Meta (Llama) vs Cohere

Meta's strategy prioritizes open-source accessibility and variety over peak performance, with 14 fully open-source models including 2 free options, though their top performer (Llama 4 Maverick) only reaches 54/100. Cohere takes a focused commercial approach with just 4 models averaging slightly higher at 36/100, but their best model (Command R+ 08-2024) caps out at 38/100, suggesting neither provider currently competes at the performance frontier.

Meta's cheapest models at $0.040/M tokens offer basic text generation across their 14-model lineup, including vision support on 4 models (28.6%) and function calling on 7 models (50%). Cohere's entry point at $0.150/M tokens provides function calling on 2 of 4 models (50%) but lacks any vision capabilities or free tier, making it 3.75x more expensive for basic text tasks without significant capability advantages.

Cohere's appeal lies in its commercial support structure and enterprise focus rather than raw specifications - their 4 models max out at 256K context versus Meta's 1M token capability. However, Cohere's pricing ceiling of $10.00/M tokens (25x higher than Meta's $0.740/M max) suggests they're targeting enterprise customers who value support contracts over the open-source flexibility of Meta's 14 models.

Meta clearly dominates multimodal use cases with vision capabilities in 4 of 14 models (28.6%) while Cohere offers zero vision support across all 4 models. Combined with Meta's 7 models supporting function calling (50%) versus Cohere's 2 models (50%), developers building visual AI applications have no choice but Meta, though neither provider offers reasoning capabilities (0/14 for Meta, 0/4 for Cohere).