mistralai/mistral-nemo
Release date: 2024-07-19
131,072 context · $0.15/M input tokens · $0.15/M output tokens
A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...
Pricing
Pay-per-use
No upfront costs, pay only for what you use
Try the model
API Usage
Use the following code examples to integrate with our 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: 0,
});
try {
const response = await client.chat.completions.create({
model: 'mistralai/mistral-nemo',
messages: [{ role: 'user', content: 'Hello!' }],
});
const body = response as typeof response & { error?: { message?: string } };
if (body.error) throw new Error(body.error.message || 'LLM generation failed');
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: 0,
});
try {
const response = await client.chat.completions.create({
model: 'mistralai/mistral-nemo',
messages: [{ role: 'user', content: 'Hello!' }],
});
const body = response as typeof response & { error?: { message?: string } };
if (body.error) throw new Error(body.error.message || 'LLM generation failed');
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}Model Introduction
Mistral: Mistral Nemo
A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...
This model is available through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.
Why It Looks Great
- text->text architecture for Text to Text workloads
- 131072 context window for long prompts, document analysis, and multi-turn workflows
- Function calling and tool-use support for agentic application workflows
- Structured output support for JSON responses and schema-constrained generation
Key Features
- Context Window: 131072 tokens
- Max Input: 114688 tokens
- Max Output: 16384 tokens
- Vision: Not listed
- Function Calling: Supported
- Structured Outputs: Supported
- Image Generation: Not listed
- Audio Input: Not listed
- Supported Parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Specifications
| Specification | Value |
|---|---|
| Provider | mistral |
| Model Type | Chat Completions model |
| Architecture | text->text |
| Context Window | 131072 tokens |
| Max Input | 114688 tokens |
| Max Output | 16384 tokens |
| Input | Text |
| Output | Text |
| Vision | Not listed |
| Function Calling | Supported |
| Structured Outputs | Supported |
| Release Date | July 19, 2024 |
How to Use
- Write your prompt - describe the task, provide context, and specify the 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-nemo
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-nemo",
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-nemo",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: mistralai/mistral-nemo
- Provider: mistral
Info
Supported Functionality
API Access Guide
Mistral Nemo API
mistralai/mistral-nemo
A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...
Input
$0.15 /M
Output
$0.15 /M
Context
131K
Max Output
16K
Tool Use
Supported
Try Mistral Nemo on WaveSpeedAI
Access Mistral Nemo through our unified API — OpenAI-compatible, no cold starts, transparent pricing.
Frequently Asked Questions about Mistral Nemo
How much does Mistral Nemo cost via the API?+
Pricing on WaveSpeedAI: $0.15 per million input tokens and $0.15 per million output tokens. Prompt caching and batch processing are billed separately and reduce effective cost on long, repetitive workloads.
What is the context window of Mistral Nemo?+
Mistral Nemo supports up to 131K tokens of context with up to 16K tokens of output per request.
Is Mistral Nemo OpenAI-compatible?+
WaveSpeedAI exposes Mistral Nemo at https://llm.wavespeed.ai/v1 through the OpenAI-compatible Chat Completions interface. Most OpenAI SDK clients work by changing the base URL and API key; optional fields depend on the selected model.
How do I get started with Mistral Nemo?+
Sign in to WaveSpeedAI, create an API key in Access Keys, then send a request to https://llm.wavespeed.ai/v1/chat/completions with the model id shown above. Check the current model catalog for availability, capabilities, and pricing.