跨多个提供商工作

1import requests
2
3def find_capable_models():
4    """
5    Fetches all models from OpenRouter and filters them
6    to find those that support tools and have a large context window.
7    """
8    try:
9        response = requests.get("https://openrouter.ai/api/v1/models")
10        response.raise_for_status() # Raise an exception for bad status codes
11
12        models_data = response.json().get('data', [])
13
14        print("Found models that support tool calling and have >32k context length:")
15
16        for model in models_data:
17            # Check if 'tools' is listed in the model's supported parameters
18            supports_tools = "tools" in model.get("supported_parameters", [])
19
20            # Check if context length is greater than 32,000
21            context_length = model.get("context_length", 0)
22
23            if supports_tools and context_length > 32000:
24                model_id = model.get("id")
25                print(f"- {model_id} (Context: {context_length})")
26
27    except requests.exceptions.RequestException as e:
28        print(f"An error occurred: {e}")
29
30find_capable_models()

单个模型的示例输出对象

1{
2  "id": "openai/gpt-5.4",
3  "canonical_slug": "openai/gpt-5.4-20260305",
4  "hugging_face_id": "",
5  "name": "OpenAI: GPT-5.4",
6  "created": 1772734352,
7  "description": "GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for text and image inputs, enabling high-context reasoning, coding, and multimodal analysis within the same workflow.\n\nThe model delivers improved performance in coding, document understanding, tool use, and instruction following. It is designed as a strong default for both general-purpose tasks and software engineering, capable of generating production-quality code, synthesizing information across multiple sources, and executing complex multi-step workflows with fewer iterations and greater token efficiency.",
8  "context_length": 1050000,
9  "architecture": {
10    "modality": "text+image+file->text",
11    "input_modalities": [
12      "text",
13      "image",
14      "file"
15    ],
16    "output_modalities": [
17      "text"
18    ],
19    "tokenizer": "GPT",
20    "instruct_type": null
21  },
22  "pricing": {
23    "prompt": "0.0000025",
24    "completion": "0.000015",
25    "web_search": "0.01",
26    "input_cache_read": "0.00000025"
27  },
28  "top_provider": {
29    "context_length": 1050000,
30    "max_completion_tokens": 128000,
31    "is_moderated": true
32  },
33  "per_request_limits": null,
34  "supported_parameters": [
35    "frequency_penalty",
36    "include_reasoning",
37    "logit_bias",
38    "logprobs",
39    "max_tokens",
40    "presence_penalty",
41    "reasoning",
42    "response_format",
43    "seed",
44    "stop",
45    "structured_outputs",
46    "tool_choice",
47    "tools",
48    "top_logprobs"
49  ],
50  "default_parameters": {
51    "temperature": null,
52    "top_p": null,
53    "top_k": null,
54    "frequency_penalty": null,
55    "presence_penalty": null,
56    "repetition_penalty": null
57  },
58  "expiration_date": null
59}

在运行时选择模型的示例代码片段

1import os
2
3# Model name is loaded from an environment variable
4# Changing it requires no code change or redeployment
5SUMMARY_MODEL = os.getenv("SUMMARY_MODEL", "google/gemini-3.1-flash-lite-preview")
6
7def generate_summary(text):
8    response = call_openrouter(
9        model_name=SUMMARY_MODEL,
10        prompt=f"Summarize this: {text}"
11    )
12    # ...