Lightning-Fast AI Assistants at OpenRouter
Introduction
Artificial Intelligence (AI) has made tremendous progress in recent years, particularly in the field of Large Language Models (LLMs). These models are designed to process and understand human language, enabling applications such as chatbots, content generation, and language translation. At their core, LLMs work by predicting the next word in a sequence, given the context of the conversation or text. This prediction is based on complex algorithms and massive amounts of training data.
Speed Matters: Fast AI Assistants for Real-Time Applications
When it comes to selecting an LLM for a specific task, speed is a critical factor to consider. Faster models enable real-time applications, such as chatbots, virtual assistants, and content generation. In this article, we'll explore the fastest AI options available at OpenRouter, with speeds over 2000 tokens per second.
Lightning-Fast LLMs: Unlocking New Possibilities
The emergence of lightning-fast LLMs has opened up new possibilities for AI applications. With speeds of over 2000 tokens per second, these models can handle demanding tasks, such as real-time language translation, sentiment analysis, and text summarization. In this article, we'll delve into the world of fast AI assistants and explore the benefits and possibilities they offer.
These are the Fastest LLM's you can get at OpenRouter.
| Index | Release Date | Company | Model | Size (B) | Engine | Context Size (K) | Max Output (K) | Throughput (tps) | Latency (s) | Context Cost (USD/1M tokens) | Output Cost (USD/1M tokens) |
Selection
When it comes to selecting a fast LLM for a specific task, there are several factors to consider. One of the most important is speed, as it directly impacts the user experience. With speeds ranging from 2000 to 3000 tokens per second, users can engage in real-time conversations, generate content instantly, and enjoy seamless interactions with AI assistants.
Size and Speed: Finding the Perfect Balance
The size of the model, measured in billions of parameters, also matters. Larger models tend to be more accurate and capable, but may require more computational resources. A model with 10 billion parameters may be sufficient for simple tasks, while a model with 400 billion parameters may be needed for more complex applications. However, with the emergence of lightning-fast LLMs, the focus has shifted from size to speed. If speed is not a problem and you want to save some pennies checkout my article: Medium-to-Large AI's, Mixture-of-Experts and Even 'Thinking' Models for Free. Yeah!
Methodology for Picking a Fast Model
- Define the task: Identify the specific application or use case for the LLM.
- Determine the required speed: Consider the desired response time and throughput.
- Choose a model size: Select a model with a suitable number of parameters for the task complexity.
- Evaluate the model's capabilities: Consider the model's performance on relevant benchmarks and tasks.
Editor's Pick
After evaluating several LLMs, we recommend the following two models:
R1 Distill Llama 3.3 70B Model from DeepSeek
- 70B
- 2303tps
- thinking
- multi-step
Llama 4 Scout Model from Meta
- 109B
- 2067tps
- accuracy
- expertise
Both models offer exceptional performance, making them attractive options for a wide range of applications. Whether you need a fast LLM for real-time applications or a highly accurate model for demanding tasks, these two models are definitely worth considering.