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

Command R vs R1 0528

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 R1 0528.

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
DeepSeek

R1 0528

May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...

View Full Specs

Technical Specifications

SpecificationCommand RR1 0528
ProviderCohereDeepSeek
Context Window128,000 tokens163,840 tokens
Agent Suitability78/100N/A
Time to First Token (TTFT)200 msN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-03-112025-05-28

API Pricing Comparison

Input Price per Million Tokens

Command R

$0.15

R1 0528

$0.50

Output Price per Million Tokens

Command R

$0.60

R1 0528

$2.15

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
R1 0528
HumanEvalPython coding & logic synthesis
7300.0%vsN/A
Command R
R1 0528
MATHComplex mathematical problem solving
5400.0%vsN/A
Command R
R1 0528
GPQAGraduate-level expert reasoning
3500.0%vsN/A
Command R
R1 0528
HellaSwagCommonsense reasoning and inference
7800.0%vsN/A
Command R
R1 0528
MT-BenchMulti-turn conversation flow quality
750.0%vsN/A
Command R
R1 0528

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

R1 0528 Quirks & Gotchas

No developer gotchas reported.