Hermes 4 70B, powered by Llama-3.1, delivers an exceptional blend of intelligence, speed, and affordability for non-reasoning tasks, positioning it as a strong contender in its class.
Hermes 4 70B, built upon the robust Llama-3.1 architecture, emerges as a highly competitive model for applications requiring efficient and intelligent text processing without complex reasoning. Benchmarked on Nebius (FP8), this model demonstrates a compelling balance across critical performance metrics: superior intelligence, impressive output speed, and attractive pricing. It's designed for developers and businesses seeking a powerful, open-weight solution that excels in generating concise and relevant text outputs, making it suitable for a wide array of generative AI tasks.
In terms of raw intelligence, Hermes 4 70B scores a notable 24 on the Artificial Analysis Intelligence Index, placing it at #14 out of 33 models evaluated. This score signifies that it performs above the average intelligence benchmark of 22 for comparable models. What's particularly impressive is its conciseness; during the Intelligence Index evaluation, it generated 6.0 million tokens, significantly less verbose than the average of 8.5 million tokens. This efficiency in output generation translates directly into lower operational costs and faster processing, as less data needs to be transmitted and consumed.
Speed is another area where Hermes 4 70B truly shines. With a median output speed of 76 tokens per second, it ranks #7 out of 33 models, comfortably surpassing the average speed of 60 tokens per second. This high throughput is crucial for real-time applications, interactive user experiences, and large-scale content generation where rapid response times are paramount. Coupled with a low latency of just 0.58 seconds to the first token, the model ensures a smooth and responsive user experience, minimizing wait times for initial output.
From a cost perspective, Hermes 4 70B offers a highly competitive pricing structure. Its input token price stands at $0.13 per 1 million tokens, which is moderately priced compared to the average of $0.20. The output token price is $0.40 per 1 million tokens, also moderately priced against an average of $0.54. This results in a blended price of $0.20 per 1 million tokens (based on a 3:1 input:output ratio), making it an economically viable choice for many applications. The total cost to evaluate Hermes 4 70B on the Intelligence Index was $8.73, further underscoring its cost-effectiveness for extensive use.
Overall, Hermes 4 70B presents a compelling package for developers. Its strong performance in intelligence, combined with its high speed and attractive pricing, positions it as a top-tier open-weight, non-reasoning model. Supporting text input and output with a generous 128k token context window, it offers flexibility and power for a diverse range of applications, from advanced summarization and content creation to efficient chatbot responses and data extraction.
24 (#14 / 33 / 3 out of 4 units)
76.4 tokens/s
$0.13 per 1M tokens
$0.40 per 1M tokens
6.0M Output tokens
0.58 seconds
| Spec | Details |
|---|---|
| Model Name | Hermes 4 - Llama-3.1 70B (Non-reasoning) |
| Model Size | 70 Billion Parameters |
| Owner | Nous Research |
| License | Open |
| Context Window | 128,000 tokens |
| Input Type | Text |
| Output Type | Text |
| Primary Provider | Nebius (FP8) |
| Intelligence Index Score | 24 |
| Output Speed (Median) | 76 tokens/second |
| Latency (TTFT) | 0.58 seconds |
| Input Token Price | $0.13 per 1M tokens |
| Output Token Price | $0.40 per 1M tokens |
| Blended Price (3:1) | $0.20 per 1M tokens |
Choosing the right provider for Hermes 4 70B is crucial for optimizing both performance and cost. While Nebius (FP8) is the benchmarked provider demonstrating excellent metrics, understanding your specific priorities can guide your decision.
Nebius offers a highly optimized environment for Hermes 4, delivering impressive speed and low latency. However, depending on your infrastructure, existing cloud relationships, or specific compliance needs, other providers might be considered, even if they require custom deployment or offer different performance profiles.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Priority | Pick | Why | Tradeoff |
| Balanced Performance & Cost | Nebius (FP8) | Benchmark shows excellent speed (76 t/s), low latency (0.58s), and competitive pricing ($0.20/M blended). | May require specific integration with Nebius ecosystem. |
| Maximum Control & Customization | Self-Hosted (On-Prem/Cloud) | Direct access to model weights, full control over infrastructure, security, and fine-tuning. | Significant operational overhead, higher initial setup costs, requires specialized ML engineering talent. |
| Existing Cloud Infrastructure | Major Cloud Provider (e.g., AWS, Azure, GCP) | Leverage existing cloud credits, infrastructure, and services. Deploy Hermes 4 on optimized instances. | Performance may vary based on instance type and optimization; potentially higher per-token cost than Nebius. |
| Developer Agility & Ease of Use | Managed LLM Platform (e.g., Hugging Face Inference Endpoints) | Simplified deployment, scaling, and API access without managing underlying infrastructure. | Less control over specific hardware optimizations, potentially higher per-token cost, limited customization. |
| Cost-Efficiency (Hypothetical) | Specialized Inference Provider (e.g., Anyscale, Together.ai) | Often offer highly optimized inference for open models at competitive rates. | Performance and pricing can fluctuate; may not offer the same level of support as larger cloud providers. |
Note: Performance and pricing for providers other than Nebius (FP8) are estimates based on general market offerings for open-weight models and would require independent benchmarking.
Understanding the real-world cost implications of Hermes 4 70B involves analyzing typical use cases and estimating token consumption. Given its high speed, conciseness, and competitive pricing, it's well-suited for a variety of generative AI tasks. The following scenarios illustrate potential costs based on the Nebius (FP8) pricing structure.
These estimates assume a 3:1 input-to-output token ratio for the blended price calculation, but individual scenarios will vary. The 128k context window allows for complex inputs, but careful prompt engineering can optimize token usage.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input | Output | What it represents | Estimated Cost |
| Summarizing a Long Document | 50,000 tokens (e.g., a research paper) | 1,000 tokens (e.g., executive summary) | Condensing extensive information into a concise overview. | $0.13 (input) + $0.0004 (output) = $6.53 |
| Generating Marketing Copy | 500 tokens (e.g., product brief) | 2,000 tokens (e.g., multiple ad variations) | Creating diverse content from a short prompt. | $0.000065 (input) + $0.0008 (output) = $0.000865 |
| Advanced Chatbot Interaction | 2,000 tokens (e.g., conversation history) | 500 tokens (e.g., detailed response) | Sustained, context-aware dialogue with users. | $0.00026 (input) + $0.0002 (output) = $0.00046 |
| Data Extraction & Structuring | 10,000 tokens (e.g., unstructured text) | 500 tokens (e.g., JSON output) | Parsing and formatting information from large text blocks. | $0.0013 (input) + $0.0002 (output) = $0.0015 |
| Code Generation (Small Function) | 1,000 tokens (e.g., requirements, existing code) | 500 tokens (e.g., Python function) | Assisting developers with boilerplate or small code snippets. | $0.00013 (input) + $0.0002 (output) = $0.00033 |
| Translating a Webpage | 20,000 tokens (e.g., full page content) | 20,000 tokens (e.g., translated page) | Translating substantial text volumes between languages. | $0.0026 (input) + $0.008 (output) = $0.0106 |
Hermes 4 70B's cost-effectiveness becomes evident in scenarios with moderate to high input and output volumes, especially where its conciseness reduces overall output token count. For tasks involving very long inputs or outputs, careful token management and prompt engineering are key to keeping costs optimized.
Optimizing costs with Hermes 4 70B involves strategic use of its features and understanding the pricing model. Its high speed and conciseness are inherent advantages, but proactive measures can further enhance efficiency and reduce expenditure.
Here are key strategies to maximize value from Hermes 4 70B, focusing on both technical implementation and operational best practices.
Hermes 4 70B is noted for its concise outputs. Capitalize on this by:
The 128k context window is powerful but can be costly if overused. Manage it effectively by:
Given Hermes 4's high output speed, consider batching requests to maximize throughput and potentially reduce per-request overhead:
Proactive monitoring is essential for cost control:
While Hermes 4 70B is versatile, not every sub-task requires its full power:
Hermes 4 70B is a powerful, open-weight large language model developed by Nous Research. It is based on the Llama-3.1 70B architecture, fine-tuned for enhanced performance in non-reasoning generative tasks.
Hermes 4 70B is explicitly labeled as a "Non-reasoning" model. While it can perform well on many tasks, it is not optimized for complex logical deduction, multi-step problem-solving, or intricate analytical reasoning. For such tasks, models specifically designed for reasoning capabilities might be more appropriate.
Benchmarked on Nebius (FP8), Hermes 4 70B exhibits a low latency of 0.58 seconds to the first token. Its median output speed is an impressive 76 tokens per second, making it one of the faster models in its class.
Hermes 4 70B offers competitive pricing with an input token cost of $0.13 per 1M tokens and an output token cost of $0.40 per 1M tokens on Nebius (FP8). This is generally below the average for comparable models, especially considering its high performance and conciseness.
Hermes 4 70B features a substantial context window of 128,000 tokens. This allows it to process and generate responses based on very long inputs, making it suitable for tasks involving extensive documents or prolonged conversations.
Yes, as an open-weight model, Hermes 4 70B can be fine-tuned on custom datasets to adapt its behavior and knowledge to specific domains or tasks. This offers significant flexibility for developers to tailor the model to their unique requirements.
Hermes 4 70B excels in applications requiring high-quality, concise text generation and summarization. This includes content creation (marketing copy, articles), advanced chatbots, data extraction, code generation, and efficient summarization of long documents, particularly where speed and cost-efficiency are critical.