Last refreshed 2026-06-15. Next refresh: weekly.
Why use OpenHermes 2.5 Mistral 7B on Together AI?
Together AI offers OpenHermes 2.5 Mistral 7B with pay-as-you-go pricing at $0.20/1M 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 OpenHermes 2.5 Mistral 7B across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="openhermes-2.5-mistral-7b",openhermes-2.5-mistral-7bRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="openhermes-2.5-mistral-7b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- 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 OpenHermes 2.5 Mistral 7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.20 | $0.20 |
| Fireworks AI | $0.20 | $0.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
No model capability flags are currently sourced.
About OpenHermes 2.5 Mistral 7B
OpenHermes 2.5 Mistral 7B is an advanced large language model developed by Teknium, building on the previous version, OpenHermes 2. Utilizing a transformer architecture, it's fine-tuned on over one million entries, combining code and non-code data, primarily composed of GPT-4 generated text. This enhances its human-like response capabilities across diverse contexts. It excels in conversational AI with its multi-turn dialogue support through the ChatML format, significantly improves in code generation tasks with a high HumanEval score, and performs robustly on benchmarks like GPT4All and AGIEval.