mistralai/mistral-small-24b-instruct-2501
Date de publication: 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...
Tarification
Paiement à l'usage
Aucun coût initial, payez uniquement ce que vous utilisez
Essayer le modèle
Utilisation de l'API
Utilisez les exemples de code suivants pour intégrer notre 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;
}Introduction au modèle
Mistralai mistral-small-24b-instruct-2501
Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks
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 for efficient local deployment.
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
Infos
Fonctionnalités prises en charge
Guide d'accès API
Mistral Small 24b Instruct 2501 API
mistralai/mistral-small-24b-instruct-2501
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...
Entrée
$0.05 /M
Sortie
$0.08 /M
Contexte
33K
Sortie max.
16K
Utilisation d'outils
Pris en charge
Essayez Mistral Small 24b Instruct 2501 sur WaveSpeedAI
Accédez à Mistral Small 24b Instruct 2501 via notre API unifiée — compatible OpenAI, sans démarrages à froid, prix transparents.
Questions fréquentes sur Mistral Small 24b Instruct 2501
Combien coûte l'API Mistral Small 24b Instruct 2501 ?+
Tarification sur WaveSpeedAI : $0.05 par million de tokens d'entrée et $0.08 par million de tokens de sortie. Le prompt caching et le traitement par batch sont facturés séparément et réduisent le coût effectif sur les charges longues et répétitives.
Quelle est la fenêtre de contexte de Mistral Small 24b Instruct 2501 ?+
Mistral Small 24b Instruct 2501 prend en charge jusqu'à 33K tokens de contexte et jusqu'à 16K tokens de sortie par requête.
Mistral Small 24b Instruct 2501 est-il compatible avec OpenAI ?+
WaveSpeedAI expose Mistral Small 24b Instruct 2501 à https://llm.wavespeed.ai/v1 via l’interface Chat Completions compatible OpenAI. La plupart des clients du SDK OpenAI fonctionnent en changeant l’URL de base et la clé API ; les champs facultatifs dépendent du modèle.
Comment démarrer avec Mistral Small 24b Instruct 2501 ?+
Connectez-vous à WaveSpeedAI, créez une clé API dans Access Keys, puis envoyez une requête à https://llm.wavespeed.ai/v1/chat/completions avec l’id du modèle affiché ci-dessus. Consultez le catalogue actuel pour la disponibilité, les capacités et les tarifs.