Command R+ vs Gemini 3.1 Flash
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 Flash.
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.
Gemini 3.1 Flash
Gemini 3.1 Flash is Google's high-speed, cost-efficient multimodal model in the 3.1 generation, purpose-built for high-volume content synthesis, classification, and intelligent routing at scale. Featuring a 1-million-token context window, it can process large batches of documents, customer data, or multimedia content in a single inference pass, dramatically reducing pipeline complexity. At just $0.25/MTok for input, it is one of the most affordable routes to Google-caliber multimodal AI, making it an ideal backbone for production pipelines, data enrichment workflows, and high-frequency API integrations.
Technical Specifications
| Specification | Command R+ | Gemini 3.1 Flash |
|---|---|---|
| Provider | Cohere | |
| Context Window | 128,000 tokens | 1,000,000 tokens |
| Agent Suitability | 86/100 | 86/100 |
| Time to First Token (TTFT) | 350 ms | 150 ms |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-04-04 | 2026-04-20 |
API Pricing Comparison
Input Price per Million Tokens
Command R+
$2.50
Gemini 3.1 Flash
$0.25
Output Price per Million Tokens
Command R+
$10.00
Gemini 3.1 Flash
$1.50
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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
- ▸Optimized for RAG workflows — best enterprise document search model
- ▸Tool calling requires explicit step definitions in Cohere's tool-use format
Gemini 3.1 Flash Quirks & Gotchas
- ▸Most cost-effective Google model — ideal for high-volume pipelines
- ▸Context caching available via Vertex AI for repeated document processing