At the sub-4B tier, every fraction of a cent matters. [Granite 3.1 2B Instruct](/models/ibm--granite-3.1-2b-instruct) and Llama 3.2 3B Instruct both price out under $0.08/1M tokens at competitive providers, but Llama 3.2 3B typically runs $0.01–0.02/1M tokens cheaper given the volume of provider competition behind Meta models. Granite 3.1 2B has a 1B-parameter weight advantage for tighter memory budgets, fitting in ~4 GB VRAM at INT4 vs ~7 GB for Llama 3.2 3B. Granite 3.1 2B Instruct was built with enterprise compliance pipelines in mind — IBM tuned it specifically for code understanding, log analysis, and retrieval-augmented generation tasks where a small, auditable model is required. On coding classification tasks (e.g., tagging support tickets by error type, routing CI/CD alerts), Granite 3.1 2B holds accuracy within 2–3% of larger models while staying well under $0.05/1M tokens. Its Apache 2.0 license also simplifies on-prem deployment approval. [Llama 3.2 3B Instruct](/models/meta--llama-3.2-3b-instruct) wins on general-purpose instruction following. Trained on a broader corpus, it scores higher on open-domain QA and summarization benchmarks, and its wider provider ecosystem means you get more flexibility on latency SLAs. For mobile or edge inference scenarios, Llama 3.2 3B has broader quantized-model support across GGUF and MLX runtimes. **Pick Granite 3.1 2B Instruct** if you're running enterprise log/code classification tasks, need Apache 2.0 licensing, or are constrained to 4 GB VRAM. **Pick Llama 3.2 3B Instruct** if you want the cheapest general-purpose small model with maximum provider choice.