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.
| Embeddinggemma-300m google/embeddinggemma-300m | Gemma-7b-IT-Lora google/gemma-7b-it-lora | |
|---|---|---|
| Provider | ||
| Family | Gemma | Gemma |
| Modality | embedding | text |
| Context window | — | 3,500 tok |
| Max output | — | 4,096 tok |
| Released | 2025-09-04 | 2024-04-02 |
| Input price | $0.020 /1M | $0.080 /1M |
| Output price | — | $0.160 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | — | yes |
| Vision | yes | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.
This is a Gemma-7B base model that Cloudflare dedicates for inference with LoRA adapters. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.
# 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 Embeddinggemma-300m
client.chat.completions.create(
model="google/embeddinggemma-300m",
messages=[{"role":"user","content":"hello"}],
)
# Try Gemma-7b-IT-Lora — same client, same key
client.chat.completions.create(
model="google/gemma-7b-it-lora",
messages=[{"role":"user","content":"hello"}],
)