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Score Breakdown
How Groq Scores
Pricing Tiers
Groq Pricing Breakdown
Smallest models, fast inference entry point.
- Llama 3 8B model
- $0.05 input / $0.10 output
- 1,200 tokens/second
- OpenAI-compatible API
- Batch processing available
70B models, best performance/cost balance.
- Llama 3 70B model
- $0.59 input / $0.79 output
- 1,200 tokens/second
- Prompt caching (50% discount)
- Batch processing 50% off
Largest open-source models, top accuracy.
- Llama 3.3 70B model
- $0.88 input / $1.06 output
- 750 tokens/second
- Speech-to-text $0.04/hr
- Text-to-speech $50/M chars
What We Like and Don't
What We Like
- + Blazingly fast inference: 1,200 tokens/second on Llama 3 8B. 20x faster than typical GPU providers. LPU architecture is genuinely novel.
- + Cost-effective at scale: Batch processing and prompt caching both offer 50% discounts. High-volume inference costs 60-70% less than OpenAI.
- + OpenAI-compatible API: Drop-in replacement. Just change base_url to groq and use your existing code.
- + Multimodal support: Text, speech-to-text, text-to-speech. Real-time voice applications are now viable.
- + Open-source model library: Llama, Mistral, Qwen, DeepSeek. If you're committed to open models, Groq is unbeatable for speed.
What We Don't
- − No proprietary models: No GPT-4, Claude, or Gemini. Limited to open-source LLMs. This is a major limitation for teams needing state-of-the-art reasoning.
- − Hardware constraints: GroqRack is expensive for on-premise deployment. GroqCloud GPU options (H100/H200) are pricey for continuous inference.
- − No free tier: Unlike OpenAI and Anthropic, Groq doesn't offer a generous free tier. Startups must pay from day one.
- − Smaller context window: Llama 3 70B has 8K context. Larger contexts mean more tokens billed, increasing costs for long documents.
Feature Deep Dive
What is Groq?
Groq is a specialist AI inference company that uses proprietary LPU (Language Processing Unit) chips instead of GPUs. LPUs are designed specifically for LLM inference, delivering deterministic, low-latency completions—1,200 tokens per second on Llama 3 8B, compared to 60 tokens/second on typical GPU providers. Groq markets itself to teams that need speed over the latest model capabilities.
Core Capabilities
LPU Architecture: Groq's LPUs eliminate memory bandwidth bottlenecks that plague GPUs. No context switching, no thrashing—just deterministic inference at scale. Ideal for real-time chat, streaming, and live transcription where latency matters more than token accuracy.
Throughput Leadership: Llama 3 8B: 1,200 tokens/sec. Llama 3 70B: 750 tokens/sec. Qwen 72B: 900 tokens/sec. These are real measurements, not marketing claims. For comparison, OpenAI's GPT-4 Turbo averages 60-80 tokens/sec in production.
OpenAI-Compatible API: Drop-in compatible. Clients using OpenAI SDK just change the base URL and API key. Code migration is minutes, not days.
Unique Features
Batch Processing Discount: Submit multiple requests as a batch, get 50% off. Perfect for non-real-time workloads (data analysis, report generation) where latency doesn't matter.
Prompt Caching: Cache long system prompts or context chunks, get 50% off cached tokens on subsequent requests. Great for retrieval-augmented generation (RAG) where the same context is queried multiple times.
Speech Capabilities: Speech-to-text at $0.04/hour. Text-to-speech at $50/million characters. Real-time voice agents are now buildable without external speech APIs.
Model Library
Groq supports Llama 3 (8B and 70B), Llama 4 Scout, Mistral models, Qwen 72B, and DeepSeek. All deployed on LPU hardware. No custom fine-tuning available—Groq provides models as-is.
Performance Characteristics
Latency: First token in 50-100ms. Time-to-first-token (TTFT) is sub-100ms even for 70B models. Throughput: 750-1,200 tokens/second depending on model. Concurrency: GroqCloud supports thousands of concurrent requests per API key. Uptime: 99.9% SLA on production endpoints.
Best Use Cases
Who It's Best For & Who Should Skip
Best For
- Speed-focused teams: If latency below 100ms is a requirement, Groq is the only choice. Real-time chat, live transcription, interactive agents.
- Open-source LLM enthusiasts: Running Llama 3, Mistral, or Qwen? Groq's LPU inference is 10-20x faster than self-hosted, and cheaper than alternatives.
- High-volume, cost-sensitive workloads: Batch processing and caching discounts make Groq 40-60% cheaper than OpenAI for large-scale inference.
- Voice-first applications: Built-in speech-to-text and text-to-speech, with sub-100ms latency. Voice agents are cheaper and faster than with separate APIs.
- Enterprises committed to open models: No vendor lock-in. Deploy Llama on-premise with GroqRack or keep using GroqCloud. Flexibility matters.
Who Should Skip It
- Teams needing GPT-4 or Claude: Groq doesn't support proprietary models. If you need state-of-the-art reasoning, look elsewhere.
- Accuracy-first use cases: Open-source models are catching up, but GPT-4 and Claude still lead on complex reasoning and edge cases. Medical diagnosis, legal analysis, etc. require the best models.
- Projects with tiny budgets: No free tier. OpenAI's free trial or Anthropic's credits are more generous for bootstrapped startups.
- Long-context requirements: Llama 3 70B maxes out at 8K context. If you need 100K+ context windows (long documents, RAG at scale), Claude or GPT-4 Turbo win.
Alternatives to Groq
User Reviews
Frequently Asked Questions
Ready to Try Groq?
Start with Groq's free trial. If you need sub-100ms latency for chat, real-time voice, or high-volume inference, Groq is unmatched. OpenAI-compatible API makes migration seamless.