Groq

Groq is the provider people reach for when they are tired of watching a cursor think. Its whole appeal is speed. In OpenClaw that matters more than it sounds, because an agent is not only writing text. It is deciding, calling tools, retrying, transcribing audio, and staying usable while a real person waits.

The easiest mental model is a pit crew. A pit crew does not win because it gives the longest speech. It wins because every motion is fast, repeatable, and clean under pressure. Groq plays that role well in OpenClaw. It can make an agent feel much sharper, provided you remember that pit crews are for speed, not for writing novels in the garage.

What Groq is actually good at

OpenClaw's Groq provider docs describe Groq as an ultra-fast inference layer for open-weight models on custom LPU hardware. The practical takeaway is simple: Groq is strongest when responsiveness is part of the product, not just a nice bonus.

• Fast chat completions: useful when you want an assistant that feels immediate instead of thoughtful-but-slow.
• Cheap, high-volume support work: a lot of classification, cleanup, extraction, or routing tasks benefit more from speed and price discipline than from flagship polish.
• Audio transcription: OpenClaw's bundled plugin also exposes Groq for speech-to-text, with whisper-large-v3-turbo as the default transcription model.
• Open-weight flexibility: Groq gives you access to Llama, Qwen, GPT OSS, DeepSeek Distill, Compound, and other model families without pretending they are all the same.
• Predictable low-latency workflows: Groq's own architecture story leans hard on deterministic execution and hardware built specifically for inference.

That last point matters. Plenty of providers are fast in marketing copy. Groq's better argument is that speed is the center of the design, not an afterthought. The company describes its LPU architecture as purpose-built for inference, with static scheduling and direct chip coordination aimed at predictable performance rather than opportunistic bursts.

Where Groq fits inside OpenClaw

Groq as the low-latency default

If your agent lives in chat and should respond like a competent human instead of a sleepy committee, Groq is a credible default. OpenClaw ships a bundled Groq plugin and the current provider docs suggest groq/llama-3.3-70b-versatile as the starter default. That is a clue: Groq is meant to be easy to plug in, not a science project.

Groq for background helpers and cheap subagents

Groq is also a good home for the boring middle of agent work. Small cleanup agents, fast tagging, quick summaries, or tool pre-processing often do not need a premium frontier model. They need something quick, steady, and inexpensive enough that you do not resent each retry.

Groq for audio-heavy workflows

This is one of the more useful edges. OpenClaw's Groq integration is not only for chat. The bundled plugin also registers Groq as an audio backend, which means voice messages and speech inputs can move through the same provider family. If your stack mixes text replies with transcription, Groq can simplify the setup.

Groq as one lane in a mixed provider stack

There is also a calmer way to use it: let Groq own speed-sensitive turns, while another provider handles the hardest reasoning or the most delicate writing. That split is often healthier than trying to make one provider prove a philosophical point.

Which Groq model lane to choose

As of May 9, 2026, OpenClaw's provider docs and Groq's official model catalog paint a fairly clear picture. The choice is not really "which Groq model is best?" It is "what kind of work are you routing into Groq?"

If you want the boring recommendation, start with groq/llama-3.3-70b-versatile as the main model and keep a smaller or alternative model behind it for cheaper overflow. Boring is underrated. Boring usually survives production.

What current Groq pricing changes in practice

Groq's official models page currently lists strikingly low prices for several production models. On May 9, 2026, for example, llama-3.1-8b-instant is shown at $0.05 per 1M input tokens and $0.08 per 1M output tokens, while llama-3.3-70b-versatile is listed at $0.59 input and $0.79 output. GPT OSS 20B is shown at $0.075 input and $0.30 output.

The exact numbers will move, so do not build your worldview around them. What matters is the pattern. Groq is attractive when you want fast open-weight inference without paying flagship-provider prices for every routine turn. That makes it especially good for overflow work, agent plumbing, and user experiences where lag is more damaging than a small quality delta.

Rate limits are part of the design, not a footnote

Groq's official rate-limit docs make an important point that applies directly to OpenClaw builders: limits are organization-wide and you can hit requests-per-minute, requests-per-day, tokens-per-minute, or tokens-per-day first depending on the pattern of use. In other words, "fast" does not mean "infinite."

This is the highway analogy Groq users need. A fast car still reaches traffic. If you point every bot, helper, cron job, and transcription task at one provider with no shaping, your elegant low-latency plan turns into a queue. Watch the response headers, keep fallbacks ready, and do not confuse a benchmark with a capacity plan.

How to configure Groq in OpenClaw

The setup is refreshingly plain. Add GROQ_API_KEY, choose a primary model, and keep your routing honest. The OpenClaw docs currently recommend Groq onboarding through the built-in provider plugin and show Groq's base URL as an OpenAI-compatible endpoint, which keeps integration simple.

{
  env: { GROQ_API_KEY: "gsk_..." },
  agents: {
    defaults: {
      model: {
        primary: "groq/llama-3.3-70b-versatile",
        fallbacks: [
          "groq/llama-3.1-8b-instant",
          "openai/gpt-5.4-mini"
        ]
      }
    }
  },
  tools: {
    media: {
      audio: {
        models: [{ provider: "groq" }]
      }
    }
  }
}

This kind of setup lets Groq cover the fast path for both chat and transcription, while leaving you an escape hatch if limits, quality, or workflow fit push you elsewhere.

Reasoning on Groq needs a little nuance

OpenClaw's Groq docs note that reasoning behavior is not uniform across Groq's catalog. GPT OSS models accept mapped reasoning_effort levels. Qwen3 changes behavior depending on whether thinking is enabled. DeepSeek Distill, QwQ, and Compound use Groq's native reasoning surfaces with their own constraints.

Translation: do not assume one reasoning toggle means exactly the same thing everywhere. If a workflow depends on hidden reasoning behavior, chain-of-thought visibility, or structured tool use, test the exact model you intend to ship. A provider namespace is not a guarantee of uniform temperament.

Common mistakes

• Using Groq everywhere just because it feels fast: low latency is a feature, not a religion.
• Ignoring rate limits: quick models still bottleneck when too many workflows pile in.
• Choosing the smallest model for everything: cheap cleanup work and higher-trust orchestration are not the same job.
• Skipping workflow testing: a fast answer in chat says very little about tool behavior, formatting discipline, or multi-step reliability.
• Forgetting audio is available: Groq is more useful when you treat transcription as part of the stack, not a separate afterthought.

A sensible default recommendation

Groq is an excellent choice when you care about responsiveness, cost-aware open-weight models, or transcription speed. It is especially strong for assistants that should feel lively, helper agents that do a lot of repetitive work, and stacks where latency affects user trust.

The sensible version is not "move everything to Groq." It is "give Groq the jobs where speed pays rent." That distinction is the difference between a sharp stack and a fashionable one.