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Command R vs Mistral Nemo

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Command R and Mistral Nemo.

Cohere

Command R

Command R is Cohere's lightweight, cost-efficient model engineered for high-speed enterprise integrations, productivity automation, and retrieval-augmented generation (RAG) pipelines. Optimized for low-latency API tool use and structured JSON output, it is particularly effective in enterprise search and question-answering systems where fast, reliable responses are critical. With a 128,000-token context window and a price of $0.15/MTok for input, Command R provides strong RAG performance and multilingual support at a fraction of the cost of Command R+, making it the preferred choice for teams scaling intelligent document retrieval at high request volumes.

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Mistral

Mistral Nemo

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...

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Technical Specifications

SpecificationCommand RMistral Nemo
ProviderCohereMistral
Context Window128,000 tokens131,072 tokens
Agent Suitability78/100N/A
Time to First Token (TTFT)200 msN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-03-112024-07-19

API Pricing Comparison

Input Price per Million Tokens

Command R

$0.15

Mistral Nemo

$0.02

Output Price per Million Tokens

Command R

$0.60

Mistral Nemo

$0.03

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
7100.0%vsN/A
Command R
Mistral Nemo
HumanEvalPython coding & logic synthesis
7300.0%vsN/A
Command R
Mistral Nemo
MATHComplex mathematical problem solving
5400.0%vsN/A
Command R
Mistral Nemo
GPQAGraduate-level expert reasoning
3500.0%vsN/A
Command R
Mistral Nemo
HellaSwagCommonsense reasoning and inference
7800.0%vsN/A
Command R
Mistral Nemo
MT-BenchMulti-turn conversation flow quality
750.0%vsN/A
Command R
Mistral Nemo

Command R Quirks & Gotchas

  • โ–ธCost-effective RAG model โ€” strong multilingual search performance
  • โ–ธLimited agentic capability โ€” use Command R+ for complex multi-step tool use

Mistral Nemo Quirks & Gotchas

No developer gotchas reported.