OpenAI vs Cohere

OpenAI's 64-model portfolio reflects a fragmentation strategy with multiple versions, fine-tunes, and specialized models (42 with vision, 57 with function calling), while Cohere focuses on 4 general-purpose models with consistent capabilities. This means OpenAI users face more complex model selection decisions but get specialized options like GPT-5.4 (67/100 score) for specific tasks, whereas Cohere users get simpler deployments with Command R+ (38/100 score) handling most use cases at $10.00/M tokens output.

Cohere has 0 of 4 models supporting vision, making it unsuitable for any image analysis, document processing, or multimodal workflows, while OpenAI offers vision in 42 of 64 models (66%) including their top performers. This capability gap means teams needing OCR, visual QA, or image-to-text must use OpenAI despite Cohere's competitive text performance at 36/100 average score versus OpenAI's 49/100.

OpenAI's pricing spans from commodity models at $0.110/M tokens to specialized high-compute models at $600/M, reflecting everything from basic chat to advanced reasoning systems across their 64-model range. Cohere's narrower $0.150-$10.00/M range across just 4 models suggests a focus on production text generation without the extreme ends of OpenAI's portfolio, though their cheapest option still costs 36% more than OpenAI's entry point.

Despite OpenAI's 5 open source models representing only 8% of their 64-model portfolio, they still provide more self-hosting options than Cohere's single open model (25% of their 4 total). However, neither provider truly prioritizes on-premise deployment compared to API-first operations, with OpenAI's top performer GPT-5.4 (67/100) and Cohere's Command R+ (38/100) both remaining closed source.

OpenAI's 1.1M token context window enables processing entire codebases or book-length documents in a single call, while Cohere's 256K limit requires chunking strategies for large inputs despite still exceeding most practical needs. This 4.3x context advantage matters most for specialized use cases like repository-wide code analysis or legal document review, though both far exceed typical conversation needs where Cohere's lower pricing ($0.150/M minimum) might offset the limitation.