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.
| Gemma-4-26b-A4b-IT google/gemma-4-26b-a4b-it | Gemma-7b-IT-Lora google/gemma-7b-it-lora | |
|---|---|---|
| Provider | ||
| Family | Gemma | Gemma |
| Modality | text | text |
| Context window | 256,000 tok | 3,500 tok |
| Max output | 4,096 tok | 4,096 tok |
| Released | 2026-04-02 | 2024-04-02 |
| Input price | $0.100 /1M | $0.080 /1M |
| Output price | $0.300 /1M | $0.160 /1M |
| Cache read | — | — |
| Tools | yes | — |
| Streaming | yes | yes |
| Vision | yes | — |
| JSON mode | yes | — |
| Reasoning | yes | — |
| Prompt caching | — | — |
Gemma 4 is Google's most intelligent family of open models, built from Gemini 3 research to maximize intelligence-per-parameter.
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 Gemma-4-26b-A4b-IT
client.chat.completions.create(
model="google/gemma-4-26b-a4b-it",
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"}],
)