The Unified Gateway for AI Models
Access leading AI models through one unified, OpenAI-compatible API.
curl https://aihubmix.com/v1/chat/completions \-H "Authorization: Bearer sk-***" \-H "Content-Type: application/json" \-d '{"model": "gpt-5.5","messages": [{"role":"user","content":"Hi"}]}' # claude-opus-4-7 / gemini-3.1-pro-preview
Apps Using AIHubMix
Discover apps using AIHubMix. Eligible usage receives 10% off, excluding Claude models.Model Rankings
Recent Blog Posts
Migrating from Claude Haiku 4.5 to 5.5: Five 400 Errors and the Quiet Changes
Changing claude-haiku-4-5 to claude-haiku-5-5 is the smallest part of this migration. Five request patterns that worked on Haiku 4.5 now return a 400 error, and several more changes fail no request but alter what you get back, what it costs, or how the model behaves inside an agent. Anthropic says existing Haiku 4.5 prompts should work well on Haiku 5.5 without changes. The request code around those prompts is a different story. This post lists each problem as you'll meet it: what you'll see,
Claude Haiku 5.5 Pricing: The 100K Line Behind the 90% Cut
Claude Haiku 5.5 costs $0.10 per million input tokens and $0.50 per million output tokens, a tenth of Haiku 4.5's $1 and $5. That holds for prompts up to 100,000 tokens. Above that line, the whole request is billed at $0.50 and $2.50, which is half of Haiku 4.5's price rather than a tenth. Anthropic puts the typical saving at around 75%, not 90%, and the difference comes from three things this post works through: where your prompts fall relative to the 100K line, how many thinking tokens the d
Claude Haiku 5.5 Effort Levels: Medium Is the Default, Low Is Often Enough
Claude Haiku 5.5 is the first Haiku with an effort setting, and the setting moves the bill more than anything else you control. In Artificial Analysis's independent runs, Haiku 5.5 at max effort scored 43 on its Intelligence Index and at low effort scored 29. Max also used 440 million output tokens to get through the index; low used 32 million. So the short answer: leave most work at the default, medium. Drop high-volume, simple routes to low. Raise knowledge work and strict instruction follow
