Hermes 3 70B Instruct

Overview

Hermes 3 70B Instruct is a text-only model from Nous in the nousresearch family. Released on 2024-08-18, it has open weights and is identified on Hugging Face as NousResearch/Hermes-3-Llama-3.1-70B. Its context window is 131,072 tokens, with a maximum output of 16,384 tokens.

Nous describes Hermes 3 as a general-purpose language model and positions it as an advance over Hermes 2. The stated improvements span agentic capabilities, roleplay, reasoning, multi-turn conversation and coherence over long contexts. Those areas make it relevant to teams evaluating a model for varied text-based tasks, though the available specifications do not quantify the improvements.

For stack decisions, the clearest fit signals are its open weights, text input and output, and long context window. The listing gives token pricing but does not establish deployment requirements or provide performance benchmarks.

Key features

• Open weights: Weights are listed as open, with the Hugging Face identifier NousResearch/Hermes-3-Llama-3.1-70B.
• Long context: The 131,072-token context window is suited to workflows that need to provide substantial text context in one interaction.
• Text in, text out: Both the listed input and output modality are text.
• Output limit: A response can contain up to 16,384 tokens according to the listing.
• Generalist focus: Nous highlights reasoning, roleplay, agentic capabilities, multi-turn conversation and long-context coherence as areas of improvement.

Pricing

Listed pricing is $0.7 USD per 1M input tokens and $0.7 USD per 1M output tokens. Input and output are priced at the same rate. The supplied pricing details do not specify other charges or hosting terms.

Platforms

The model listing identifies Hermes 3 70B Instruct by the model ID nousresearch/hermes-3-llama-3.1-70b and links it to an OpenRouter page. Its open-weight status and Hugging Face ID are useful identifiers for teams assessing integration options, but the available details do not name supported SDKs, deployment platforms or hardware requirements.

Who it's for

Hermes 3 70B Instruct is worth considering for teams looking for an open-weight, text-focused generalist with a large context window and straightforward per-token pricing. Its stated focus areas may suit applications involving extended conversation, roleplay or reasoning, as well as agentic workflows. Teams should treat those as positioning rather than quantified performance claims and verify that the model's operational requirements fit their environment.

Builders comparing Nous releases can also review Nous models. Teams weighing openness, cost or context size across a wider field can browse open-weight models, cheapest language models and longest-context models.

Pros and cons

Pros

• Open weights and a supplied Hugging Face identifier provide useful starting points for technical evaluation.
• A 131,072-token context window and 16,384-token maximum output support substantial text exchanges.
• Input and output rates are equal at $0.7 USD per 1M tokens each.
• The model is presented as a generalist rather than for a single narrow text task.

Cons

• The available facts provide no benchmark results or quantified evidence for the claimed improvements.
• Deployment requirements, supported platforms and additional hosting costs are not specified.
• Text-only input and output exclude non-text modalities from the listed capabilities.

Alternatives

Within the Hermes line, Hermes 3 405B Instruct is another listed model to compare, while Hermes 4 405B offers a different release in the family. The supplied facts do not include specifications for those alternatives, so compare their listings directly rather than assuming differences in capability, context or cost.

For a broader view of recent additions, browse newest models.

Verdict

Hermes 3 70B Instruct has a clear profile: an open-weight, text-only generalist from Nous, a 131,072-token context window, a 16,384-token output ceiling and equal input and output pricing of $0.7 USD per 1M tokens. The listed focus on reasoning, roleplay, agentic work and long conversations gives teams several plausible use cases to evaluate. The missing details on benchmarks and deployment mean it is best approached as a candidate for stack-level assessment, not as a model whose relative performance is established by these specifications alone.