MiMo v2.6

Original Article ↗ Hacker News Discussion ↗

Xiaomi’s MiMo v2.6 release is being praised as a highly capable, low-cost open-weight model that lands on the intelligence‑vs‑price frontier, with some users finding it competitive with US “frontier” systems for coding, agents and multimodal tasks. Commenters highlight Xiaomi’s unusual transparency, especially a live RL training dashboard, while also noting practical issues like overlong reasoning traces, looping, and token inefficiency in some settings. The launch feeds into a broader shift toward Chinese and other open models on cost and efficiency grounds, raising questions about US labs’ business models, regulatory pushback on open weights, and long‑term competition over compute, energy, and AI leadership.

Model capabilities, pricing, and positioning

  • MiMo v2.6 is widely seen as a strong, cost‑effective open‑weight release, especially the Flash variant (309B total / 15B activated params) and Pro (1.02T / 42B activated).
  • Several users say 2.6 Flash/Pro feel roughly in the Opus/Sol tier for many tasks while being much cheaper, particularly for coding and agentic use.
  • There’s also a 9B Qwen distill trained on MiMo‑generated data, targeting “toy GPUs” and lighter use cases.

Benchmarks, Pareto frontier, and comparisons

  • MiMo is praised for honestly placing itself behind top US frontier models on many benchmarks while highlighting its cost/performance “Pareto front.”
  • On some harder or less benchmark‑saturated tasks, commenters claim MiMo leads comparable Chinese models like Kimi K3, and competes with DeepSeek 4.1 Flash.
  • Others distrust benchmarks in general, citing overfitting and degradation of proprietary models after launch; some per‑benchmark rankings (e.g., Opus vs Astra/Fable) are disputed.

Training transparency and RL dashboard

  • The live RL dashboard is a major positive: it exposed restarts, dropped datasets (e.g., a “cyber” dataset that hurt coding), real‑time loss curves, rollouts, costs, and advanced metrics (model staleness, KL divergence).
  • Commenters see it as a valuable educational resource and an unusually open look at frontier‑style RL, though dataset identities remain opaque.

User experiences and behavior quirks

  • Many like MiMo’s real‑world coding and scientific performance; others report infinite loops, long “overthinking” traces, and very slow completion at higher effort levels.
  • Flash sometimes reasons longer than Pro, contrary to expectations. Some see MiMo 2.5 as having “gotten dumber” shortly before 2.6’s release.
  • SVG “pelican” benchmarks show competent but not bench‑maxxed behavior; some prefer other models’ outputs.

Chinese vs US labs, regulation, and commoditization

  • Strong sentiment that Chinese models (MiMo, DeepSeek, GLM, Kimi, Qwen) are closing the capability gap while dominating on efficiency and price.
  • Many speculate US frontier labs are pushing “AI safety” regulation and restrictions on open weights or Chinese models as de‑facto protectionism and regulatory capture.
  • Others counter that compute advantages still favor US labs, and regulation would apply to all, not just newcomers.
  • Several predict LLM inference will become a low‑margin commodity like mobile data; value will migrate to applications and enterprise integration rather than raw tokens.

Infrastructure, energy, and hardware

  • Discussion covers running MiMo and other large MoE models on “Spark”/GB10‑class hardware using aggressive quantization and n‑gram per‑layer embeddings with SSD offload.
  • Memory is a major constraint; many users with 128–256 GB RAM can’t yet host full‑context frontier‑scale models.
  • There is debate over whether the true bottleneck is compute or power; some argue China’s grid build‑out and engineer pipeline will give it a long‑term AI edge, others note US hyperscalers’ global datacenter strategy.