I asked the highest IQ GPT5.4pro thinking to find the current local model hardware and local models that come closest to the reasoning and programming capabilities of Claude 4.6 and GPT5.4. After thinking for a long time, this is the answer it gave: Mac Studio M4 Max 128GB Apple's official Mac Studio now offers the M4 Max, with a maximum configuration of 128GB unified memory. If you absolutely need to buy a new machine, I would consider 128GB as the 'minimum serious configuration,' because for local LLMs, the real determinant of the experience is often memory capacity first, followed by CPU/GPU performance. The difference between 64GB and 96/128GB is reflected in the choice of models, context length, concurrency capacity, and quantization levels, which are all more critical than simply upgrading to a faster CPU. What models you should actually run locally: For programming: • Qwen3-Coder-Next: Officially positioned as coding agents and for local development, supports 256K context, emphasizes long-term reasoning, complex tool usage, and recovery from execution failures. However, it’s explicitly an 80B total / 3B activated sparse model, with LM Studio marking a minimum of 42GB RAM. • Qwen2.5-Coder-32B-Instruct: Qwen officially claims this was the state-of-the-art open-source code LLM at the time, with coding capabilities matching GPT-4o and a 128K context. For reasoning: • DeepSeek-R1-Distill-Qwen-32B: DeepSeek officially states it surpasses o1-mini and is among the strongest local 32B-level reasoning models currently available. • Qwen3-32B: Officially highlights its ability to switch between thinking/non-thinking modes within a single model, with improved reasoning, code generation, and agent capabilities compared to previous generations. This local setup, compared to the overall level of Claude Opus 4.6 / GPT-5.4, is roughly estimated at: • Overall capability: About 60%–75% • Medium-complexity programming: About 70%–85% • Long-process agents / large codebases / multi-tool autonomy: About 45%–65% • Ultra-long context tasks: About 30%–50%