AI tool comparison
Tencent Hy3 Preview vs Ternary Bonsai
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
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.
Open Source Models
Ternary Bonsai
1.58-bit LLMs that fit in 1.75 GB — runs in your browser via WebGPU
75%
Panel ship
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Community
Paid
Entry
PrismML's Ternary Bonsai is a family of ultra-compressed language models using 1.58-bit weights — meaning every parameter is stored as -1, 0, or +1, with no higher-precision layers anywhere in the architecture. The line-up covers 8B, 4B, and 1.7B parameter models. The flagship 8B model fits in 1.75 GB of RAM, a 9x reduction versus a 16-bit baseline. Unlike earlier 1-bit experiments that felt like a party trick with serious capability regressions, Ternary Bonsai 8B outperforms PrismML's own prior 1-bit Bonsai 8B by 5 points on average across standard benchmarks. The team also ships WebGPU inference, so the 1.7B model runs entirely in a browser tab. This is the first time a production-quality chat model has run with no server at all. The real-world use case is edge and offline deployment: medical devices, air-gapped government systems, consumer apps that need to work without a signal. At 1.75 GB, the 8B model fits on the GPU RAM of a six-year-old gaming laptop. PrismML is positioning this as the foundation for truly offline AI — a credible claim if the capability benchmarks hold up under real-world testing.
Reviewer scorecard
“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.”
“1.75 GB for an 8B model is a genuine engineering achievement. I can finally ship a capable model inside a desktop Electron app without requiring users to have a dedicated GPU. The WebGPU demo loads fast and output quality is surprisingly coherent for its size.”
“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.”
“Benchmarks are one thing; real task performance is another. A 9x memory saving typically comes with a 15-30% quality drop on anything beyond simple Q&A. And 'scores 5 points higher than our previous 1-bit model' is a low bar when the previous model wasn't competitive with 4-bit quants.”
“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.”
“Browser-native LLMs with no server change the entire privacy calculus. If this scales to 13B+ parameter territory at comparable compression ratios, every personal AI assistant can run offline on consumer hardware. That's a trajectory worth tracking closely.”
“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.”
“WebGPU inference means I can build offline creative tools — grammar checkers, caption writers, image prompt expanders — without an API key or monthly cost. The 1.7B model is small enough to embed in a browser extension with manageable download size.”
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