RAG with Pro
40M input + 5M output tokens / month
Long context on Gemini Pro
- Official (Gemini 3.1 Pro)
- $140.00/mo
- LumeAPI
- $84.00/mo
- Monthly savings
- $56.0040% off
Gemini API
Gemini 3.1 Pro, 3.5 Flash, and 3 Flash at ~40% below reference on one OpenAI-compatible gateway.
Hub for Gemini text models. See Gemini Pro for complex workloads.
Official reference vs LumeAPI catalog rates. Pricing unit: per 1M input / output tokens. Last updated: July 2026. Source: provider list price.
Illustrative totals for gemini-3.1-pro-preview using catalog list prices — your actual bill depends on retries, tool loops, and output length.
40M input + 5M output tokens / month
Long context on Gemini Pro
180M input + 35M output tokens / month
High-volume Flash steps
Gemini Flash models handle throughput. Gemini 3.1 Pro handles long context and harder reasoning. Most mature products use both in one gateway integration.
Multimodal roadmaps can add Gemini image catalog entries on the same API key when text features stabilize.
See /gemini-pro-api for Pro-specific long-context and coding narratives; /cheap-gemini-api for savings-focused migration.
Split traffic between Flash and Pro using feature flags. Flash handles routing, classification, and first-pass answers; Pro handles escalations with large retrieved context.
When you add Gemini image models later, reuse the same API key and wallet—update runbooks so finance expects mixed text and image line items in Usage.
LumeAPI is designed for developers who want to integrate without scheduling demos. Create an account, confirm your email, and open Console to generate an API key. Fund your USD wallet with USDT on supported chains when you are ready for billable traffic—there is no mandatory minimum beyond what your tests require.
Point your OpenAI-compatible client at https://api.lumeapi.site/v1, set Authorization to Bearer your key, and pass a catalog model id in the model field. Run a short curl or SDK script from /docs to verify latency, streaming, and error handling before you attach the key to production services.
Use Usage logs to reconcile per-call cost with finance forecasts. When a model tier is too expensive or quality is insufficient, change model id—not your entire integration. For cross-provider price tables and Research deep dives, follow internal links on this page rather than duplicating migration math here.
Every catalog model has a detail page under /models with official reference pricing, LumeAPI pricing, and links to /docs/models/{id} for parameters and curl examples. Start there when this commercial page points you to a model id you have not called before.
The /docs index lists gateway authentication, Chat Completions, image endpoints, and async video patterns. llms.txt bundles the same information for agent tooling—useful when you want a single URL to paste into Cursor or an internal bot.
Research articles explain why bills grow and how to compare providers; commercial pages like this one explain what LumeAPI offers and how to start. Follow internal links instead of searching for duplicate migration content across pages.
If billing, chain deposits, or integration behavior is unclear, use /contact for support channels. Include your model id, approximate request time, and whether the issue is authentication, balance, or model parameters—that speeds up resolution.
GPT, Claude, and Gemini catalog ids with published per-token or per-media rates — not obscure small models marketed as discounts.
Official reference vs LumeAPI columns on every commercial page. Your request model id matches Usage logs and billing.
One base URL for Chat Completions, image generations, and async video. Swap key, base URL, and model id — keep your SDK.
Top up with USDT, create keys in Console, and track per-call cost in Usage — no sales calls required.
Three values change: API key, base URL, model id. Everything else stays the same.
from openai import OpenAI
client = OpenAI(api_key="YOUR_OPENAI_API_KEY")
response = client.chat.completions.create(
model="gemini-3.1-pro-preview",
messages=[{"role": "user", "content": "Hello"}],
)from openai import OpenAI
client = OpenAI(
api_key="YOUR_LUMEAPI_KEY",
base_url="https://api.lumeapi.site/v1",
)
response = client.chat.completions.create(
model="gemini-3.1-pro-preview",
messages=[{"role": "user", "content": "Hello"}],
)Full step-by-step rollout, streaming checks, and FAQ: Gemini cost guide →
API key, base URL (https://api.lumeapi.site/v1), and model id to a LumeAPI catalog entry. Message shape stays OpenAI-compatible for most apps.
Streaming, tool calling, JSON mode, and error handling on your heaviest models. Shadow 5–10% of traffic before full cutover.
Provider-native features (Anthropic Batch, Google Grounding, OpenAI Assistants) may require the official API. Test your exact payload.
Keep environment variables for base URL and model id. Switch back instantly if staging tests fail.
Not affiliated.
Model cost per task in staging.
See model pages for limits.
May need native Google API.
Pro for long context. Flash for routing.
Flash keeps margin on per-user AI.
Text today, Gemini image on same gateway.
Mix Gemini with GPT and Claude by model id.
Pro for hard tasks. Flash for volume.
See /gemini-pro-api.
See /cheap-gemini-api.
Yes with catalog Gemini ids.
See /image-generation-api.
See /ai-api-pricing.
Create a key and call Gemini through https://api.lumeapi.site/v1. Need help choosing a model? Browse the developer docs or contact support.