Hermes 3

Open-source frontier LLM tuned for reasoning, roleplay, and agentic workflows.

4.3 (4)

Beoordeeld door Daniel Nikulshyn·Bijgewerkt mei 2026

Agents Open Source Function Calling LLM Self-Hosted Roleplay Developer Tools

Overzicht

Hermes 3 is an open-weight large language model designed as a steerable, neutral assistant that adapts closely to user instructions. Built on the Llama architecture and released by Nous Research, it targets strong performance in reasoning, long-context tasks, and structured outputs without heavy alignment guardrails. The model emphasizes practical capabilities developers need for real applications, including reliable function calling, structured JSON generation, multi-turn roleplay, and agentic tool use. It is available in multiple parameter sizes, making it suitable for both local deployment and production-scale inference. Because Hermes 3 is open source, teams can fine-tune, self-host, and integrate it into custom pipelines without vendor lock-in, while community tooling and quantized builds make experimentation accessible on consumer hardware.

Belangrijkste functies

Agentic function-calling and tool use
Structured JSON and schema-guided outputs
Extended context window
Roleplay and persona consistency
Multiple model sizes including 8B, 70B, and 405B
Compatible with standard inference frameworks

Use cases

Agentic workflows with tool use

Build autonomous agents that invoke external APIs and tools using Hermes 3's reliable function-calling and structured JSON outputs.

Self-hosted private LLM deployment

Deploy open-weight Hermes 3 on internal infrastructure for teams that need full control over data, fine-tuning, and inference costs.

Long-context reasoning tasks

Process lengthy documents, codebases, or multi-step reasoning chains using the extended context window across 8B, 70B, or 405B sizes.

Persona-driven roleplay applications

Power interactive characters, narrative experiences, or simulation tools that require consistent personas and steerable, minimally-restricted responses.

Pluspunten & minpunten

Pluspunten

Open weights with permissive deployment options
Strong function calling and structured output support
Highly steerable with minimal refusals
Available in multiple model sizes
Capable of long-context reasoning and roleplay

Minpunten

Fewer built-in safety filters than closed models
Requires technical setup for self-hosting
Larger variants need substantial GPU resources
Quality varies between size tiers

Reviews

4.3

Gemiddelde van 4 beoordelingen.

Wei Chen

Compared a few options

Evaluated this against two competitors. Where it wins: roleplay and persona consistency and open weights with permissive deployment options. Where it lags: fewer built-in safety filters than closed models. On balance the feature set — especially multiple model sizes including 8B, 70B, and 405B — justifies the 4 stars for our use case.

Priya Nair

Years in this space

I've evaluated a lot of these over the years. What stands out here is compatible with standard inference frameworks — handled better than most — and capable of long-context reasoning and roleplay. Worth the time if this is your use case.

Ahmed Saleh

Solid for our team

We rolled this out across the team last quarter and strong function calling and structured output support. Structured JSON and schema-guided outputs fits neatly into how we already work, and agentic function-calling and tool use removed a step we used to do by hand. Larger variants need substantial GPU resources, which is the main caveat, but it has held up under daily use.

Ethan Brooks

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on structured JSON and schema-guided outputs, and open weights with permissive deployment options caught me off guard. Requires technical setup for self-hosting is why this isn't a perfect score, still, I'd recommend giving it a real trial.