OpenAI o4-mini is the newest lightweight model in the o-series, engineered for efficient and capable reasoning across text and visual tasks. Optimized for speed and performance, it excels in code generation and image-based understanding, while maintaining a balance between latency and reasoning depth. The model supports a 200,000-token context window with up to 100,000 output tokens, making it suitable for extended, high-volume interactions. It handles both text and image inputs, producing textual outputs with advanced reasoning capabilities. With its compact architecture and versatile performance, o4-mini is ideal for a wide array of real-world applications demanding fast, cost-effective intelligence.
Command R+ is Cohere’s cutting-edge generative AI model, engineered for enterprise-grade performance where speed, security, and output quality are critical. Designed to run efficiently with minimal infrastructure, it outperforms top-tier models like GPT-4o and DeepSeek-V3 in both capability and cost-effectiveness. Featuring an extended 256K token context window—twice as large as most leading models—it excels at complex multilingual and agent-based tasks essential for modern business operations. Despite its power, it can be deployed on just two GPUs, making it highly accessible. With blazing-fast throughput of up to 156 tokens per second—about 1.75x faster than GPT-4o—Command R+ delivers exceptional efficiency without compromising accuracy or depth.
| o4-mini | Command A | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images | text |
API Providers
| OpenAI API | Cohere, Hugging Face, Major cloud providers |
Knowledge Cut-off Date
| - | - |
Open Source
| No | Yes |
Pricing Input
| $1.10 per million tokens | $2.50 per million tokens |
Pricing Output
| $4.40 per million tokens | $10.00 per million tokens |
MMLU
| fort | 85.5% Source |
MMLU-Pro
| - | Not available |
MMMU
| 81.6% Source | Not available |
HellaSwag
| - | Not available |
HumanEval
| 14.28% Source | Not available |
MATH
| - | 80% Source |
GPQA
| 81.4% Source | 50.8% Source |
IFEval
| - | 90.9% Source |
SimpleQA
| - | - |
AIME 2024 | 93.4% Source | - |
AIME 2025 | 92.7% Source | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | - |
MathVista
| - | - |
Mobile Application | - |
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