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+

Cohere's enterprise-optimized model built for advanced Retrieval-Augmented Generation (RAG) and multi-step tool use. Highly effective for multilingual business processes.

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 R+MiniMax M2.7
ProviderCohereMiniMax
Context Window128,000 tokens204,800 tokens
Agent Suitability86/100N/A
Time to First Token (TTFT)350 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-04-042026-03-18

API Pricing Comparison

Input Price per Million Tokens

Command R+

$2.50

MiniMax M2.7

$0.18

Output Price per Million Tokens

Command R+

$10.00

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
7570.0%vs8250.0%
Command R+
MiniMax M2.7
HumanEvalPython coding & logic synthesis
7800.0%vs8000.0%
Command R+
MiniMax M2.7
MATHComplex mathematical problem solving
6200.0%vs5400.0%
Command R+
MiniMax M2.7
GPQAGraduate-level expert reasoning
4200.0%vs3900.0%
Command R+
MiniMax M2.7
HellaSwagCommonsense reasoning and inference
8250.0%vs8400.0%
Command R+
MiniMax M2.7
MT-BenchMulti-turn conversation flow quality
800.0%vs870.0%
Command R+
MiniMax M2.7

Command R+ Quirks & Gotchas

  • โ–ธOptimized for RAG workflows โ€” best enterprise document search model
  • โ–ธTool calling requires explicit step definitions in Cohere's tool-use format

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.