Mistral Small 24b Instruct 2501 | Mistral AI LLM API

Name: Mistral Small 24b Instruct 2501 API
Brand: mistralai
Price: 0.05 USD
Availability: InStock

mistralai/mistral-small-24b-instruct-2501

發布時間: 2025-01-31

32,768 context · $0.05/M input tokens · $0.08/M output tokens

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...

定價

按用量付費

無需預付費用，僅按實際使用量付費

輸入$0.05 / M Tokens

輸出$0.08 / M Tokens

試用模型

mistralai/mistral-small-24b-instruct-2501

線上

嗨！我是樂於助人的 AI 助理。有什麼可以幫你的嗎？

準備在本機編碼 Agent 中使用這個模型嗎？Agent 設定

API 使用

使用以下程式碼範例整合我們的 API：

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 2,
});

try {
  const response = await client.chat.completions.create({
    model: 'mistralai/mistral-small-24b-instruct-2501',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 2,
});

try {
  const response = await client.chat.completions.create({
    model: 'mistralai/mistral-small-24b-instruct-2501',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

模型介紹

Mistralai mistral-small-24b-instruct-2501

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks

The model achieves 81% accuracy on the MMLU benchmark and performs competitively with larger models like Llama 3.3 70B and Qwen 32B, while operating at three times the speed on equivalent hardware. Read the blog post about the model here.

Why It Looks Great

Large Language Model architecture for efficient processing
32768 context window for long document handling
Competitive pricing at $0.0/$0.1 per million tokens

Key Features

Context Window: 32768 tokens
Max Output: 16384 tokens
Vision: Supported
Function Calling: Supported

Specifications

Specification	Value
Provider	Mistralai
Model Type	Large Language Model (LLM)
Architecture	N/A
Context Window	32768 tokens
Max Output	16384 tokens
Input	Text
Output	Text
Vision	Supported
Function Calling	Supported

Pricing

Token Type	Cost per Million Tokens
Input	$0.0
Output	$0.1

How to Use

Write your prompt — describe the task, provide context, and specify desired output format.
Submit — the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: mistralai/mistral-small-24b-instruct-2501

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="mistralai/mistral-small-24b-instruct-2501",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "mistralai/mistral-small-24b-instruct-2501",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: mistralai/mistral-small-24b-instruct-2501
Provider: Mistralai

資訊

提供商mistral

類型llm

支援功能

輸入

文字

輸出

文字

上下文32,768

最大輸出16,384

視覺-

函式呼叫✓ 支援

API 存取指南

Base URLhttps://llm.wavespeed.ai/v1

API 端點chat/completions

Model IDmistralai/mistral-small-24b-instruct-2501

關於 Mistral Small 24b Instruct 2501 的常見問題

Mistral Small 24b Instruct 2501 API 多少錢?+

WaveSpeedAI 定價:輸入每百萬 token $0.05,輸出每百萬 token $0.08。Prompt 快取與批次處理分別計費,可顯著降低長上下文、高重複任務的實際成本。

Mistral Small 24b Instruct 2501 的上下文視窗有多大?+

Mistral Small 24b Instruct 2501 每次請求最多支援 33K 上下文 token,輸出最多 16K token。

Mistral Small 24b Instruct 2501 是否相容 OpenAI?+

WaveSpeedAI 透過 https://llm.wavespeed.ai/v1 的 OpenAI 相容 Chat Completions 介面提供 Mistral Small 24b Instruct 2501。大多數 OpenAI SDK 用戶端只需更換 base URL 和 API Key；選用欄位取決於具體模型。

如何開始使用 Mistral Small 24b Instruct 2501?+

登入 WaveSpeedAI，在 Access Keys 中建立 API Key，然後使用上方顯示的 model id 向 https://llm.wavespeed.ai/v1/chat/completions 發送請求。模型可用性、能力和價格請以目前模型目錄為準。

定價