An open-source model from Nous Research, built on Llama-3.1, offering a huge 128k context window with trade-offs in speed and general intelligence.
Hermes 3 - Llama-3.1 70B is a new large language model from the prolific open-source AI lab, Nous Research. Built upon Meta's recently released Llama-3.1 70B foundation, this model inherits one of the most talked-about features in the industry: a massive 128,000-token context window. This positions Hermes 3 not as a general-purpose chatbot competitor, but as a specialized tool designed for heavy-duty text processing. With an open license and a knowledge cutoff of November 2023, it represents an accessible option for developers looking to build applications that can ingest and analyze entire books, extensive legal documents, or vast codebases in a single pass.
However, a deep dive into its performance metrics reveals a clear set of trade-offs. On the Artificial Analysis Intelligence Index, Hermes 3 scores a 15, which is notably below the average of 22 for comparable open-weight models. This suggests that while it can handle vast amounts of information, its ability to perform complex reasoning, follow nuanced instructions, or engage in sophisticated creative tasks may be limited compared to its peers. It's a workhorse for data processing, not a philosopher for abstract thought.
The performance profile is further defined by its speed and pricing. With an output speed of approximately 38 tokens per second, it is significantly slower than the class average of 60 tokens/s. This can make real-time generation of long responses feel sluggish. In contrast, its latency, or time-to-first-token (TTFT), is an excellent 0.30 seconds, meaning it begins responding almost instantly. This creates a user experience of immediate feedback, even if the full answer takes time to generate. This combination is ideal for streaming interfaces where the user sees words appear one by one.
Pricing on the benchmarked provider, Deepinfra, is symmetric at $0.30 per 1 million tokens for both input and output. This is a crucial detail. While the input price is somewhat expensive compared to the class average of $0.20, the output price is a bargain, coming in well under the average of $0.54. This pricing structure makes Hermes 3 particularly cost-effective for applications that generate large amounts of text from relatively short prompts, such as content creation or detailed summarization. Conversely, it can become costly for input-heavy tasks like Retrieval-Augmented Generation (RAG) where the prompt is filled with extensive context documents.
Ultimately, Hermes 3 - Llama-3.1 70B is a model for developers with a specific need. If your application's primary bottleneck is the inability to process more than a few thousand tokens of context at a time, Hermes 3 is a compelling solution. It unlocks new possibilities for deep document analysis and long-form conversational memory. However, for those seeking the highest intelligence, fastest generation speeds, or the lowest cost for input-heavy workloads, other models may be a better fit. It is a specialist tool that excels in its niche but does not attempt to be the master of all trades.
15 (21 / 33)
38 tokens/s
0.30 $/1M tokens
0.30 $/1M tokens
N/A
0.30 seconds
| Spec | Details |
|---|---|
| Model Name | Hermes 3 - Llama-3.1 70B |
| Owner / Developer | Nous Research |
| Base Model | Meta Llama-3.1 70B |
| Parameters | ~70 Billion |
| Context Window | 128,000 tokens |
| License | Llama 3.1 Community License |
| Knowledge Cutoff | November 2023 |
| Architecture | Transformer with Grouped-Query Attention (GQA) |
| Input Modalities | Text |
| Output Modalities | Text |
| Primary Use Case | Large-context document analysis, RAG, summarization |
Currently, Deepinfra is the primary provider benchmarked for Hermes 3 - Llama-3.1 70B. This centralizes the decision-making process but makes it crucial to understand how to best leverage the model within this specific provider's ecosystem. Our picks are framed around matching a user's priority to the model's distinct performance characteristics on this platform.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Balanced Use | Deepinfra | As the sole benchmarked provider, it offers a well-rounded profile with excellent latency and competitive output pricing, making it the default choice. | There is no alternative provider for comparison, meaning users are tied to Deepinfra's specific performance and pricing implementation. |
| Cost-Effective Output | Deepinfra | The $0.30/1M output token price is a significant discount compared to the class average ($0.54), making it ideal for summarization or content generation. | The slow generation speed of 38 tokens/s may offset some cost benefits in time-sensitive applications where speed is paramount. |
| Large Context Tasks | Deepinfra | Provides full access to the 128k context window, enabling powerful RAG and document analysis workflows that are impossible on smaller-context models. | Input costs for filling the large context can be substantial and must be managed carefully to avoid budget overruns. |
| Low Latency Apps | Deepinfra | Delivers an impressive 0.30s time-to-first-token, ensuring a responsive feel for interactive applications like chatbots. | The low initial latency is immediately followed by a slow token generation rate, which may not be suitable for use cases needing a fast full response. |
Provider analysis is based on data from a single benchmarked API provider (Deepinfra). Performance and pricing may vary as more providers offer this model. All prices are in USD per 1 million tokens. Blended price assumes a 3:1 output-to-input token ratio.
To understand the practical cost implications of Hermes 3, let's examine a few real-world scenarios. These examples highlight how the model's symmetric pricing and large context window affect costs for different tasks. All estimates are based on Deepinfra's pricing of $0.30 per 1 million tokens for both input and output.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Email Summarization | 1,000 tokens | 150 tokens | A common business task of summarizing a long email thread to extract key points. | $0.00035 |
| Blog Post Generation | 100 tokens | 2,000 tokens | Generating a draft article from a brief prompt, an output-heavy task. | $0.00063 |
| RAG over a Document | 10,000 tokens | 500 tokens | Answering a question using a provided 10k-token document as context. | $0.00315 |
| Code Generation & Explanation | 300 tokens | 1,200 tokens | Creating a functional code snippet and explaining how it works in detail. | $0.00045 |
| Large Document Analysis | 100,000 tokens | 1,000 tokens | Finding key insights from a very large report, leveraging the massive context window. | $0.03030 |
| Multi-turn Chat Session | 2,500 tokens | 2,500 tokens | A balanced conversation with history included in the context. | $0.00150 |
The key takeaway is that Hermes 3's costs are highly sensitive to input size. While output-heavy tasks like blog post generation are very affordable, workloads that leverage its large context window for RAG or analysis can become significantly more expensive, with a single query costing several cents. Careful prompt management is essential for cost control.
The model's greatest cost advantage is its low output price. Since output tokens cost the same as input tokens ($0.30/1M) and are much cheaper than the average model's output price ($0.54/1M), you should prioritize it for tasks that generate significantly more text than they consume.
While the 128k context window is a powerful feature, filling it is not cheap. A prompt with 100,000 input tokens costs $0.03. To mitigate this, use context strategically rather than universally.
Hermes 3 is fast to start (0.30s TTFT) but slow to generate (38 t/s). This profile is perfect for a streaming user interface, which improves perceived performance.
Given its below-average intelligence score, you should avoid using Hermes 3 for tasks that require high-fidelity, multi-step reasoning or creative problem-solving. Its strength lies in processing and restructuring information that is already present in the context.
Hermes 3 - Llama-3.1 70B is a large language model developed by Nous Research. It is a fine-tuned version of Meta's Llama-3.1 70B model and is distinguished by its massive 128,000-token context window and its open-source-friendly license.
Its primary advantage is the 128,000-token context window. This allows it to process and analyze extremely large documents, long conversation histories, or entire codebases in a single prompt, which is a capability few other models possess.
It presents a mixed performance profile. It has below-average intelligence for complex reasoning and a slower-than-average generation speed. However, it excels with a very low time-to-first-token (high responsiveness) and offers a very competitive price for output tokens.
It depends on the task. Its input token price ($0.30/1M) is slightly above average, making input-heavy tasks (like RAG with a full context) costly. However, its output token price ($0.30/1M) is well below average, making it very cost-effective for tasks that generate a lot of text from short prompts.
Hermes 3 is a fine-tuned version of the base Llama-3.1 70B model. Nous Research has trained it on a specific dataset to enhance certain capabilities, typically related to instruction following or conversational ability. While it inherits core features like the 128k context window from the base model, its 'personality' and specific task performance are shaped by this additional training.
This model is ideal for developers and businesses building applications that need to process long texts. Key use cases include summarizing legal documents, analyzing research papers, or implementing Retrieval-Augmented Generation (RAG) over extensive private knowledge bases. It is less suited for applications demanding top-tier reasoning, high creativity, or the fastest possible generation of long responses.