Command R vs Gemini 3.1 Pro
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 Gemini 3.1 Pro.
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.
Gemini 3.1 Pro
Google's premiere multi-modal model featuring a massive 2 million token context window. Engineered for deep code analysis, video indexing, and long-context reasoning.
Technical Specifications
| Specification | Command R | Gemini 3.1 Pro |
|---|---|---|
| Provider | Cohere | |
| Context Window | 128,000 tokens | 2,000,000 tokens |
| Agent Suitability | 78/100 | 93/100 |
| Time to First Token (TTFT) | 200 ms | 420 ms |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-03-11 | 2026-04-20 |
API Pricing Comparison
Input Price per Million Tokens
Command R
$0.15
Gemini 3.1 Pro
$2.00
Output Price per Million Tokens
Command R
$0.60
Gemini 3.1 Pro
$12.00
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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.
Command R Quirks & Gotchas
- ▸Cost-effective RAG model — strong multilingual search performance
- ▸Limited agentic capability — use Command R+ for complex multi-step tool use
Gemini 3.1 Pro Quirks & Gotchas
- ▸Best model for massive context — 2M token window is class-leading
- ▸Tool calling requires explicit schema definition in Google AI Studio