Last refreshed 2026-09-14. Next refresh: weekly.
Why use Snorkel Mistral PairRM on Fireworks AI?
Fireworks AI offers Snorkel Mistral PairRM with pay-as-you-go pricing at $0.20/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare Snorkel Mistral PairRM across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport FIREWORKS_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],snorkel-mistral-pairrmRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="snorkel-mistral-pairrm",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Fireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
- The examples expect FIREWORKS_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.