MiMo-V2.5-Pro Benchmarked: Strong Social Deduction Reasoning, Good Value vs K2.6

MiMo-V2.5-Pro, Xiaomi's latest open-weights model, has been benchmarked in autonomous games of Blood on the Clocktower — a complex social deduction game similar to Mafia/Werewolf. The benchmark, created by Reddit user cjami, pits models against each other in full games, measuring reasoning, deception, and tool use.
Key Results
- Win rate: 88% as Good team, 48% as Evil team — overall high but lopsided. Evil performance is the main weakness vs Kimi K2.6.
- Token efficiency: 183,639 output tokens per game, similar to Gemini 3.1 Pro. Compare to Kimi K2.6 at 580k tokens (3x longer).
- Cost per game: $0.99 — less than half Kimi K2.6 ($2.65) and far below Claude Opus 4.6 ($3.76).
- Match duration: 2-3 hours (vs Kimi K2.6 which takes 10-15 hours due to verbose reasoning).
- Tool call error rate: 0.4% — reliable for autonomous agent workflows.
Notable Performance
Strong reasoning under uncertainty: example of thinking from others' perspectives vs GPT 5.5 and clean deductions winning a game.
Notable Mistakes
- Expected an evil Baron to self-reveal, leading to a loss — vs Claude Opus 4.6.
- Minion confessing their role — transcript.
Practical Takeaway
For developers needing an open-weights model with strong reasoning in multi-agent or game-theoretic settings, MiMo-V2.5-Pro offers the best value among top-tier models — lower cost, faster inference, and reasonable reliability, albeit with room for improvement in adversarial roles.
Full model transcripts and game logs: MiMo-V2.5-Pro on Clocktower Radio. Methodology: How-it-works.
📖 Read the full source: r/LocalLLaMA
👀 See Also

Persistent Data Loss in Claude Projects: Conversations Disappearing Without Recovery
A long-form writer reports losing entire days of work in Claude Projects due to conversations disappearing from the project chat list, unsearchable and unrecoverable, with no response from Anthropic support after three incidents.

Wikipedia's AI Policy: LLMs Banned for Article Creation, Exceptions for Copyediting and Translation
Wikipedia prohibits using LLMs to generate or rewrite articles, with narrow exceptions for basic copyediting and translation. Violations can lead to speedy deletion (G15) and removal of AI-generated comments from talk pages.

HN data confirms arXiv paper share dropping, LLM hype peak behind us?
Dylan Castillo used Claude to query HN BigQuery data, finding that the percentage of front-page stories linking to arXiv has been decreasing rapidly in recent months, after an LLM-dominated peak in 2023–2026.

User reports switching from Gemini Pro to Claude Max for academic project assistance
A user switched from Gemini Pro to Claude Max after experiencing frustration with Gemini's performance on practical tasks. They report Claude successfully reviewed their academic project, asked clarifying questions, and suggested logging learned information to a memory.md file.