Comparative Experience of Mainstream Large Model API Aggregation Platforms

Comparative Experience of Mainstream Large Model API Aggregation Platforms

Recently, I've had the chance to explore various mainstream large model API aggregation platforms due to a project requirement. I wanted to share my insights and experiences, which I hope will help anyone looking to integrate large model APIs.

1. OpenRouter: openrouter.ai Model Coverage: Supports 400+ models, including popular ones like GPT, Claude, Gemini, Grok, Qwen, DeepSeek, Llama, and Mistral. Service Stability: Overall, open-source models are quite stable, while closed-source models tend to be less reliable, with occasional rate limits, request failures, or unexplained errors. One notable issue is that OpenRouter may disconnect during long inference tasks, especially when handling complex requests or longer response times. I haven't found a stable workaround for this yet. Pricing: Generally aligns with the original model pricing. User Experience: It's user-friendly; after creating an API key, you can use it in an OpenAI-compatible manner. Payment is possible via Visa, and I've been informed that selecting one-time payment options allows for WeChat and Alipay.

1. Gptproto: gptproto.com Model Coverage: Supports 200+ models, including major ones like GPT, Claude, Gemini, Grok, Qwen, DeepSeek, Llama, and Mistral. Service Stability: Both open-source and closed-source models have reliable stability. When errors occur, customer support is usually quick to resolve them, making it one of my go-to API platforms. Pricing: Depends on the group; some can be significantly cheaper than the original model prices. User Experience: Easy to use; you can call it through an OpenAI-compatible interface after creating an API key. It offers a variety of payment options, making it accessible. The customer service is responsive and helpful when issues arise.

1. Fal: fal.ai Model Coverage: Primarily focuses on image and video generation models like FLUX, Stable Diffusion, SDXL, LoRA, image upscaling, background removal, and image-to-video tasks. While it covers visual generation well, its large language model support is average. Service Stability: Image generation tasks are generally stable, but longer video generation tasks might have queues or wait times, so asynchronous calls are recommended. Pricing: Charges based on models and tasks; standard image generation is reasonably priced, but video generation and high-definition tasks can be more expensive, suitable for on-demand usage. User Experience: After creating an API key, you can access it easily. Documentation and examples are clear, allowing for quick onboarding, though it's not OpenAI-compatible, requiring separate adjustments for different model parameters.

1. Fireworks.ai: fireworks.ai
   
   Model Coverage: Focuses mainly on open-source large models like Llama, Qwen, DeepSeek, Mistral, Mixtral, and Gemma—it's more of an LLM inference platform with decent model coverage.
   
   Service Stability: Generally stable with good speed; routine calls usually go smoothly, although there might be slight delays during peak times or with larger models.
   
   Pricing: Billed per token; open-source models generally offer competitive pricing, cheaper than many closed-source options, but high-demand or dedicated deployments can be pricier.
   
   User Experience: Supports an OpenAI-compatible interface, making integration straightforward—just change the base URL to get started. The documentation is clear, suitable for chat, RAG, agent tasks, and code generation.
   

2. WaveSpeed.ai: wavespeed.ai
   
   Model Coverage: Primarily an image/video generation API platform, not just an LLM platform. It features many models for text-to-image, image-to-image, and video generation, ideal for AIGC image and video functionalities.
   
   Service Stability: Positioned for high-speed inference, speed is a key selling point. However, image and video tasks can be resource-intensive, so expect possible queues or delays during peak times. It’s advisable to test actual business scenarios for success rates and output speed.
   
   Pricing: Usually billed per task or generation count, differing from pure token billing typical of LLMs. Image costs are generally manageable, while video generation can be pricier, depending on the model, resolution, duration, and concurrency needs.
   
   User Experience: For image/video generation APIs, the integration is straightforward, making it easy to incorporate AIGC capabilities into products. Before full deployment, it’s essential to test three key aspects: generation speed, failure retries, and the stability of different model outputs.

Summary:

Overall, OpenRouter excels in model coverage, while WaveSpeed.ai is better suited for image/video generation. If you’re looking for a user-friendly platform with extensive model options and friendly costs for everyday project use, I highly recommend Gptproto.

Note:

When using third-party proxy platforms, carefully verify the models being called—some platforms might misrepresent what's being offered. For instance, when calling the so-called DeepSeek-V3 model on OpenRouter, it repeatedly claimed to be an OpenAI model, while on other platforms, it accurately identified itself as DeepSeek. Additionally, try to choose models with clear version numbers and release dates; otherwise, it can be challenging to confirm which version your requests are routed to, impacting consistency and control over model performance.