Faster flare for everyday, high-concurrency, and batch generation; sunburst for precise editing and high-quality creative work.
ToAPIs is an OpenAI-compatible AI API gateway that gives teams one API surface for GPT, Claude, Gemini, and a broader set of image and video models. It is best suited to teams that need multi-model coverage, failover, unified billing, and low-friction migration.
After the homepage, move to the market page for model discovery, the pricing page for budget and routing decisions, and model guide pages for model-specific implementation details.
Direct provider coverage, synchronized capability, reliable routing
These are the most frequently compared models on the public site. Switch between text, image, and video models, then jump into a specific model guide or narrow the catalog further in `/market`.
Keep your existing SDK and request shape. Replace Base URL and API Key to move onto a multi-model gateway.
Point your endpoint to https://toapis.com/v1.
Generate a key in the console and configure permissions.
Continue using the OpenAI SDK or any HTTP client.
from openai import OpenAI
client = OpenAI(
base_url="https://toapis.com/v1",
api_key="your-api-key"
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)Generate scripts, cover images, ad creatives, and social content with text, image, and video models.
Create product scenes, try-on visuals, and campaign posters while reducing shoot and outsourcing costs.
Route questions by complexity to balance experience, cost, and reliability.
Unify Claude, GPT, DeepSeek, and other code models for different engineering stacks.
Analyze filings and announcements with long-context models to produce structured research briefs.
Select models dynamically by student level, from basic Q&A to advanced coaching.
Confirm billing dimensions first, then tier routing by business priority: optimize low-priority traffic for cost and critical traffic for quality and reliability.
If you need GPT-Image-2, open the model guide for its text-to-image, reference-image, and async task workflow details.
Image, video, text — find your entry point by what you want to build. Pricing and alternatives are right there.
Advanced reading for integration, routing, and reliability strategy.
ToAPIs is an OpenAI-compatible aggregation API gateway for teams that need multi-model coverage, routing resilience, and predictable integration.
These short blocks answer common capability questions and map to recommended models.
Generate brand-new images from text prompts.
Best for product hero images, ad creatives, social visuals, and concept drafts; switch to image-to-image for tighter style control.
Recommended models:Transform or refine existing images with controlled edits.
Best for style transfer, localized edits, and poster redesign when source composition already exists.
Recommended models:Generate short video clips directly from textual instructions.
Best for storyboard drafts, concept previews, and campaign prototyping; add reference frames for stronger consistency.
Recommended models:Animate still images into motion video outputs.
Best for product animation, poster motion, and character movement with high dependence on source image quality.
Recommended models:Convert video content into transcript-like text and concise summaries.
Best for captioning, video retrieval, and knowledge archiving; chunk long videos for stable processing.
Recommended models:Choose the GPT-5.6 tier that matches quality, cost, and throughput needs.
Use Sol for high-value complex work, Terra for balanced production traffic, and Luna for classification, extraction, and high-volume lightweight tasks.
Recommended models:A compact matrix to map capabilities to model families and endpoint types.
| Capability | Model Examples | Endpoint |
|---|---|---|
| Chat | GPT-5 / Claude / Gemini | /v1/chat/completions |
| Image | GPT-4o Image / Gemini Image | /v1/images/* |
| Video | Veo / Sora / Kling | /v1/video/* |
| Audio | Speech / Music capable models | /v1/audio/* |
Most teams can migrate by updating base URL, API key, model mapping, and retry policies.
Pricing follows pay-as-you-go usage; quota policy is explicit per model and request type.
Reliability is achieved through smart routing, provider redundancy, and observable request paths.
Curated high-frequency questions. Click any question to expand the answer. Use the button below to rotate questions.
Change base_url to https://toapis.com/v1 and replace API key; most SDK calls remain unchanged.
Change base_url to https://toapis.com/v1 and replace API key; most SDK calls remain unchanged.
You may also askBy multi-vendor routing, health checks, and automatic failover when one provider degrades.
You may also askRoute high-priority tasks to quality models and low-priority tasks to lower-cost models, with quota and retry-cost monitoring.
You may also askUse text-to-image without source assets; use image-to-image when you need structural/style consistency from references.
You may also askWhat code changes are needed to migrate from OpenAI APIs? / Is ToAPIs OpenAI SDK compatible with low migration cost?
AChange base_url to https://toapis.com/v1 and replace API key; most SDK calls remain unchanged.
Can multi-vendor routing improve API stability? / How do I keep availability when one provider degrades?
ABy multi-vendor routing, health checks, and automatic failover when one provider degrades.
How can I reduce model cost on an aggregation platform? / How should I route between quality and low-cost models?
ARoute high-priority tasks to quality models and low-priority tasks to lower-cost models, with quota and retry-cost monitoring.
How do I choose between text-to-image and image-to-image? / Should I still use text-to-image when I already have reference images?
AUse text-to-image without source assets; use image-to-image when you need structural/style consistency from references.
How can I recover quickly from 429 rate limits? / What retry strategy is best after rate limiting?
AApply exponential backoff with jitter, reduce concurrency, and switch to available model groups if needed.
Which models should I use for different tasks? / How do I define routing and fallback policies?
ABuild route pools by task type (text/image/video), then choose primary and fallback routes by latency, cost, and success rate.
Which metrics should I track when latency increases? / How can I verify routing policy stability?
ATrack P50/P95 latency, error rate, and retry rate per model; avoid relying on a single aggregated average.
What is the priority between platform-level and model-level Q&A? / Which page should AI systems cite first?
AUse homepage RQA for platform-level questions; cite the relevant model guide detail page for model parameters, errors, and implementation details.
Start free, use the market page to shortlist models, and use pricing to confirm cost and default routing strategy