Groq vs Mistral Small
- ✦ Ultra-Fast Inference
- ✦ Llama 3 Models
- ✦ Mixtral Models
- ✦ Efficient Architecture
- ✦ Instruction Following
- ✦ Code Generation
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Groq's LPU delivers Llama 3 inference at 750+ tokens per second, enabling pipelines where Whisper transcription feeds directly into an LLM analysis step with a total round-trip under 500ms.
Mistral Small's sub-$1/million token pricing makes it practical for high-volume classification pipelines that require LLM-quality reasoning but cannot justify frontier model costs.
Groq's time-to-first-token under 100ms enables natural-feeling voice conversational interfaces where LLM response latency is the bottleneck, not TTS or ASR.
Mistral Small's function calling produces well-structured JSON tool calls with lower latency than larger models, making it a cost-efficient backbone for agent frameworks that make dozens of tool calls per session.
Groq's per-token cost on Llama 3 8B is under $0.06 per million tokens, making high-volume classification or extraction tasks that previously required GPU servers economically viable via API.
Mistral Small's open weights run on a single A10G GPU, enabling LLM capability in air-gapped or data-sovereign environments where cloud API calls are prohibited.
Pricing Intelligence
Groq Plans
Free tier available
- • Rate-limited free access
- • All supported models
- • API access
- • ~$0.05/1M tokens (Llama)
- • No monthly fee
- • Higher rate limits
Mistral Small Plans
Paid plans only
- • $0.1/M input tokens
- • $0.3/M output tokens
- • One of cheapest APIs
- • Open weights available
- • Apache 2.0 license
- • Commercial use allowed
Feature Matrix
6 differences found across 15 standardized features
- •Ultra-Fast Inference
- •Llama 3 Models
- •Mixtral Models
- •Gemma Models
- •OpenAI-Compatible API
- •Function Calling
- •JSON Mode
- •Streaming
- •Tool Use
- •Low Latency
- •High Throughput
- •Free Tier
- •Python SDK
- •JavaScript SDK
- •LPU Hardware
- •Efficient Architecture
- •Instruction Following
- •Code Generation
- •Function Calling
- •JSON Mode
- •Streaming
- •32k Context
- •Open Weights
- •Commercial License
- •API Access
- •Multilingual
- •Low Latency
- •RAG Optimized
- •Tool Use
- •Safety Features
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +Fastest LLM inference available (~500 tokens/sec)
- +OpenAI-compatible API (easy migration)
- +Generous free tier
- +Very low latency
- −Limited model selection vs OpenAI
- −Not suitable for fine-tuning
- −Rate limits on free tier can be restrictive
- +Very affordable API pricing
- +Open weights with commercial license
- +Strong performance for its size
- +Good for high-volume applications
- −Less capable than Mistral Large for complex tasks
- −Limited context compared to GPT-4o
- −Smaller ecosystem
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.Groq Official Pricing–Vendor pricing page
- 2.Mistral Small Official Pricing–Vendor pricing page
- 3.Groq Official Website–Official product website
- 4.Mistral Small Official Website–Official product website