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.
| M2.7 minimax/m2.7 | Aura-2-EN deepgram/aura-2-en | |
|---|---|---|
| Provider | MiniMax | Deepgram |
| Family | Aura | |
| Modality | text | audio-tts |
| Context window | 128,000 tok | — |
| Max output | 4,096 tok | — |
| Released | 2026-04-13 | 2025-10-09 |
| Input price | $0.300 /1M | $0.030 /1K ch |
| Output price | $1.20 /1M | — |
| Cache read | — | — |
| Tools | — | — |
| Streaming | yes | yes |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
MiniMax's M2.7 language model with multilingual capabilities.
Aura-2 is a context-aware text-to-speech (TTS) model that applies natural pacing, expressiveness, and fillers based on the context of the provided text. The quality of your text input directly impacts the naturalness of the audio output.
# 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 M2.7
client.chat.completions.create(
model="minimax/m2.7",
messages=[{"role":"user","content":"hello"}],
)
# Try Aura-2-EN — same client, same key
client.chat.completions.create(
model="deepgram/aura-2-en",
messages=[{"role":"user","content":"hello"}],
)