Hermes 3 405B Instruct
Nous Research · released Aug 13, 2024 · NousResearch/Hermes-3-Llama-3.1-405B
- Type
- Open weightsLlama 3 Community License
- Params
- 406B
- Context
- 131K
about 98K words of context · download allowed, licence restricts use
Our take
Written Aug 3, 2026Hermes 3 is a 406-billion-parameter text-only instruct model from Nous Research, released in 2024 with a 131,072-token request limit. It is the largest Hermes variant available, though no benchmark scores have been catalogued to verify its quality.
Pick this when you need the largest available Hermes instruct model for long-context text tasks, or when you want uniform pricing across every provider we track. Skip it if you need measured quality scores, fast responses, or a fully permissive licence for unrestricted commercial use.
The case for it
- 406 billion total parameters — extremely large scale among downloadable instruct models.
- 131,072-token request limit, enough for document-scale tasks.
- Every listed provider charges the same rate, so there is no cost-comparison friction.
The case against it
- No benchmark scores in our catalogue — chat, reasoning, coding and safety performance are all unverified.
- One measured endpoint delivers only 9 tokens per second, so low-latency needs are not met.
- The Llama 3 Community Licence is less flexible than Apache or MIT, and all 406 billion parameters appear active on every token with no efficiency architecture indicated.
How good is it?
We hold no score for this model.
We look for every model we track on every board we watch, and none of them has turned up Hermes 3 405B Instruct — so there is no intelligence, coding, agentic or writing score to show you, not a low one, none.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
GeForce RTX 4090 · 24 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M1 Pro (16-core GPU) · 32 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Comfortable fit
Apple M3 Ultra (80-core GPU) · 512 GB
Room to spare. 118.1 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 3 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per 1M tokens
- $1.00 in / $1.00 out
- Context served
- 131K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $1.00 / $1.00 | 131K | not measured | Unknown | Unknown | Unknown |
| DeepInfrafp8 | $1.00 / $1.00 | 131K | 9 tok/s | No | No | Confirmed |
| DeepInfrafp8 | $1.00 / $1.00 | 131K | not measured | Unknown | Unknown | Unknown |
DeepInfraAcross the 3 listings we hold: 1 say they do not train on prompts, 0 say they do and 2 do not say. 1 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouter | ✗ | ✓ | ✓ |
| DeepInfrafp8 | ✗ | ✓ | ✓ |
| DeepInfrafp8 |
Tool calling: 0 of 3 listings say yes, 2 say no, 1 publishes no parameter list. JSON output: 2 of 3 listings say yes, 1 publishes no parameter list. Strict schema: 2 of 3 listings say yes, 1 publishes no parameter list.
Models people weigh against Hermes 3 405B Instruct
When we formed this view
Dates behind this page
Prices last checked 4d ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No board we watch has turned up a score, so we hold no quality figures at all.
- 1 of 3 listings publish no parameter list, so what their API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 2 of 3 listings do not say whether they train on prompts.
- We hold no cached-input rate for any of its listings.
Licence and identifiers
What the licence allowsLlama 3 Community License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Llama 3 Community License
Commercial use allowed below 700M MAU; requires "Built with Meta Llama 3" attribution and Llama naming on derivatives.
Identifiers
- Hugging Face
- NousResearch/Hermes-3-Llama-3.1-405B
- Architecture
- Dense
- Modality record
- text->text
- Catalogue slug
- nousresearch-hermes-3-405b-instruct