oMLX vs Speko: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of oMLX and Speko — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
Speko
Speko
A router for voice AI that benchmarks 56 speech and language models language by language, then routes each session to the one that wins.
Key features
- 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.
Best for
- 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.