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Top 5 · 2026-05-01 · source-backed
Xiaomi released MiMo-V2.5-Pro, a 1.02 trillion parameter mixture-of-experts model (42B active) with 1M token context, fully MIT licensed. In benchmarks, it achieves 63.8% success on agentic tasks using 40-60% fewer tokens than Claude Opus 4.6 or GPT-5.4 for comparable results. In a demo, it built a complete SysY compiler in Rust in 4.3 hours with 672 tool calls.
This is the most capable fully open-source agentic model released to date. Full stop.
The timing matters. r/LocalLLaMA is calling April 2026 the "best month of all time" for local models. Six organizations shipped competitive open weights in a single month: Google (Gemma 4), Alibaba (Qwen 3.6), Meta (Llama 4), Mistral (Medium 3.5), Zhipu AI (GLM-5.1 744B MoE), and DeepSeek (V4). The gap between open and closed is collapsing faster than anyone predicted.
What catches my attention about MiMo isn't the parameter count. It's the token efficiency. Using 40-60% fewer tokens than frontier closed models for comparable agentic results means dramatically lower inference costs for self-hosted deployments. Combined with the MIT license, this opens the door for companies that can't or won't send proprietary code to Anthropic or OpenAI. And the 1M context window means you're not making compromises on what the model can hold in working memory.
For builders evaluating self-hosted agentic models, benchmark MiMo-V2.5-Pro against your current setup this week. If you're paying per-token for agentic workflows and the quality holds, the cost savings alone could justify the migration. If you're in a regulated industry where data can't leave your infrastructure, this might be the first open model that's actually good enough for production agent work.
One caveat: I haven't run it myself yet. The benchmarks look strong but benchmarks lie, especially for agentic tasks where real-world reliability matters more than peak performance. Test it on your actual workflows before committing.
Each link below shares sources, entities, or timing with this story.
Meta partners with Google / Shared entities / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (Meta partners with Google); both cover Alibaba, Anthropic, Claude Opus, DeepSeek; overlapping topics (benchmark, license, model, parameter).
Qwen competes with Meta / Shared entities / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (Qwen competes with Meta); both cover Alibaba, Anthropic, Claude Opus, Combined; overlapping topics (agentic, benchmark, closed, model).
Qwen competes with Meta / Shared entities / Shared topic / Earlier coverage
Linked by a graph relationship (Qwen competes with Meta); both cover April, Gemma, Google, Llama; overlapping topics (benchmark, license, model).
Qwen competes with Meta / Shared entities / Same source domain / What happened next
Linked by a graph relationship (Qwen competes with Meta); both cover Alibaba, Anthropic, April, Full; reported by the same outlet (venturebeat.com).
Meta released Muse Spark / Shared entities / Shared topic / What happened next
Linked by a graph relationship (Meta released Muse Spark); both cover Anthropic, April, DeepSeek, LocalLLaMA; overlapping topics (model, token).
Qwen competes with Meta / Shared entities / Shared topic / What happened next
Linked by a graph relationship (Qwen competes with Meta); both cover Anthropic, GLM, Google, LocalLLaMA; overlapping topics (closed, model).
Qwen competes with Meta / Shared entities / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (Qwen competes with Meta); both cover Alibaba, Anthropic, April, Claude Opus; overlapping topics (cost, model, token).
Qwen competes with Meta / Shared entities / What happened next
Linked by a graph relationship (Qwen competes with Meta); both cover Alibaba, DeepSeek, Gemma, Google; picks up the Alibaba thread on 2026-08-16.