Cohere vs Groq
Cohere and Groq are both Large Language Models tools. Compare features, pricing, and ratings below to find the best fit for your team.
When to Choose Cohere vs Groq
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Embed API indexes internal wikis and runbooks. Queries return semantically relevant results under 500ms - replacing keyword search across the entire knowledge base.
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.
Command R+ handles 25+ languages in a single deployment. Fine-tuning on company-specific terms ensures consistent tone and accuracy across all regional support queues.
Chain dense retrieval into Cohere's ranking stage. A SaaS processing 2M daily queries dropped average response time from 1.8s to 0.6s without adding infrastructure.
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.
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.
Pricing Comparison & PlansHigh· Verified May 30, 2026
Cohere Plans
Free tier available
Command R7B (12-2024)
$0.04/per 1M input tokens- ✓Generative model
- ✓Output: $0.15 per 1M tokens
Command R (08-2024)
$0.15/per 1M input tokens- ✓Generative model
- ✓Output: $0.60 per 1M tokens
Rerank 2
$1/per 1K searches- ✓Reranking capabilities
Command R+ (08-2024)
$2.5/per 1M input tokens- ✓Generative model
- ✓Output: $10.00 per 1M tokens
Command A
$2.5/per 1M input tokens- ✓Generative model
- ✓Output: $10.00 per 1M tokens
Classify fine-tuning
$2.5/per 1K classifications- ✓Fine-tuning for classification tasks
Fine-tuning (Custom Models)
$3/per 1M training tokens- ✓Create custom models
Groq Plans
Free tier available
Free
FreeBest for: 14,400 req/day is enough for dev and low-traffic apps - start here before paying anything.
- ✓Build and Test on Groq APIs
- ✓Community Support
- ✓Zero-data Retention Available
Developer
Contact Sales- ✓Build and Test on Groq APIs
- ✓Community Support
- ✓Zero-data Retention Available
- ✓Higher Token Limits
- ✓Chat Support
Enterprise
Contact Sales- ✓Build and Test on Groq APIs
- ✓Community Support
- ✓Zero-data Retention Available
- ✓Higher Token Limits
- ✓Chat Support
Capability Breakdown
16 differences found across 33 standardized features
- •Command R+ model
- •Embed API
- •Rerank API
- •RAG toolkit
- •Multi-lingual
- •Fine-tuning
- •Deployment flexibility
- •Enterprise security
- •On-premise option
- •Connectors
- •Tool use
- •Structured outputs
- •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
Strengths & Limitations
Evaluative strengths and weaknesses: not feature lists
- +Command R+ model is optimized for enterprise RAG and tool use
- +State-of-the-art multilingual embedding models (Embed v3)
- +Dedicated Rerank model significantly improves search relevance
- +Designed for private data deployment on any cloud or on-prem
- +API-first approach with SDKs for Python, Go, Node, and Java
- −Fewer open-source or fine-tunable models compared to competitors
- −Limited native integrations beyond major cloud providers (AWS, Oracle)
- −Less focus on creative or general-purpose consumer applications
- −Observability tools for embedding and API usage can be basic
- −Steeper learning curve for those not focused on RAG pipelines
- +World's fastest inference speed (500+ tokens/sec)
- +Custom LPU hardware eliminates sequential processing bottlenecks
- +OpenAI-compatible API for seamless, drop-in integration
- +Predictable, low-latency performance regardless of load
- +Generous free tier for development and testing
- −Very limited selection of open-source models (no GPT-4, Claude)
- −No support for fine-tuning or custom model hosting
- −Lacks advanced features like function calling or JSON mode on some models
- −Rate limits can be a bottleneck for high-throughput applications
- −Newer hardware, less proven for enterprise-scale reliability
At a Glance
Recent Price History
Cohere removed the "Enterprise" plan
Plan removed · May 30, 2026
Cohere removed the "Production" plan
Plan removed · May 30, 2026
Cohere removed the "Developer (Trial)" plan
Plan removed · May 30, 2026
Cohere added a new "Fine-tuning (Custom Models)" plan at $3/mo
Plan added · May 30, 2026
Cohere added a new "Classify fine-tuning" plan at $2.5/mo
Plan added · May 30, 2026
Groq added a new "Developer" plan (Custom pricing)
Plan added · May 30, 2026
Groq removed the "Pay-as-you-go" plan
Plan removed · May 30, 2026
Groq added a new "Enterprise" plan
Plan added · May 21, 2026
Frequently Asked Questions
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- 1.Official Pricing Page·Source of verified tiers(Checked: 2026-05-30)
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Sources & Data Trail · Groq
- 1.Official Pricing Page·Source of verified tiers(Checked: 2026-05-30)
- 2.Official Website·Official vendor website
- 3.G2·G2 verified reviews · 4.7/5 · 915 reviews
- 4.PeerSpot·PeerSpot enterprise peer reviews