Meta (Llama) vs DeepSeek

DeepSeek's focus on reasoning capabilities comes at the cost of model diversity, with 0 vision models compared to Meta's 4 of 14, while their best model (DeepSeek V3.2 Exp at 46/100) falls 8 points short of Llama 4 Maverick's 54/100. The specialized approach also drives up pricing, with DeepSeek's cheapest option at $0.290/M being 7.25x more expensive than Meta's $0.040/M entry point.

Meta's free tier models enable zero-cost prototyping and development, while DeepSeek's minimum $0.290/M output pricing means even basic testing accumulates costs. For production workloads processing 100M tokens monthly, Meta's pricing advantage ranges from $4 (at $0.040/M) to $74 (at $0.740/M) versus DeepSeek's $29 to $250, making Meta 7.25x to 3.4x cheaper across comparable tiers.

Meta's 1M token context (6.1x larger than DeepSeek's 164K) reflects their investment in long-form document processing and multi-turn conversations, while DeepSeek's smaller windows align with their reasoning-task focus where problems typically fit within 164K tokens. This makes Meta superior for analyzing codebases or long documents, while DeepSeek's 91% reasoning model coverage (10 of 11) targets complex but contained logical problems.

Function calling represents table stakes for modern LLMs with Meta at 50% coverage (7 of 14 models) and DeepSeek at 73% (8 of 11), but Meta's 4 vision models versus DeepSeek's 0 reveals fundamentally different market strategies. Meta targets multimodal applications while DeepSeek doubles down on text-only reasoning, evident in their 10 reasoning models versus Meta's 0.