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Claude Opus 4 vs Command R

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 Claude Opus 4 and Command R.

Anthropic

Claude Opus 4

Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in...

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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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Technical Specifications

SpecificationClaude Opus 4Command R
ProviderAnthropicCohere
Context Window200,000 tokens128,000 tokens
Agent SuitabilityN/A78/100
Time to First Token (TTFT)N/A200 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-05-222024-03-11

API Pricing Comparison

Input Price per Million Tokens

Claude Opus 4

$15.00

Command R

$0.15

Output Price per Million Tokens

Claude Opus 4

$75.00

Command R

$0.60

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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
9050.0%vs7100.0%
Claude Opus 4
Command R
HumanEvalPython coding & logic synthesis
9300.0%vs7300.0%
Claude Opus 4
Command R
MATHComplex mathematical problem solving
8540.0%vs5400.0%
Claude Opus 4
Command R
GPQAGraduate-level expert reasoning
7230.0%vs3500.0%
Claude Opus 4
Command R
HellaSwagCommonsense reasoning and inference
9650.0%vs7800.0%
Claude Opus 4
Command R
MT-BenchMulti-turn conversation flow quality
945.0%vs750.0%
Claude Opus 4
Command R

Claude Opus 4 Quirks & Gotchas

No developer gotchas reported.

Command R Quirks & Gotchas

  • β–ΈCost-effective RAG model β€” strong multilingual search performance
  • β–ΈLimited agentic capability β€” use Command R+ for complex multi-step tool use