OpenJarvisvsGroq

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

OpenJarvis

AI Agent Frameworks

Local-first personal AI agents that run with Ollama

Groq

LLM APIs & Inference

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

FeatureOpenJarvisGroq
CategoryAI Agent FrameworksLLM APIs & Inference
PricingFree (open-source)Free tier available, pay-per-token for production
GitHub Stars
More stars
5k
PlatformsmacOS, Linux, Windows, WSL2, DockerWeb
Key Features
  • Local-first personal AI agents
  • Built-in Ollama support
  • Morning briefing preset
  • Deep research across web and local documents
  • Code assistant preset
  • Local engines: Ollama, vLLM, SGLang, llama.cpp
  • Optional cloud engines
  • Energy, cost and latency-aware routing
  • 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
Pros
  • + Strong fit for Ollama-based local agent workflows
  • + Apache-2.0 open-source project
  • + Ships ready-to-run presets instead of only framework primitives
  • + Supports both local engines and optional cloud escalation
  • + Built around privacy, cost, latency and energy as first-class constraints
  • + 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)
Cons
  • Young v1.0 project with fast-moving docs and releases
  • Local-first does not mean cloud-free unless configured that way
  • Personal-agent presets may need access to sensitive local files, email or calendar data
  • Efficiency claims are project-reported and should be tested on your own workloads
  • Cloud-only — cannot self-host LPU hardware
  • Rate limits on free tier (1K RPM)
  • Smaller model catalog than running locally via Ollama
Tags
open-sourcelocal-firstpersonal-aiagentsollamalocal-airesearchpython
inferencefastfreehardware

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