Minimax M2.5 - What Makes This Different!

Quick Overview

MiniMax M2.5 achieves state-of-the-art (SOTA) performance in coding and agentic tasks, matching or exceeding proprietary models like Claude Opus 4.6 and GPT-5.2 Codex on several benchmarks, while offering significantly lower operational costs, exemplified by a $1/hour continuous runtime at 100 tokens per second output.

Key Points: MiniMax M2.5 achieves SOTA performance in coding benchmarks, boasting 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp (with context management). The M2.5 model demonstrates tremendous speed, completing the SWE-Bench Verified evaluation 37% faster than MiniMax M2.1, matching the speed of Claude Opus 4.6. The new M2.5 model is significantly cost-effective, costing $1 per hour to run continuously at 100 output tokens per second, or $0.30 per million input tokens and $2.4 per million output tokens for the M2.5-Lightning variant. M2.5's output cost is one-tenth to one-twentieth that of top proprietary models like Opus, Gemini 3 Pro, and GPT-5. MiniMax has developed an agent-native RL framework called Forge, which decouples training/inference, supports arbitrary agents, and achieves approximately 40x training speedup using tree-structured merging strategy. The company has built over 10,000 Experts on MiniMax Agent, with 30% of daily tasks autonomously completed by M2.5, showing strong performance in coding scenarios (80% of newly committed code). The OpenRouter pricing shows MiniMax M2.5 (fp16) costing $0.30/$1.20 input/output per million tokens, significantly cheaper than competitors like Claude Opus 4-6 ($0.30/$10.00) and GPT-5.2 Codex ($0.30/$75.00) on input price, and far cheaper on output price.

Context: The video discusses the release and capabilities of MiniMax M2.5, a new frontier large language model from the Chinese AI company MiniMax, focusing on its performance in real-world productivity and agentic tasks, particularly coding. The presentation contrasts M2.5's performance and extreme cost-effectiveness against established proprietary models like Anthropic's Claude family and OpenAI's GPT series, highlighting the company's underlying reinforcement learning (RL) infrastructure called Forge.