Frontier LLM price hikes make offshore engineers plus open models the cheaper bet

Inference costs at the major US labs are climbing, not falling. GPT-5.5 tripled its API pricing versus GPT-5 from eight months earlier, Gemini 3.5 Flash tripled its predecessor’s rates, and Anthropic’s Opus-4.7 introduced a tokenizer that burns 32-47% more tokens than Opus-4.6. Combined with the industry’s accelerating token consumption per task, enterprise AI bills are compounding from both directions.

A blended per-million-token cost comparison puts Anthropic and OpenAI near $2.80 while DeepSeek lands at roughly $0.09 — a 30x gap. The closed frontier models remain more capable, but the author argues that for coding work, a competent human engineer paired with a near-frontier open-source model closes the capability gap that matters. Frontier LLMs excel at scoped task execution but still lag humans on long-horizon skills like meta-memory, evidential sufficiency, and genuine autonomy.

The projection: an engineer in a low-cost country running DeepSeek-class models locally will soon be more economical than paying frontier API rates. The simplifying assumptions are real, but the structural argument holds — runaway inference spend creates its own price ceiling, and improving local hardware plus rapidly improving open weights tilt the math further toward the outsourcing-plus-local-AI stack.