🔥 Qwen/Qwen3-32B
High-performance 32B parameter LLM. Excellent for reasoning, coding, and multilingual tasks.
Input: $0.15/M tokensOutput: $0.44/M tokens
33.54M runs in 7 days
Mistral's pioneering MoE model matching GPT-3.5 performance at a fraction of the cost.
Parameters
46.7B (MoE, 12.9B active)
Context
32,768 tokens
Organization
Mistral AI
Start using Mixtral 8x7B in minutes. VoltageGPU provides an OpenAI-compatible API — just change the base_url.
from openai import OpenAI
client = OpenAI(
base_url="https://api.voltagegpu.com/v1",
api_key="YOUR_VOLTAGE_API_KEY"
)
response = client.chat.completions.create(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=[
{"role": "user", "content": "Write a concise summary of the key benefits of microservices architecture."}
],
max_tokens=1024,
temperature=0.5
)
print(response.choices[0].message.content)curl -X POST https://api.voltagegpu.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_VOLTAGE_API_KEY" \
-d '{
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"messages": [
{"role": "user", "content": "Write a concise summary of the key benefits of microservices architecture."}
],
"max_tokens": 1024,
"temperature": 0.5
}'| Component | Price | Unit |
|---|---|---|
| Input tokens | $0.24 | per 1M tokens |
| Output tokens | $0.24 | per 1M tokens |
New accounts receive $5 free credit. No credit card required to start.
Mixtral 8x7B matches or exceeds GPT-3.5 Turbo on most benchmarks: MMLU (70.6%), HellaSwag (84.4%), and ARC (66.4%). It handles English, French, Italian, German, and Spanish natively. The MoE architecture provides 6x faster inference than a comparable dense 70B model while maintaining similar quality. It supports instruction following, text generation, summarization, and basic reasoning.
Mixtral 8x7B is Mistral AI's groundbreaking Mixture-of-Experts model that revolutionized the open-source LLM landscape. With 46.7 billion total parameters but only 12.9 billion active per forward pass, it delivers performance matching Llama 2 70B and GPT-3.5 Turbo while being significantly faster and more cost-effective. The model uses 8 expert networks and a router that selects 2 experts per token, enabling efficient specialization across different types of knowledge and tasks.
Build production chatbots with GPT-3.5-level quality at significantly lower cost.
Generate blog posts, marketing copy, and creative writing in multiple European languages.
Serve real-time applications that need quick responses with high throughput.
Summarize articles, reports, and documents efficiently at scale.
Translate between English, French, German, Italian, and Spanish.
https://api.voltagegpu.com/v1/chat/completions| Authorization | Bearer YOUR_VOLTAGE_API_KEY | Required |
| Content-Type | application/json | Required |
mistralai/Mixtral-8x7B-Instruct-v0.1Use this value as the model parameter in your API requests.
curl -X POST https://api.voltagegpu.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_VOLTAGE_API_KEY" \
-d '{
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"messages": [
{"role": "user", "content": "Write a concise summary of the key benefits of microservices architecture."}
],
"max_tokens": 1024,
"temperature": 0.5
}'Great price-performance for smaller models with 24GB VRAM.
Enterprise-grade GPU for production inference at scale.
Access this model and 140+ others through our OpenAI-compatible API.
Compare GPU cloud pricing and model hosting features.
View GPU compute and AI inference pricing with no hidden fees.
Deploy a GPU pod in under 60 seconds to run models locally.
Mixtral 8x7B matches or exceeds GPT-3.5 Turbo on most benchmarks while being open source and cheaper. At $0.24/M tokens vs GPT-3.5's $0.50/M, it offers excellent value. It particularly excels at European language tasks and code generation.
MoE (Mixture-of-Experts) routes each token to only 2 of the 8 expert networks, meaning only 12.9B of the 46.7B parameters are active per token. This gives the model the knowledge capacity of a large model with the speed of a small one.
While newer models have surpassed Mixtral 8x7B on some benchmarks, it remains an excellent choice for production workloads where cost and speed are priorities. Its proven reliability and the efficiency of its MoE architecture make it a strong option for many use cases.
Mixtral 8x7B supports a 32,768 token context window, sufficient for most production use cases including multi-turn conversations, document summarization, and code generation.
Get $5 free credit when you sign up. No credit card required. Deploy in under 30 seconds with our OpenAI-compatible API.