Hermes 4 405B: The New Open-Source Reasoning Giant

Performance & Benchmarks

In terms of raw capability, Hermes 4 405B outperforms its predecessors on nearly all standard benchmarks. The model demonstrates a significant improvement in MMLU scores, achieving a pass rate of 88.5%, which places it among the top open-weight models globally. This score reflects its superior knowledge retention and reasoning abilities across diverse domains.

Coding and software engineering tasks are where Hermes 4 truly shines. On the HumanEval benchmark, the model scores 92%, indicating a high proficiency in generating functional code snippets. Furthermore, it shows dominance on RefusalBench, effectively handling edge cases and safety constraints without hallucinating harmful content.

SWE-bench results are particularly impressive, with Hermes 4 solving 45% of hard issues, surpassing the 40% threshold set by Llama 3.1 405B. This improvement is attributed to the enhanced reasoning style and better alignment with developer intent. The model also maintains high stability across different hardware configurations.

• MMLU: 88.5%
• HumanEval: 92%
• RefusalBench: Dominant
• SWE-bench: 45% Hard Issues

API Pricing & Availability

As an open-weight model, Hermes 4 405B is free to download and self-host. There are no direct API costs associated with running the model locally, as the licensing is open. However, for users who prefer managed inference via the Nous Portal, specific tiered pricing may apply depending on the compute resources required for the 405B parameter count.

Developers should note that while the model itself is free, the hardware requirements for inference are substantial. Running Hermes 4 405B efficiently typically requires at least 141.9GB of VRAM for the 70B variant, and significantly more for the 405B flagship. Cloud inference costs will vary based on the provider's GPU pricing.

NousResearch offers a free tier for smaller variants like Hermes 4 36B, which can be used to test the function calling capabilities before scaling to the 405B version. This tier allows developers to validate workflows without incurring immediate costs, making it an excellent entry point for experimentation.

• Model Cost: Free (Open-Weight)
• Hardware: High VRAM (405B)
• Portal: Free Tier Available for 36B

Comparison Table

To understand where Hermes 4 fits in the ecosystem, we compared it against other leading open-source models. The table below highlights the key differences in context window, output capabilities, and pricing structures.

While Llama 3.1 405B remains the standard for raw scale, Hermes 4 405B offers superior function calling and structured output features out of the box. Smaller models like Mixtral 8x22B remain viable for lightweight tasks but lack the reasoning depth of the 405B flagship.

• See table below for detailed specs.

Use Cases

Hermes 4 is ideally suited for enterprise applications requiring high reliability and structured data handling. Its advanced function calling makes it perfect for building autonomous agents that can manage complex workflows, such as automated customer support or internal IT ticketing systems.

In the realm of RAG (Retrieval-Augmented Generation), the 131K context window allows the model to process vast documentation sets without truncation. This is invaluable for legal tech, medical research, and financial analysis where context integrity is non-negotiable.

Software development teams can leverage Hermes 4 for code generation, debugging, and refactoring tasks. The high HumanEval score suggests that integrating this model into IDE plugins could significantly boost developer productivity by reducing boilerplate code generation errors.

• Autonomous Agents
• Enterprise RAG Systems
• Code Generation & Debugging
• Long-Document Analysis

Getting Started

Accessing Hermes 4 405B is straightforward for users with the necessary hardware. The model is available on Hugging Face under the NousResearch namespace. Users can clone the repository and follow the provided scripts to load the weights into their preferred inference engine, such as vLLM or Ollama.

For those without local GPU clusters, NousResearch provides a portal for managed inference. The Hermes Agent framework v0.8.0 also offers SDKs for seamless integration into existing Python workflows. Documentation is hosted on the official GitHub repository, including guides on function calling implementation.

Start by cloning the repository and verifying the environment. Ensure your CUDA drivers are up to date for optimal performance. The technical report provides detailed instructions on quantization strategies to reduce VRAM usage while maintaining accuracy.

• Platform: Hugging Face
• Framework: Hermes Agent v0.8.0
• Engine: vLLM, Ollama

Comparison

API Pricing — Context: 131K

Sources