Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| Hermes-2-Pro-Mistral-7b hf/nousresearch/hermes-2-pro-mistral-7b | Mistral-Small-3.1-24b-Instruct mistralai/mistral-small-3.1-24b-instruct | |
|---|---|---|
| Provider | Hugging Face | Mistral |
| Family | Mistral | Mistral |
| Modality | text | text |
| Context window | 4,096 tok | 131,072 tok |
| Max output | 4,096 tok | 4,096 tok |
| Released | 2024-04-01 | 2025-03-18 |
| Input price | $0.050 /1M | $0.350 /1M |
| Output price | $0.100 /1M | $0.550 /1M |
| Cache read | — | — |
| Tools | — | yes |
| Streaming | yes | yes |
| Vision | — | yes |
| JSON mode | — | yes |
| Reasoning | — | — |
| Prompt caching | — | — |
Hermes 2 Pro on Mistral 7B is the new flagship 7B Hermes! Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try Hermes-2-Pro-Mistral-7b
client.chat.completions.create(
model="hf/nousresearch/hermes-2-pro-mistral-7b",
messages=[{"role":"user","content":"hello"}],
)
# Try Mistral-Small-3.1-24b-Instruct — same client, same key
client.chat.completions.create(
model="mistralai/mistral-small-3.1-24b-instruct",
messages=[{"role":"user","content":"hello"}],
)