Overview & Capability
Groundbreaking open-weights reasoning model trained via large-scale reinforcement learning. Competes directly with OpenAI o1 on math, science, and competitive coding benchmarks.
Key Highlights
- Transparent step-by-step reasoning tokens with self-correction
- Distilled open-weights available for local Ollama/LM Studio execution
- 95%+ cost reduction compared to proprietary reasoning APIs
Robbie's Independent Take
Verified: 2026-10-11
Excellent candidate for local execution with zero cloud lock-in. For 7B-8B parameter models, budget at least 8 GB of unified memory or VRAM (Q4_K_M quantization). For 32B-70B models, aim for 24 GB+ to maintain interactive throughput without CPU offload penalties.
Alternatives & Replaces
- OpenAI o1 ($15/1M tokens)
- OpenAI o3-mini