Best Local LLM for Math and Quantitative Reasoning
Which open-weight models actually solve math on your hardware, from quick arithmetic to AIME-level problems, ranked by VRAM tier and use case.
Tag
#reasoning
Reasoning Models vs Standard LLMs: When Thinking Actually Helps
When to use a reasoning model vs a standard one: tasks where thinking helps, where it hurts, and how to set effort on OpenAI, Anthropic, Gemini, and DeepSeek.
What Thinking Mode Actually Costs You (September 2026)
Reasoning tokens, latency, and KV cache cost of thinking mode in local LLMs. How to toggle it per runner and when it is the wrong choice.
Best Local Reasoning Models by VRAM (October 2026)
Reasoning-capable open-weight models sized by VRAM. DeepSeek-R1, Qwen3 with thinking, QwQ, Phi-4 reasoning, gpt-oss, and the licence traps.
Nvidia's Nemotron 3: The First Open-Source Model Worth Running Locally in 2026
Nvidia open-sources a 30B-parameter reasoning model that runs on consumer GPUs with a million-token context window. Here's what makes it different.
Google+UVA: Longer Reasoning Predicts Failure, Not Success
Google and UVA research shows longer AI reasoning traces correlate with wrong answers. The fix: measure how deeply the model thinks, not how much it writes.
Gemini 3.1 Pro More Than Doubles Its ARC-AGI-2 Score
Google's Gemini 3.1 Pro scores 77.1% on ARC-AGI-2. API pricing starts at $2 per million input tokens for prompts up to 200,000 tokens.
Google's Gemini 3 Deep Think Solves 18 Unsolved Research Problems and Disproves a Decade-Old Conjecture
Google's upgraded reasoning model finds flaws in peer-reviewed papers, optimizes semiconductor fabrication, and outperforms every frontier model on scientific benchmarks.