When to Choose Each Option
Clear guidance based on your specific situation and needs.
Our Recommendation
There is no universal winner here, and the honest axis is not "free versus paid" - it is what you can verify and what you can survive. On cost, open weights win outright and by more than most teams assume. DeepSeek-V4-Pro's own live pricing page lists $0.435 per million input tokens and $0.87 per million output; Anthropic's pricing page lists Claude Opus 5 at $5 and $25. That is roughly 11x on input and 29x on output for a hosted open-weight model - before you consider running it yourself. Adoption is real, not aspirational: Z.ai's GLM-5.2 and its FP8 variant have together drawn close to four million Hugging Face downloads in the twenty-five days since publication, and an open-weight Chinese model did the TypeScript-to-native port of the Vercel CLI in July 2026. What you trade away is delivery and verification. Kimi K3's 2.8-trillion-parameter weights were dated 27 July 2026; a direct Hugging Face API check that morning shows no official repository, only third-party derivatives - while several outlets reported the release as done. Self-hosting also has a hardware floor that "free" hides: a frontier open-weight model needs a multi-GPU node before it will even load, and that node is a fixed monthly cost whether you send it one request or a million. On independently reproduced coding benchmarks the closed flagships still lead. The 2026 addition to this decision is policy. In July, the "Open Weights and American AI Leadership" letter went from 25 to 50 signatories in a single day - Nvidia, Microsoft, Meta, IBM, Hugging Face, Mozilla, the Linux Foundation, Mistral, with OpenAI and Google joining late and Anthropic and Amazon absent. Read it as a supply-chain input rather than a morality play: a vendor whose policy position is that certain weights should not be publishable is a vendor whose roadmap may diverge from your ability to exit. Open weights are the exit. The practical answer for most teams is both: a proprietary flagship for the frontier work where verified benchmarks and enterprise governance are the requirement, and an open-weight model held ready for the sensitive, high-volume, or cost-dominated workloads - so that a licence change, a price change or a policy change is an inconvenience rather than a migration.
- Choose Open-Source LLMs when...
- Your data class rules out sending prompts to a third-party API at all
- Volume is high and steady enough to amortise a dedicated GPU node
- You need to fine-tune, quantize or inspect the model rather than call it
- You want a migration path that survives a supplier's pricing or policy change
- Choose Proprietary LLMs when...
- You need the top of the frontier on benchmarks a third party has reproduced
- Your volume is spiky or small, so a fixed GPU node would sit idle
- You need enterprise governance today: regional hosting, DPAs, compliance tooling, support
- You have no platform team to own inference infrastructure and its failure modes