msg.tool_calls:
        args = json.loads(tc.function.arguments)
        result = execute_tool(tc.function.name, args)
        print(f"[Tool Result: {tc.function.name}] {result}")
        messages.append({"role": "tool", "tool_call_id": tc.id, "content": result})

流式 · 交错思考

import os
import sys
import json
from openai import OpenAI

client = OpenAI(
    base_url="https://aihubmix.com/v1",
    api_key=os.environ.get("AIHUBMIX_API_KEY", "sk-***"),
)

# ── Tool definition & mock execution ─────────────────────────
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string", "description": "City name"}},
            "required": ["location"]
        }
    }
}]

WEATHER_DB = {
    "boston": {"temperature": "45°F (7°C)", "condition": "rainy", "humidity": "85%", "wind": "15 mph NE"},
    "tokyo":  {"temperature": "72°F (22°C)", "condition": "sunny", "humidity": "45%", "wind": "5 mph S"},
}

def execute_tool(name: str, args: dict) -> str:
    if name == "get_weather":
        key = next((k for k in WEATHER_DB if k in args.get("location", "").lower()), None)
        return json.dumps(WEATHER_DB.get(key, {"temperature": "65°F", "condition": "clear"}))
    return "{}"

# ── Stream response collector ────────────────────────────────
def stream_and_collect(turn: int, **kwargs):
    """Stream response, print thinking/content in real-time, accumulate reasoning_details/tool_calls."""
    rd = {}            # accumulated reasoning_details
    content = ""       # accumulated response text
    tc_map = {}        # accumulated tool_calls (by index)
    cur = "none"       # current output section: none / thinking / content

    stream = client.chat.completions.create(stream=True, **kwargs)
    for chunk in stream:
        if not chunk.choices:
            continue
        delta = chunk.choices[0].delta

        # ── Handle thinking ──
        rd_delta = getattr(delta, "reasoning_details", None)
        if rd_delta and isinstance(rd_delta, dict):
            for k, v in rd_delta.items():
                if k == "type":
                    rd[k] = v
                elif isinstance(v, str):
                    rd[k] = rd.get(k, "") + v
                elif v is not None:
                    rd[k] = v
            # Print thinking chunks in real-time
            thinking_chunk = rd_delta.get("thinking", "")
            if thinking_chunk:
                if cur != "thinking":
                    cur = "thinking"
                    label = "Interleaved Thinking" if turn > 1 else "Thinking"
                    sys.stdout.write(f"\n[{label}] ")
                sys.stdout.write(thinking_chunk)
                sys.stdout.flush()

        # ── Handle content ──
        if delta.content:
            if cur != "content":
                if cur == "thinking":
                    sys.stdout.write("\n")
                cur = "content"
                sys.stdout.write("\n[Response] ")
            sys.stdout.write(delta.content)
            sys.stdout.flush()
            content += delta.content

        # ── Handle tool_calls ──
        for tc in delta.tool_calls or []:
            i = tc.index
            if i not in tc_map:
                tc_map[i] = {"id": "", "type": "function",
                             "function": {"name": "", "arguments": ""}}
            if tc.id:
                tc_map[i]["id"] = tc.id
            if tc.function:
                tc_map[i]["function"]["name"] += tc.function.name or ""
                tc_map[i]["function"]["arguments"] += tc.function.arguments or ""

    # End current output section
    if cur in ("thinking", "content"):
        sys.stdout.write("\n")

    tool_calls = [tc_map[i] for i in sorted(tc_map)] if tc_map else None
    return {
        "content": content or None,
        "reasoning_details": rd or None,
        "tool_calls": tool_calls,
    }

# ── Multi-turn conversation loop ─────────────────────────────
messages = [
    {"role": "user", "content": "What's the weather like in Boston? Then recommend what to wear."}
]

turn = 0
while True:
    turn += 1
    print(f"\n── Turn {turn} ──")

    result = stream_and_collect(
        turn,
        model="claude-sonnet-4-5",
        messages=messages,
        tools=tools,
        extra_body={"reasoning": {"max_tokens": 2000}},
    )

    # Print tool calls
    if result["tool_calls"]:
        for tc in result["tool_calls"]:
            print(f"[Tool Call: {tc['function']['name']}] {tc['function']['arguments']}")

    # Build assistant message, preserve reasoning_details (critical!)
    assistant_msg = {"role": "assistant", "content": result["content"]}
    if result["tool_calls"]:
        assistant_msg["tool_calls"] = result["tool_calls"]
    if result["reasoning_details"]:
        assistant_msg["reasoning_details"] = result["reasoning_details"]  # pass back unmodified
    messages.append(assistant_msg)

    # No tool_calls means conversation is done
    if not result["tool_calls"]:
        break

    # Execute tools and append results to messages
    for tc in result["tool_calls"]:
        args = json.loads(tc["function"]["arguments"])
        tool_result = execute_tool(tc["function"]["name"], args)
        print(f"[Tool Result: {tc['function']['name']}] {tool_result}")
        messages.append({"role": "tool", "tool_call_id": tc["id"], "content": tool_result})

1.6 思考强度映射规则

Effort 模式：

• Opus 4.6 / Sonnet 4.6 及更高版本：映射到 Anthropic 原生的 Adaptive Thinking effort 级别。
• 其他模型：使用 budget_tokens 的公式计算：

budget_tokens = max(min(max_tokens × effort_ratio, 128000), 1024)

Adaptive Thinking Effort 映射：

max_tokens 模式： 直接赋值为 Anthropic 的 budget_tokens。

-think 后缀： Opus/Sonnet 4.6+ 使用自适应思考（effort=medium）；其他模型设置 budget_tokens = min(10240, max_tokens - 1)，默认 max_tokens 为 4096。

2. 提示词缓存（Prompt Caching）

通过 Chat 接口请求 Claude 模型时，你可以使用提示词缓存。通过在消息中设置 cache_control 断点，可以缓存大段文本（如角色设定、RAG 数据、书籍章节等）以便重复使用，后续请求即可直接命中缓存，从而显著降低成本。

Claude 官方文档：Prompt Caching

2.1 缓存费用

2.2 支持的模型与最小缓存长度

断点数量上限： 每个请求最多 4 个 cache_control 断点。

2.3 缓存 TTL

1 小时 TTL 的写入费用更高，但在较长的会话中可以减少重复写入，从而节省总体开销。Claude 4.5 及之后的所有模型在各提供商（包括 Anthropic、Amazon Bedrock、Google Vertex AI）中均支持 1 小时 TTL。

2.4 用法

你可以在 system、user（包括图片）和 tools 中使用 cache_control 字段设置缓存断点。以下示例仅展示关键结构，省略了大段文本。

System 消息缓存（默认 5 分钟 TTL）：

{
  "model": "claude-opus-4-5",
  "messages": [
    {
      "role": "system",
      "content": [
        {"type": "text", "text": "You are an AI assistant"},
        {
          "type": "text",
          "text": "(long context)",
          "cache_control": {"type": "ephemeral"}
        }
      ]
    },
    {
      "role": "user",
      "content": [{"type": "text", "text": "Hello"}]
    }
  ]
}

User 消息缓存（1 小时 TTL）：

{
  "model": "claude-opus-4-5",
  "messages": [
    {
      "role": "system",
      "content": [{"type": "text", "text": "You are an AI assistant"}]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "(long context)",
          "cache_control": {"type": "ephemeral", "ttl": "1h"}
        },
        {"type": "text", "text": "Hello"}
      ]
    }
  ]
}

图片消息缓存：

{
  "role": "user",
  "content": [
    {
      "type": "image_url",
      "image_url": {"detail": "auto", "url": "data:image/jpeg;base64,/9j/4AAQ..."},
      "cache_control": {"type": "ephemeral"}
    },
    {"type": "text", "text": "What's this?"}
  ]
}

工具定义缓存：

cache_control 置于工具对象的顶层（与 type 和 function 并列）：

{
  "tools": [{
    "type": "function",
    "function": {
      "name": "get_weather",
      "description": "Get current weather for a location",
      "parameters": {
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"]
      }
    },
    "cache_control": {"type": "ephemeral", "ttl": "1h"}
  }]
}

2.5 查看缓存状态

响应中的 usage 会返回 claude_cache_tokens_details，记录详细的缓存信息：

首次请求（创建缓存）：

{
  "usage": {
    "prompt_tokens": 22,
    "completion_tokens": 890,
    "total_tokens": 912,
    "claude_cache_tokens_details": {
      "cache_creation_input_tokens": 6266,
      "cache_read_input_tokens": 0,
      "cache_write_5_minutes_input_tokens": 6266,
      "cache_write_1_hour_input_tokens": 0
    }
  }
}

后续请求（命中缓存）：

{
  "usage": {
    "prompt_tokens": 22,
    "completion_tokens": 810,
    "total_tokens": 832,
    "prompt_tokens_details": {
      "cached_tokens": 6266
    },
    "claude_cache_tokens_details": {
      "cache_creation_input_tokens": 0,
      "cache_read_input_tokens": 6266,
      "cache_write_5_minutes_input_tokens": 0,
      "cache_write_1_hour_input_tokens": 0
    }
  }
}

3. anthropic-beta 请求头

你可以通过 HTTP 请求头 anthropic-beta 启用 Claude 模型的 beta 功能，AIHubMix 会将其透传给 Anthropic API。

用法

在请求头中添加 anthropic-beta，值为对应 beta 功能的标识符：

curl "https://aihubmix.com/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "anthropic-beta: context-1m-2025-08-07" \
  -d '{
  "model": "claude-opus-4-5",
  "messages": [
    {
      "role": "system",
      "content": [
        {"type": "text", "text": "You are an AI assistant"},
        {
          "type": "text",
          "text": "(long context)",
          "cache_control": {"type": "ephemeral"}
        }
      ]
    },
    {"role": "user", "content": [{"type": "text", "text": "hello"}]}
  ]
}'

具体可用的 beta 标识符，请参阅 Anthropic API 文档。

最后更新：2026-06-01