FreeLLMAPI vs Speko: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of FreeLLMAPI and Speko — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

FreeLLMAPI

Tashfeen Ahmed

Freemium

Self-hostable router that aggregates free tiers from 34 LLM providers and 635 free model endpoints behind one OpenAI-compatible /v1 API.

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.

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.

Freemium

A router for voice AI that benchmarks 56 speech and language models language by language, then routes each session to the one that wins.

Language-by-Language Benchmarks: 56 speech and language models measured across ten languages, exposing that a model topping the English table may have no published rank anywhere else.
Cost-Versus-Accuracy Charts: Word error rate plotted against dollars per minute for each pipeline stage — STT, LLM, TTS and speech-to-speech — so a model choice is a priced trade-off, not a guess.
Managed Router: A hosted, provider-neutral data plane at router.speko.dev that selects the winning model per session using the measurements and fails over before a response is returned.
Typed Contracts: Public OpenAPI and AsyncAPI specifications for the Router, so integrations are generated rather than hand-rolled against an undocumented endpoint.
Open Gateway Runtime: A customer-side runtime with native LiveKit and Pipecat services offering provider-direct streaming and local BYOK credentials, keeping your vendor relationships intact.
Drop-In Framework Integration: Swap STT, LLM and TTS in a LiveKit AgentSession for Speko equivalents with credential_source="auto" and model="auto" and get routing without restructuring the agent.
MCP Server: Point Claude Code or Cursor at mcp.speko.ai to query models, voices and benchmark data from inside a coding agent.
Two Pricing Paths: Either add 5% on top of a provider's published rate while routing your own spend, or take the bundled Speko infrastructure rate of $0.09 per minute across all three legs.

Multilingual Voice Products: Ship a voice agent to non-English markets and route each language to the model that actually performs best there.
Vendor Selection Research: Use the published benchmarks to decide which STT or TTS provider to sign with before committing to a contract.
Voice Cost Optimization: Trade a small accuracy delta for a large cost reduction by picking a model at the right point on the WER-versus-price curve.
Failover and Reliability: Keep voice sessions alive through a provider outage with pre-response failover across the routed model pool.
LiveKit or Pipecat Migration: Add measured routing to an existing agent stack by swapping in Gateway services rather than rewriting the pipeline.
Agent-Assisted Model Research: Query the benchmark data from Claude or Cursor over MCP while writing the voice integration.