AI tool comparison
MiniMax M2.7 vs Tencent Hy3-preview
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
MiniMax M2.7
230B open-weights MoE reasoning model built for coding and agentic workflows
50%
Panel ship
—
Community
Free
Entry
MiniMax M2.7 is a 230B-parameter Mixture-of-Experts reasoning model released as open weights in April 2026. Only 10 billion parameters activate per token (8 of 256 experts), which enables frontier-level performance at significantly lower inference cost and latency than dense models of comparable quality. The context window stretches to 204,800 tokens — roughly 307 pages of text — with strong performance on long-horizon agentic tasks. M2.7 is purpose-built for tool-using agents and coding workflows. It scored 50 on the Artificial Analysis Intelligence Index, placing it among the top open-weight models globally. Weights landed on Hugging Face simultaneously with an API launch and the open-sourcing of OpenRoom, MiniMax's interactive agent orchestration system — a rare move that gives developers the full stack from model to agent runtime. MiniMax is a Shanghai-based AI company that has been quietly iterating through M1, M2, M2.5, and now M2.7 with consistent improvements. The M2.7 release represents a notable capability jump in the MoE open-weights space, particularly for developers who need a locally deployable model that can handle complex multi-step agent tasks without calling a paid API.
AI Models
Tencent Hy3-preview
Tencent's first open-source frontier MoE — 295B params, 21B active, free on HuggingFace
75%
Panel ship
—
Community
Free
Entry
Tencent's Hy3-preview is the company's first public frontier-class language model, released April 23 as open weights on Hugging Face. The model is a 295B parameter Mixture-of-Experts architecture with only 21B parameters active per token — keeping inference costs comparable to much smaller dense models while reaching capabilities that compete with leading proprietary systems. The release comes under new leadership: Yao Shunyu, a former OpenAI researcher, joined Tencent in early 2026 to build out its frontier AI effort. The team claims to have gone from project start to public release in under three months — an unusually fast timeline for a model of this scale. The 256K context window and strong performance on agentic and coding benchmarks position it directly against GLM-5.1 and Qwen3.6 in the open-source frontier race. Free inference is available on OpenRouter's free tier at launch, with the model also appearing on Hugging Face's Inference API. The architecture uses 192 routed experts in a hybrid dense-MoE configuration. For teams needing a capable open-weights model for agentic workflows without paying proprietary API rates, Hy3-preview arrives as a credible option at a remarkable cost-to-capability ratio.
Reviewer scorecard
“Only 10B active params with 230B total is a sweet spot — you get near-frontier quality with manageable inference costs. The open-sourced OpenRoom agent runtime alongside the weights makes this a production-ready stack, not just a model drop.”
“295B MoE with 21B active per token is a sweet spot for production use — you get frontier-quality outputs at a fraction of the compute cost. The 256K context and agent-optimized design make this immediately useful for complex workflow automation. Worth running evals against your specific use case.”
“MiniMax is still less battle-tested than Qwen or Llama in community tooling. 230B total weights still require serious hardware even with MoE efficiency. And the version cadence (M2 to M2.5 to M2.7) suggests rapid deprecation cycles.”
“Tencent hasn't published a full technical report yet, so benchmark claims are hard to independently verify. The 'three months to frontier' narrative sounds impressive but raises questions about training data sourcing and evaluation rigor. Preview releases from large Chinese labs have historically required patience before production stability.”
“The combination of open-source agent runtime plus frontier-adjacent open weights is exactly the stack needed to enable truly sovereign AI deployments. MiniMax is quietly building one of the most complete open-source AI stacks in the world.”
“The pace of open-source frontier models from Chinese labs is accelerating faster than anyone predicted — we now have credible open-weight competition from Alibaba, Zhipu, Xiaomi, and Tencent simultaneously. This is geopolitically significant and means the open-source ecosystem will stay competitive with proprietary models for years.”
“For pure creative tasks, the MoE trade-offs in consistency aren't ideal. Locally running a 230B model is still not practical for most creator workflows without dedicated GPU infrastructure.”
“For multilingual creative work — especially for Chinese market content — having a frontier-quality open-source model from a Chinese lab is meaningful. The free OpenRouter tier means creators can experiment without API budgets.”
Weekly AI Tool Verdicts
Get the next comparison in your inbox
New AI tools ship daily. We compare them before you waste an afternoon.