Nous: Hermes 4 70B

Text input Text output
Author's Description

Hermes 4 70B is a hybrid reasoning model from Nous Research, built on Meta-Llama-3.1-70B. It introduces the same hybrid mode as the larger 405B release, allowing the model to either respond directly or generate explicit <think>...</think> reasoning traces before answering. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) This 70B variant is trained with the expanded post-training corpus (~60B tokens) emphasizing verified reasoning data, leading to improvements in mathematics, coding, STEM, logic, and structured outputs while maintaining general assistant performance. It supports JSON mode, schema adherence, function calling, and tool use, and is designed for greater steerability with reduced refusal rates.

Key Specifications
Cost
$$
Context
131K
Parameters
70B
Released
Aug 26, 2025
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Include Reasoning Top Logprobs Stop Logprobs Max Tokens Top P Frequency Penalty Reasoning Logit Bias Seed Temperature Presence Penalty
Features

This model supports the following features:

Reasoning
Performance Summary

Nous: Hermes 4 70B, a hybrid reasoning model built on Meta-Llama-3.1-70B, demonstrates a balanced performance profile with notable strengths in reliability and cost-effectiveness. It typically performs in the top tier for speed (71st percentile) and offers competitive pricing (74th percentile). The model exhibits strong reliability with a 95% success rate across benchmarks, indicating consistent operational stability. In terms of specific capabilities, Hermes 4 70B excels in hallucination prevention (98.0% accuracy) and email classification (98.0% accuracy), showcasing its ability to handle uncertainty and categorize information effectively. Its performance in ethics (99.0% accuracy) is also very strong. While general knowledge is solid (96.5%), its accuracy in instruction following (55.1%), coding (55.0%), reasoning (54.0%), and mathematics (69.0%) suggests areas for potential improvement, particularly given its emphasis on verified reasoning data. The model's hybrid reasoning mode and support for structured outputs like JSON and function calling are key features designed to enhance steerability and reduce refusal rates.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.13
Completion $0.4

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
Nebius
Nebius | nousresearch/hermes-4-70b 131K $0.13 / 1M tokens $0.4 / 1M tokens
Chutes
Chutes | nousresearch/hermes-4-70b 131K $0.11 / 1M tokens $0.38 / 1M tokens
Chutes
Chutes | nousresearch/hermes-4-70b 131K $0.11 / 1M tokens $0.38 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Strategy Free Executions Accuracy Cost Duration
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