Last refreshed 2026-09-14. Next refresh: weekly.
Why use Snorkel Mistral PairRM on Together AI?
Together AI offers Snorkel Mistral PairRM 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 Snorkel Mistral PairRM 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="snorkel-mistral-pairrm",snorkel-mistral-pairrmRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="snorkel-mistral-pairrm",
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 Snorkel Mistral PairRM 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 Snorkel Mistral PairRM
The Snorkel Mistral PairRM-DPO is a chat-optimized large language model, leveraging the Mistral-7B-Instruct-v0.2 architecture. Designed to interpret and respond efficiently to user inputs, it employs Direct Preference Optimization alongside the Pairwise Reward Model (PairRM) to enhance its alignment with human preferences. Exclusively trained on the UltraFeedback dataset without input from other LLMs, it excels in generating text for conversational contexts, ranking third on the AlpacaEval 2.0 leaderboard at 30.22. Post-processing with PairRM-best-of-16 boosts its score to 34.86.