Hermes 4 405B (Reasoning)

Since Hermes 4 405B is inherently concise, leverage this trait. Design prompts that explicitly ask for brief, to-the-point answers, or specify maximum word/sentence counts. Avoid open-ended prompts that might encourage unnecessary verbosity.

• Use directives like "Summarize in 3 sentences."
• Specify output format to reduce extraneous text (e.g., JSON, bullet points).
• Pre-process inputs to remove redundant information before sending to the model.

The large context window is a strength, but using it indiscriminately will incur high input costs. Only include necessary information in the prompt. For iterative tasks, consider summarizing previous turns or using retrieval-augmented generation (RAG) to fetch only relevant snippets.

• Employ RAG to provide only highly relevant context, rather than entire documents.
• For long conversations, summarize past interactions to keep the active context smaller.
• Batch related queries to utilize the context window efficiently across multiple requests.

With output tokens being three times more expensive than input tokens, strict control over generated content is paramount. Implement post-processing to trim unnecessary text or set hard limits on output length at the application level.

• Set `max_tokens` parameters in your API calls to prevent overly long responses.
• Implement client-side truncation if the model exceeds desired length.
• Analyze common output patterns to identify and prune repetitive phrases or boilerplate.

While the model's speed is moderate, batching multiple independent requests can improve overall throughput and potentially reduce per-request overhead. This is particularly useful for offline processing or tasks where immediate real-time responses aren't critical.

• Group similar, non-urgent tasks together for a single API call if the provider supports it.
• Process large datasets in chunks, optimizing for the model's context window and speed.
• Schedule batch jobs during off-peak hours if pricing tiers vary.

For tasks that don't require Hermes 4 405B's full reasoning power or large context, consider using a smaller, more cost-effective model for initial drafts or simpler queries. Reserve Hermes 4 405B for the most complex, high-value reasoning tasks.

• Use a cheaper model for initial content generation, then refine with Hermes 4 405B.
• Employ smaller models for basic summarization or classification, saving Hermes 4 405B for deep analysis.
• Implement a routing layer that directs queries to the most appropriate (and cost-effective) model.

Hermes 4 405B is a large language model developed by Nous Research, based on the Llama-3.1 405B architecture. It is specifically designed for advanced reasoning tasks and features an extensive 128k token context window, making it suitable for processing and analyzing large volumes of text.

Hermes 4 405B scores 42 on the Artificial Analysis Intelligence Index, placing it below the average for comparable models in its class. However, it achieves this score with remarkable conciseness, generating significantly fewer tokens during evaluation, which suggests a different approach to its 'intelligence' output.

Hermes 4 405B is positioned at the higher end of the cost spectrum. With input tokens at $1.00 and output tokens at $3.00 per 1M, it is more expensive than the average. Its cost-effectiveness depends heavily on the specific use case and the ability to optimize token usage, especially for output generation.

Its strengths lie in complex reasoning, deep analysis, and tasks requiring a large context window. Ideal applications include detailed document summarization, advanced code generation, intricate problem-solving, and long-form content creation where quality and contextual understanding are paramount.

Hermes 4 405B boasts an impressive 128k token context window. This allows the model to process and maintain context over very long inputs, enabling it to handle extensive documents, multi-turn conversations, and complex data analysis without losing track of earlier information.

The model is owned by Nous Research. It operates under an open license, which provides users with significant flexibility for deployment, customization, and integration into various applications without restrictive proprietary constraints.

Being 'highly concise' means that Hermes 4 405B tends to generate fewer tokens to convey information, particularly during intelligence evaluations. While this can reduce output costs, it also implies that users might need to be more explicit in their prompts if they require detailed or verbose responses.

Hermes 4 405B has a median output speed of 37 tokens per second, which is slower than the average of 45 tokens/s for comparable models. This moderate speed means it might not be the fastest choice for applications requiring extremely rapid, high-volume text generation.