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Prism

概覽​

PropertyDetails
DescriptionPrism Inference 透過 OpenAI Chat Completions、OpenAI Responses 與 Anthropic Messages APIs 提供開放權重模型，輸入與輸出皆零資料保留。
Provider Route on LiteLLMprism/
Link to Provider DocPrism 文件 ↗
Base URLhttps://api.prisminference.com/v1
Supported Operations/chat/completions, /responses, /messages


我們支援所有 Prism 模型，傳送請求時只要將 prism/ 設為前綴即可

可用模型​

ModelDescriptionContext WindowMax Output
prism/deepseek-v4.1-flashDeepSeek-V4.1-Flash，現行一代，適用於程式設計、視覺、推理與工具使用迴圈；可接受文字與圖片輸入1,000,000 tokens384,000 tokens
prism/deepseek-v4-flashDeepSeek-V4-Flash，純文字模型，適合快速程式設計與工具迴圈1,000,000 tokens384,000 tokens

這兩個模型都支援推理、函式呼叫、JSON 模式與 JSON schema 輸出，LiteLLM 也會隨附其定價（輸入、輸出與快取輸入），因此開箱即可追蹤支出。Prism 在 GET https://api.prisminference.com/v1/models 的型錄列出更多模型（例如 glm-5.3 與 kimi-k3）；任何一個都可搭配 prism/ 前綴使用，但若要追蹤那些模型的支出，您需要傳入 input_cost_per_token 與 output_cost_per_token 到 litellm_params，直到它們被加入 LiteLLM 的模型成本對照表。

必要變數​

Environment Variables
os.environ["PRISM_API_KEY"] = ""  # your Prism API key

用法 - LiteLLM Python SDK​

非串流​

Prism Non-streaming Completion
import os
import litellm
from litellm import completion

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

# Prism call
response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=messages
)

print(response)

串流​

Prism Streaming Completion
import os
import litellm
from litellm import completion

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

messages = [{"content": "Write a short story about AI", "role": "user"}]

# Prism call with streaming
response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk)

函式呼叫​

Prism Function Calling
import os
import litellm
from litellm import completion

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather in a location",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "The city, e.g. San Francisco"
                }
            },
            "required": ["city"]
        }
    }
}]

messages = [{"role": "user", "content": "What's the weather in San Francisco?"}]

response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response)

結構化輸出​

Prism JSON Schema Output
import os
from litellm import completion

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=[{"role": "user", "content": "The city is San Francisco"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "city",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
                "additionalProperties": False,
            },
        },
    },
)

print(response)

視覺​

圖片輸入支援於 prism/deepseek-v4.1-flash。

Prism Image Input
import os
from litellm import completion

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What colour fills this image?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.png"}},
        ],
    }],
)

print(response)

Responses API​

Prism 原生提供 OpenAI Responses API，因此 litellm.responses 會將請求直接送至 https://api.prisminference.com/v1/responses。

Prism Responses API
import os
import litellm

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

response = litellm.responses(
    model="prism/deepseek-v4.1-flash",
    input="Say hello",
)

print(response.output_text)

Anthropic Messages API​

Prism 也原生提供 Anthropic Messages API，因此 litellm.anthropic.messages.acreate 會將請求直接送至 https://api.prisminference.com/v1/messages。

Prism Anthropic Messages API
import asyncio
import os
import litellm

os.environ["PRISM_API_KEY"] = ""  # your Prism API key

async def main():
    response = await litellm.anthropic.messages.acreate(
        model="prism/deepseek-v4.1-flash",
        messages=[{"role": "user", "content": "Say hello"}],
        max_tokens=64,
    )
    print(response["content"][0]["text"])

asyncio.run(main())

用法 - LiteLLM Proxy Server​

config.yaml
model_list:
  - model_name: deepseek-v4.1-flash
    litellm_params:
      model: prism/deepseek-v4.1-flash
      api_key: os.environ/PRISM_API_KEY
  - model_name: deepseek-v4-flash
    litellm_params:
      model: prism/deepseek-v4-flash
      api_key: os.environ/PRISM_API_KEY

以這種方式設定的部署會在 proxy 上提供全部三個端點：/v1/chat/completions、/v1/responses 與 /v1/messages。

curl
curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4.1-flash",
    "messages": [{"role": "user", "content": "Hello, how are you?"}]
  }'

自訂 API Base​

選項 1：環境變數

Custom API Base via env var
import os
from litellm import completion

os.environ["PRISM_API_BASE"] = "https://custom.prisminference.example/v1"
os.environ["PRISM_API_KEY"] = ""  # your API key

response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=[{"content": "Hello!", "role": "user"}],
)

選項 2：直接傳入

Custom API Base via parameter
from litellm import completion

response = completion(
    model="prism/deepseek-v4.1-flash",
    messages=[{"content": "Hello!", "role": "user"}],
    api_base="https://custom.prisminference.example/v1",
    api_key="your-api-key",
)

支援的 OpenAI 參數​

  • temperature
  • max_tokens
  • max_completion_tokens
  • top_p
  • frequency_penalty
  • presence_penalty
  • stop
  • n
  • stream
  • stream_options
  • tools
  • tool_choice
  • response_format
  • seed
  • logprobs
  • top_logprobs
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