A 405-billion parameter open-weight model from Nous Research, offering exceptional conciseness but at a premium price with below-average speed and intelligence scores.
Hermes 4 405B (Reasoning) is one of the largest and most ambitious open-weight models available, developed by the prolific AI research group Nous Research. Built upon Meta's powerful Llama-3.1 405B foundation, this model is part of the Hermes family, known for its high-quality instruction tuning on synthetic and curated datasets. With a massive 405-billion parameter count and a generous 128,000-token context window, it is positioned to tackle complex, large-scale reasoning and generation tasks that are beyond the reach of smaller models.
Despite its impressive scale, Hermes 4's performance on the Artificial Analysis Intelligence Index is surprisingly modest. It scores a 42, placing it slightly below the average for the 51 models benchmarked in its class. This suggests that while its sheer size provides a vast repository of knowledge, its ability to apply that knowledge in standardized reasoning and problem-solving tests does not currently surpass its peers. The model's most striking characteristic is its extreme conciseness. During the intelligence evaluation, it generated only 5.8 million tokens, a fraction of the 22 million token average. This tendency to provide short, direct answers is a significant differentiator, impacting both user experience and operational cost.
The economic profile of Hermes 4 is a critical consideration. On the benchmarked provider, Nebius (using FP8 quantization), its pricing is set at $1.00 per million input tokens and a steep $3.00 per million output tokens. These rates are significantly higher than the average for comparable open-weight models, which stand at $0.57 for input and $2.10 for output. This premium pricing, combined with its modest intelligence score, positions Hermes 4 as a specialized tool rather than a general-purpose workhorse. The total cost to run our intelligence benchmark on this model was $65.50, highlighting the financial commitment required to operate it at scale.
Performance in terms of speed is another area where Hermes 4 does not lead the pack. With a median output speed of 37 tokens per second, it is slower than the class average of 45 tokens per second. This, combined with a time-to-first-token (TTFT) of 0.77 seconds, means it may not be suitable for real-time, interactive applications where low latency is paramount. Ultimately, Hermes 4 405B presents a unique trade-off: users gain access to a massive, open model with an enormous context window and exceptional brevity, but must accept higher costs, slower generation speeds, and intelligence performance that doesn't necessarily reflect its colossal parameter count.
42 (28 / 51)
37 tokens/s
$1.00 / 1M tokens
$3.00 / 1M tokens
5.8M tokens
0.77 seconds
| Spec | Details |
|---|---|
| Model Name | Hermes 4 - Llama-3.1 405B (Reasoning) |
| Owner / Creator | Nous Research |
| Base Model | Meta Llama 3.1 405B |
| Parameters | ~405 Billion |
| Context Window | 128,000 tokens |
| License | Llama 3.1 Community License |
| Input Modalities | Text |
| Output Modalities | Text |
| Architecture | Transformer-based, Decoder-only |
| Training Data | Fine-tuned on a curated dataset of synthetic and real-world data. |
| Specialization | Reasoning, General Purpose Tasks |
| Benchmarked Provider | Nebius (FP8 Quantization) |
This analysis focuses exclusively on the performance and pricing of Hermes 4 405B as offered by Nebius, utilizing FP8 quantization. This specific implementation is the basis for all metrics on this page. As other providers may offer different quantizations or hosting environments, performance and cost could vary elsewhere.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Balanced | Nebius (FP8) | The sole provider benchmarked for this analysis, offering a known baseline for performance, price, and latency using an efficient FP8 quantization. | Lack of competition means there are no alternative price points or performance profiles to compare against within this analysis. |
| Cost-Focused | (Not Benchmarked) | Self-hosting or seeking providers with more aggressive quantization (e.g., 4-bit) could potentially lower costs. | Requires significant technical expertise and infrastructure; performance may be degraded with heavier quantization. |
| Performance-Focused | (Not Benchmarked) | A provider offering the model with higher precision (e.g., BF16) might yield better quality, though likely at higher cost and latency. | Increased operational costs and potentially slower inference speeds compared to the benchmarked FP8 version. |
Provider picks are based on the data collected for this analysis. The AI landscape is dynamic; performance and pricing are subject to change. 'Not Benchmarked' indicates a hypothetical alternative not included in our direct testing.
The cost of using Hermes 4 405B depends heavily on the ratio of input to output tokens. Its pricing structure, with output tokens costing three times as much as input tokens, creates distinct economic advantages and disadvantages for different types of tasks. The following scenarios illustrate the estimated cost for common workloads.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Long Document Summarization | 100k input tokens | 2k output tokens | Represents an input-heavy RAG or summarization task, leveraging the large context window. | ~$0.106 |
| Creative Content Generation | 500 input tokens | 8k output tokens | A typical output-heavy task like writing a blog post or generating a detailed plan. | ~$0.0245 |
| Complex Chatbot Session | 15k input tokens | 15k output tokens | A balanced, multi-turn conversation where the model must recall previous context. | ~$0.06 |
| Codebase Analysis & Refactoring | 80k input tokens | 10k output tokens | Analyzing a large file or set of files and suggesting improvements. | ~$0.11 |
| Email Drafting | 200 input tokens | 500 output tokens | A short, common task with a small amount of generation. | ~$0.0017 |
These examples highlight a clear pattern: Hermes 4 405B is most economical for workloads that are heavily skewed towards input, such as analyzing or summarizing large documents. Tasks requiring extensive generation become comparatively expensive, quickly driving up costs due to the high price of output tokens.
Given its unique profile of high cost, low verbosity, and a massive context window, managing the operational expense of Hermes 4 405B requires a strategic approach. Developers should focus on leveraging its strengths while mitigating its weaknesses to achieve a positive return on investment.
Hermes 4's greatest cost-saving feature is its natural brevity. Since you pay a premium for every output token, getting the answer in fewer tokens is a significant advantage.
The 1:3 input-to-output price ratio makes the model a better fit for certain types of problems. Focus your use cases on scenarios where the input is much larger than the output.
While the large context window is a powerful feature, filling it unnecessarily is a costly mistake. At $1.00 per million tokens, a full 128k context prompt costs approximately $0.128 each time.
Hermes 4 405B (Reasoning) is an open-weight large language model developed by Nous Research. It is a fine-tuned version of Meta's Llama 3.1 405B model, specifically adapted using a curated dataset to enhance its performance on tasks requiring reasoning and instruction following.
Despite its massive 405-billion parameter size, its score of 42 on the Artificial Analysis Intelligence Index is below the average for its peer group. This suggests that parameter count alone does not guarantee top-tier performance on standardized benchmarks, and other models may offer better reasoning capabilities for their size or cost.
Its low verbosity, or high conciseness, is a key differentiator. The model tends to provide short, direct answers. This has two main benefits: first, it reduces costs, as you pay less for output tokens. Second, it can improve user experience by providing information that is faster to read and less prone to conversational filler.
The high cost is primarily due to the immense computational resources required to host and run a 405B-parameter model. Even with efficient quantization like FP8, the hardware (e.g., multiple high-end GPUs) is expensive to operate. Providers pass these operational costs on to the end-user. It is significantly more resource-intensive than models in the 7B to 70B parameter range.
The "(Reasoning)" tag suggests that Nous Research specifically tuned the model on datasets designed to improve its performance in logic, mathematics, and multi-step problem-solving. However, its benchmark scores indicate that while this was the goal, it doesn't consistently outperform other models in these areas in a standardized testing environment.
The 128k context window is a powerful tool for tasks involving very long documents, but it's not always an advantage. Using the full context is expensive due to the input token cost ($1.00/1M tokens). For tasks that only require a few thousand tokens of context, using this model can be overkill and economically inefficient compared to smaller, cheaper models.