AI tool comparison
Gemma 4 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
Gemma 4
Google's sharpest open models — multimodal, 256K context, runs on a Raspberry Pi
75%
Panel ship
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Community
Free
Entry
Gemma 4 is Google DeepMind's fourth-generation open model family, released April 2, 2026, under Apache 2.0. Four variants ship in the family: E2B and E4B edge models that run fully offline on phones, Raspberry Pi, and NVIDIA Jetson; a 26B Mixture-of-Experts model that activates only 3.8B parameters at inference; and a 31B Dense flagship. The 31B scores 1452 on the Arena AI text leaderboard (third among all open models), hits 89.2% on AIME 2026 math, and 85.2% on MMLU Pro — versus Gemma 3's 20.8% on AIME. All four model sizes accept text and image inputs. The edge models additionally handle native audio and video, making them the first on-device models with full multimodal coverage. Context windows reach 256K tokens on the large variants, enabling entire codebases or long documents in a single prompt. Native support for tool use, structured output, and agentic workflows is baked in from the start. For the open-source AI community, Gemma 4 is a watershed: a commercially permissive model that genuinely competes with closed-source alternatives on reasoning benchmarks. Gemma downloads crossed 400 million before this launch — Gemma 4's edge deployment story, combining on-device inference with frontier-class reasoning, looks set to make that number look small.
AI Models
Tencent Hy3-preview
Tencent's first open-source frontier MoE — 295B params, 21B active, free on HuggingFace
75%
Panel ship
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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
“Apache 2.0, runs on a Pi, 256K context, beats proprietary models on AIME — this is the open-source AI stack I've been waiting for. The agentic workflow support baked in natively means I'm not bolting on separate tooling. Shipping today.”
“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.”
“The benchmark numbers are impressive on paper, but Gemma 3 was also hyped and underdelivered in production on complex multi-step tasks. The edge models are still unproven outside of Google's own hardware partnerships. Watch the community benchmarks before committing to a migration.”
“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.”
“On-device frontier-class intelligence with native audio and video is the inflection point for ambient AI. When a $35 Raspberry Pi can run a model that beats last year's GPT-4 on math, the entire economics of edge AI applications change overnight. This is the model that makes AI infrastructure costs asymptotically cheap.”
“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.”
“The document and PDF parsing, OCR, chart comprehension, and UI understanding built into every model size is huge for creative workflow automation. I can finally build tools that read design briefs, invoices, and mockups without needing a cloud API call. The offline capability means client data never leaves my machine.”
“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.”
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