FreeLLMAPI vs oMLX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of FreeLLMAPI and oMLX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
FreeLLMAPI
Tashfeen Ahmed
Self-hostable router that aggregates free tiers from 34 LLM providers and 635 free model endpoints behind one OpenAI-compatible /v1 API.
Key features
- Unified OpenAI-Compatible Endpoint: Exposes 635 free model endpoints from 34 providers behind a single /v1 API that existing OpenAI clients can use unchanged.
- Smart Model Router: Selects the best available model per request and automatically falls over to another provider when one returns a rate limit.
- Per-Key Quota Tracking: Monitors usage against each provider's free tier cap so requests are spread out and no individual key is exhausted.
- Encrypted Key Storage: Provider API keys are stored encrypted rather than in plaintext configuration files.
- Custom Endpoint Support: Any additional OpenAI-compatible chat, embedding, image or audio endpoint can be registered alongside the built-in providers.
- Self-Hosting via Docker: Ships as a container image on GHCR with a Docker Compose setup, so the gateway runs entirely on your own infrastructure.
- Desktop and Mobile Apps: Native builds for macOS and Windows plus an Android app on Google Play for running the router outside a server.
- Signed Live Catalog: The model catalog updates itself from a signed feed, so new free models and quota changes arrive without pulling new code.
Best for
- Zero-Cost Prototyping: Build and test an LLM application against free provider tiers before committing to a paid API contract.
- Rate Limit Resilience: Keep a coding agent or chatbot running through provider rate limits by automatically failing over to another free endpoint.
- Coding Agent Backend: Point Cursor, Claude Code or any OpenAI-compatible CLI at a single local endpoint instead of juggling provider keys per tool.
- Model Comparison: Evaluate responses across hundreds of models from different labs through one consistent API surface.
- Private Gateway Deployment: Self-host the router inside a network so provider keys and prompts never pass through a third-party proxy.
- Multi-Provider Key Management: Consolidate scattered free-tier accounts into one encrypted store with visibility into remaining quota.
oMLX
Jun Kim
An open-source native macOS LLM inference server built on MLX whose paged SSD KV cache drops coding-agent time-to-first-token from 30-90s to under 5s.
Key features
- Paged SSD KV Caching: Cache blocks persist to disk in safetensors format with hot blocks in RAM and cold blocks on SSD, so previously seen prefixes are restored in milliseconds and survive server restarts.
- Sub-5s Agent TTFT: Cuts time-to-first-token for coding agents from 30-90 seconds down to under 5 seconds from the second turn onward.
- Continuous Batching: Handles concurrent requests through mlx-lm's BatchGenerator, measured at up to 4.14x generation speedup at 8x concurrency.
- OpenAI and Anthropic Drop-In API: Serves both OpenAI-compatible endpoints and a native Anthropic /v1/messages endpoint so Claude Code, OpenClaw, and Cursor connect without adapters.
- Multi-Model Serving: Loads LLM, VLM, embedding, and reranker models at the same time with LRU eviction when memory is constrained.
- Native Menu Bar App: A signed and notarized macOS app with in-app auto-update to start, stop, and monitor the server, plus a web dashboard for model management and live metrics.
- Tool Calling and MCP: Supports JSON, Qwen, Gemma, GLM, and MiniMax tool-calling formats with MCP integration and configurable trimming of oversized tool results.
- Config Command Generation: The dashboard emits the exact configuration command for each supported client tool.
Best for
- Local Coding Agents: Run Claude Code or OpenClaw entirely against a local model without the 90-second waits that make local inference impractical for agents.
- Private Codebase Work: Keep proprietary source on-device by pointing an OpenAI-compatible IDE assistant at a local endpoint.
- Offline Development: Continue agent-assisted coding without network access or per-token API costs.
- Model Benchmarking: Compare Qwen3.5-122B, Qwen3-Coder-Next, MiniMax-M2.5, and GLM-5 throughput on the same Apple Silicon hardware.
- Multi-Client Serving: Serve several concurrent agent sessions from one Mac using continuous batching rather than queuing behind a single request.
- RAG on a Mac: Host an LLM alongside embedding and reranker models in a single process for local retrieval pipelines.