Nous Hermes 2 Mixtral 8x7B on Together AI
Hermes 2 · Nous Research
DeprecatedRetired August 28, 2025
Open SourceLast refreshed 2026-11-11. Next refresh: weekly.
Why use Nous Hermes 2 Mixtral 8x7B on Together AI?
Together AI offers Nous Hermes 2 Mixtral 8x7B with pay-as-you-go pricing at $0.40/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Nous Hermes 2 Mixtral 8x7B across 4 providers to find the best fit for your use caseInput / 1M
$0.40
Output / 1M
$0.40
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install togetherAuth
export TOGETHER_API_KEY=...Call
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",Model ID
NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPORequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", not the LLMReference slug "nous-hermes2-mixtral-8x7b".
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_API_KEY; rename it only if your application config maps the new variable.
Compare Nous Hermes 2 Mixtral 8x7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| OctoAI API (Deprecated) | — | — |
| Fireworks AI | — | — |
| Together AI | $0.40 | $0.40 |
| OctoML (Deprecated) | $0.40 | $0.60 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.40 |
| Output tokens | $0.40 |
Capabilities
No model capability flags are currently sourced.
About Nous Hermes 2 Mixtral 8x7B
Mixtral MoE variant of Hermes trained on 1M+ GPT-4 entries for content generation and customer service. Available in quantized formats (GGUF, GPTQ, AWQ) for flexible deployment.
Get Started
Model Specs
Released2023-12-12
Parameters8x7B
Context32K
ArchitectureMixture of Experts
Knowledge cutoff2023-12