AI tool comparison
Google 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.
Open Source Models
Google Gemma 4
Google's first Apache 2.0 open model family with native multimodal
75%
Panel ship
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Community
Free
Entry
Gemma 4 is Google's newest open model family — E2B, E4B, 26B, and 31B sizes — built on Gemini 3 architecture. For the first time, Google has released Gemma under Apache 2.0, making the models fully commercial-friendly with no Google-specific use restrictions. Every model in the family is natively multimodal from training: text, image, video, and audio inputs are all first-class. Context windows run 128K–256K tokens depending on size, and the models include built-in function calling, structured JSON output, and agentic workflow support. The E2B and E4B variants target on-device mobile and laptop deployment, with native audio understanding designed for always-on assistant scenarios. NVIDIA has already published optimized Gemma 4 containers for RTX hardware. The Apache 2.0 license removes a major adoption barrier that held back Gemma 3 in commercial products. Gemma 4 landed at #1 on Hacker News with 1,400+ points — the open-source model community's reaction was immediate and enthusiastic.
AI Models
Tencent Hy3 Preview
295B MoE open weights — China's most efficient frontier model yet
75%
Panel ship
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Community
Paid
Entry
Tencent open-sourced Hy3 Preview on April 23, 2026 — the first model to emerge from the company's rebuilt AI infrastructure, and its most credible challenge to frontier closed models to date. With 295 billion total parameters but only 21 billion active at inference time (plus 3.8B MTP layer parameters), it's a Mixture-of-Experts architecture that punches far above its compute weight. The model supports up to 256K context and is available via Hugging Face, ModelScope, and GitCode under the Tencent Hy Community License. On coding benchmarks, Hy3 scores 74.4% on SWE-bench Verified, 54.4% on Terminal-Bench 2.0, and 67.1% on BrowseComp — placing it firmly in the same tier as top models from Anthropic and OpenAI. Tencent claims a 40% efficiency improvement over its predecessor Hunyuan models, and pricing through Tencent Cloud TokenHub is aggressive: RMB 1.2 per million input tokens. A free two-week window at launch via OpenRouter made it widely accessible immediately. The model was led by a team that includes former OpenAI researchers and has already been deployed across Tencent's core products — WeChat, Yuanbao, and QQ. That production integration is a meaningful signal: this isn't a benchmark vanity release. For developers who need a powerful, cost-efficient reasoning and agentic model with actual open weights, Hy3 Preview is one of the most interesting drops of April 2026.
Reviewer scorecard
“Apache 2.0 means I can embed it in commercial products without legal review overhead. Native audio + 256K context on a 26B model that runs on a single A100 is a killer combo for production agent work. This is the open model I've been waiting for.”
“21B active params with 295B total — this is genuinely practical to deploy on reasonable hardware while matching models 10x the inference cost. The 256K context and strong SWE-bench score make it a legitimate option for agentic coding pipelines. I'd use this today.”
“Google has a history of releasing models and then quietly deprioritizing them once the PR cycle ends. Gemma 1 and 2 both got less maintenance than promised. The Apache license is great news, but trust has to be earned over time with consistent model updates.”
“The Tencent Hy Community License is not Apache 2.0 or MIT — read it carefully before using this in production. There are usage restrictions that could bite commercial deployments. Also, benchmark scores look great, but independent evals of Chinese labs' models have historically diverged from self-reported numbers.”
“Native multimodal understanding — including audio — on models small enough for phones changes what ambient computing looks like. Gemma 4 on-device could be the model layer for a generation of always-on smart devices that don't need cloud inference.”
“The MoE efficiency race is the actual story here — we're getting frontier-class capability at a fraction of the activation cost. Hy3 is proof that the compute-vs-capability Pareto frontier keeps moving. Open weights with real deployment signals (WeChat at scale) is a combination that matters.”
“Image, video, and audio in one open model I can run locally? The creative tooling possibilities are enormous. I can build private multimodal workflows for client work without data leaving my machine. Apache 2.0 seals it — this is a Ship.”
“Strong visual coding capabilities and multimodal understanding make this genuinely useful for design-to-code workflows. The health image analysis and product comparison use cases already deployed in Yuanbao show real-world creative utility beyond pure benchmark games.”
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