I wouldn’t pick a multimodal API by counting models. I’d start with what the application actually does: synchronous text requests, occasional image generation, thousands of queued video jobs, or inference behind enterprise IAM.
Those are different operating models. A single account can simplify procurement and billing, but it does not make request schemas, output formats, or job lifecycles interchangeable.
My shortlist splits into four categories: a cross-provider gateway for mixed commercial models, Replicate for experimentation and custom deployments, fal.ai for media-heavy products, and Google Vertex AI for Google Cloud operations.
The catalog and pricing examples below reflect the article’s September 2026 snapshot, not independently verified live quotes. Temporary pricing deserves particular attention before it goes into a cost model.
Start with the integration you’ll have to maintain
Before comparing providers, I’d answer five questions:
- Which outputs do I need? Accepting an image or video as input does not imply that a model can generate images or video.
- Am I consuming commercial models or deploying my own? These lead to different platform choices.
- Which requests become background jobs? Long-running media generation needs persistence and completion handling, not just an HTTP client.
- What is the actual billing unit? Tokens, images, megapixels, characters, calls, and generated seconds are not comparable without a workload.
- Which controls are mandatory? IAM, regional availability, observability, quotas, retries, concurrency, and governance can outweigh catalog breadth.
A multimodal model processes more than one data type. A multi-model API exposes multiple distinct models, potentially from different creators. I keep that distinction explicit when evaluating platforms: neither label alone guarantees the outputs or operational behavior I need.
| Platform approach | Workload I’d use it for | Integration shape | Cost dimensions |
|---|---|---|---|
| Cross-provider gateway | Text, image, video, and audio in one application | Shared account; OpenAI-compatible routes where supported | Model-specific tokens, calls, or generated seconds |
| Replicate | Open-model evaluation, version pinning, custom deployments | Prediction API with model- or version-specific inputs | Output units or compute time |
| fal.ai | High-volume generative media | Endpoint-specific SDK calls and queued HTTP jobs | Images, megapixels, seconds, characters, or requests |
| Google Vertex AI | Google models under cloud governance | Projects, IAM, regions, and service-specific APIs | Tokens, images, video units, or audio units |
When Google Cloud governance is the constraint
I’d evaluate Vertex AI first if the application and its data already live in Google Cloud and requests must follow existing IAM and regional policies.
A regulated document workflow is a useful example: Gemini handles analysis, Imagen generates approved creative assets, and Veo supports controlled video experiments. In that setting, auditability and cloud operations matter more than reducing setup to one API key.
Google is both the platform operator and the creator of Gemini, Imagen, Veo, and Lyria. The available workload categories include:
- Text and multimodal reasoning through Gemini.
- Image generation through Gemini Image and Imagen.
- Video generation through Veo.
- Speech or music through Gemini audio, Cloud speech services, and Lyria.
Vertex AI also provides Model Garden, evaluation, grounding, quotas, and Google Cloud observability.
Pricing and operational trade-offs
The September 2026 snapshot lists standard Gemini 3.8 Flash promotional pricing at $0.75 per million input tokens and $3.75 per million text output tokens, through December 31, 2026. Imagen 4 Fast is listed at $0.02 per generated image.
Video and audio have separate units and rates. I’d use the Vertex AI pricing reference rather than extrapolate from Gemini text pricing.
The trade-off is setup and workflow complexity. Projects, IAM, regions, and usually Cloud Storage replace the simplicity of a standalone API key. Different model families still have different endpoints and operational paths.
That is reasonable overhead when cloud governance is a requirement. It is less compelling when the main goal is rapid switching across model creators.
When the model itself is part of your deployment
Replicate is where I’d look when the workflow involves testing checkpoints, pinning versions, fine-tuning, or deploying a custom variant.
For example, an ML team might benchmark several open image and video models, select specific versions, then serve its own fine-tuned checkpoint. Deployment flexibility matters more here than uniform chat compatibility.
Replicate is a third-party hosting platform with official and community models. The catalog snapshot includes:
| Modality | Examples |
|---|---|
| Text | GPT-5.6 models |
| Images | Seedream 5, Qwen Image 3 |
| Video | Seedance 2.5, Wan 3 |
| Audio | MiniMax Speech 2.8, Gemini TTS |
The distinction between official and community models is operationally important. Official models are maintained, always warm, and expose stable model-specific prediction APIs. Community models may require version hashes and have different cold-start or maintenance characteristics.
Budget per model, not per platform
There is no universal inference rate. Official models use units such as tokens, images, or video seconds; many public models bill by compute time.
One time-limited example is GPT-5.6 Sol at $2.50 per million input tokens and $15 per million output tokens, through September 18, 2026. The individual model page is the pricing authority.
I like this fit for experimentation and custom deployment. I would not expect OpenAI-compatible requests across the catalog, nor assume that community models share the same latency or maintenance guarantees.
Version pinning helps control model selection. It does not eliminate the need to understand each model’s input schema and billing behavior.
When media generation is the product
For an application generating thousands of ad images and short clips, I’d put fal.ai near the top of the evaluation list.
The attraction is its focus on generative media: queued requests, webhooks, media-oriented endpoints, and high-throughput infrastructure. Broad access to general-purpose LLMs is less central to its offering.
The catalog snapshot includes GPT Image 2, Seedream 5, and Qwen Image 3 for images; Seedance 2.5, Wan 3, Kling 3, and Veo 3.1 for video; and MiniMax, ElevenLabs, and Index TTS endpoints for audio.
There is a common SDK pattern, but each endpoint keeps its own input schema. I’d still put an application-level abstraction around those endpoints rather than leak provider payloads throughout the codebase.
Output pricing needs output settings
- Images can be metered per image or megapixel.
- Video can be metered per second or per video.
- Audio can be metered per character, second, or request.
The snapshot gives GPT Image 2 at roughly $0.005 per low-quality 1024×768 image. Seedance 2.5 is about $0.473 per 720p output second for the common 16:9 case, with the Seedance token formula remaining authoritative.
Those settings belong beside the price. Removing resolution or quality turns a useful quote into a misleading comparison.
Pricing information can be queried programmatically for supported endpoints. That is useful for larger applications, but mixed units and endpoint-specific schemas still need explicit handling.
I’d choose this direction when image, video, and audio generation dominate the workload. I’d look elsewhere first if broad frontier text-model access were the primary requirement.
When one application mixes commercial model families
A support SaaS might generate text continuously, create campaign images occasionally, produce onboarding clips in the background, and add speech to an assistant. Maintaining separate vendor relationships for every category can become unnecessary operational work.
For that workload, CometAPI is a cross-provider option: one account, one balance, and a shared integration layer across text, image, video, and audio.
It is a third-party API provider, not a model creator. Its catalog spans providers including OpenAI, Anthropic, Google, xAI, ByteDance, and Alibaba.
The September 3, 2026 snapshot lists:
| Model | Workload | Starting price |
|---|---|---|
| Gemini 3.8 Flash | Text and multimodal understanding | $0.60 per million input tokens |
| GPT Image 2 | Image generation | $4 per million tokens |
| Seedance 2.5 | Video generation | $0.0824 per generated second |
| GPT-Realtime-2.1 | Audio | $3.20 per million input tokens |
The provider documents a 0.8:1 consumer ratio for models with unified official pricing. Models without official APIs may instead be billed per call.
For applicable OpenAI-compatible routes, the documented base URL is:
https://api.cometapi.com/v1
I’d treat that as a routing convenience, not a promise of universal compatibility. Chat and responses are the most useful compatibility surface; image, video, and audio can require dedicated endpoints and parameters.
The practical benefit is consolidated billing and easier access across commercial model families. The remaining work is model-specific: verify availability, pin tested model IDs, and handle each route’s payload and output format.
The production checks I wouldn’t skip
Persist asynchronous job state
Text requests are often synchronous. Video and longer media jobs normally are not.
My default flow would be:
- Submit the generation request.
- Save its task ID.
- Poll the result endpoint or receive a webhook.
- Handle completion in the application.
I would not hold a synchronous request open for video generation unless the provider explicitly supports that behavior. Queueing support belongs in the provider evaluation, alongside retries, concurrency, and observability.
Compare a workload, not a headline rate
There is no honest platform-wide “cheapest” winner across these billing systems.
I’d compare the exact model and settings: quality, resolution, duration, output volume, and failure policy. A video-second price and a token formula need to be evaluated against the same output, not placed next to each other as though they were equivalent.
Promotional expiry dates matter too. A temporary rate is not a stable budget assumption.
Keep the direct API option open
If I only need one model creator, require a first-party feature immediately, or have a negotiated enterprise agreement, I’d evaluate the creator’s API directly.
An intermediary is useful when it removes real account-management or integration work. It is not automatically the right layer for every application.
My selection rule
I’d choose by the hardest requirement:
- Custom checkpoints and versioned experiments: Replicate.
- Media throughput and queued generation: fal.ai.
- Google Cloud IAM, regions, and governance: Vertex AI.
- Mixed commercial models with consolidated account management: a cross-provider gateway.
- One creator and first-party requirements: the creator’s own API.
Before committing, I’d shortlist one model for each required modality and run a small, production-shaped workload. Verify the model ID, endpoint, price, region, and asynchronous behavior, then compare output quality, latency, and actual cost.
That exercise tells me more than the length of any model catalog.
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