โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

Command R vs MiniMax M2.7

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 MiniMax M2.7.

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.

View Full Specs
MiniMax

MiniMax M2.7

MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...

View Full Specs

Technical Specifications

SpecificationCommand RMiniMax M2.7
ProviderCohereMiniMax
Context Window128,000 tokens204,800 tokens
Agent Suitability78/100N/A
Time to First Token (TTFT)200 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-03-112026-03-18

API Pricing Comparison

Input Price per Million Tokens

Command R

$0.15

MiniMax M2.7

$0.18

Output Price per Million Tokens

Command R

$0.60

MiniMax M2.7

$0.72

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%vs8250.0%
Command R
MiniMax M2.7
HumanEvalPython coding & logic synthesis
7300.0%vs8000.0%
Command R
MiniMax M2.7
MATHComplex mathematical problem solving
5400.0%vs5400.0%
Command R
MiniMax M2.7
GPQAGraduate-level expert reasoning
3500.0%vs3900.0%
Command R
MiniMax M2.7
HellaSwagCommonsense reasoning and inference
7800.0%vs8400.0%
Command R
MiniMax M2.7
MT-BenchMulti-turn conversation flow quality
750.0%vs870.0%
Command R
MiniMax M2.7

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

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.