Mistral: Mistral Nemo
Reason 128k Tokens Fast
Deploy Nemo Capabilities
128k Context
Process Long Inputs
Handle complex documents and multi-turn conversations with 128k token window.
State-of-Art Reasoning
Excel Coding Math
Lead in reasoning, world knowledge, and code accuracy for 12B models.
FP8 Optimized
Run Efficient Inference
Use quantization-aware training for FP8 without performance loss on any hardware.
Examples
See what Mistral: Mistral Nemo can create
Copy any prompt below and try it yourself in the playground.
Code Refactor
“Refactor this Python function to use list comprehensions and improve efficiency: def process_data(data): result = []; for item in data: if item > 0: result.append(item * 2); return result”
Math Proof
“Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction. Provide step-by-step reasoning.”
Summary Task
“Summarize key advancements in transformer models from 2017 to 2024, focusing on attention mechanisms and efficiency gains.”
Multilingual Query
“Traduisez cette phrase en français, espagnol et allemand: 'AI models like Mistral Nemo enable efficient multilingual processing.' Explain tokenizer efficiency.”
For Developers
A few lines of code.
Nemo inference. Two lines.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Mistral: Mistral Nemo on ModelsLab.