GroqvsBark

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

Groq

LLM APIs & Inference

The fastest AI inference platform — LPU-powered, 1000+ tokens/sec

Bark

Voice & Audio

Text-to-audio model supporting speech, music, and sound effects

FeatureGroqBark
CategoryLLM APIs & InferenceVoice & Audio
PricingFree tier available, pay-per-token for productionFree (open-source)
GitHub Stars
More stars
36k
PlatformsWebLinux, macOS, Windows
Key Features
  • LPU hardware — custom chips for inference, not repurposed GPUs
  • GPT OSS 120B at 500 tok/s ($0.15/M input)
  • GPT OSS 20B at 1000 tok/s ($0.075/M input)
  • Llama 4 Scout 17B at 750 tok/s with 131K context + vision
  • Qwen3-32B at 400 tok/s with 131K context
  • Compound AI systems with web search + code execution
  • Whisper transcription ($0.04-0.11/hour)
  • OpenAI-compatible API — drop-in replacement
  • Free developer tier: 250-300K TPM, 1K RPM
  • Text-to-speech
  • Music generation
  • Sound effects
  • Multi-language
  • Voice presets
Pros
  • + Fastest inference available (500-1000 tok/s)
  • + Free tier with generous limits (250K+ tokens/min)
  • + OpenAI-compatible API — swap one line of code
  • + Latest open-source models (GPT OSS, Llama 4, Qwen3)
  • + Compound AI for agentic workflows (search + code exec)
  • + Speech + music + sound effects
  • + Multi-language support
  • + Speaker presets
  • + Open-source
  • + Unique capabilities
Cons
  • Cloud-only — cannot self-host LPU hardware
  • Rate limits on free tier (1K RPM)
  • Smaller model catalog than running locally via Ollama
  • Slower than ElevenLabs
  • Lower quality than commercial TTS
  • Requires GPU
  • No real-time streaming
Tags
inferencefastfreehardware
audiottsmusicopen-source

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