AI tool comparison
DeepSeek V4-Pro 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.
Foundation Models
DeepSeek V4-Pro
1.6T-param MoE model, 1M context, Nvidia-free — just dropped Apache 2.0
75%
Panel ship
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Community
Paid
Entry
DeepSeek just dropped V4-Pro and V4-Flash simultaneously — and it's a statement release. V4-Pro packs 1.6 trillion total parameters in a MoE architecture with only 49B active per token, a 1-million-token context window, and a hybrid attention system (Compressed Sparse Attention + Heavily Compressed Attention) that requires just 27% of single-token inference FLOPs compared to V3.2. Both models are Apache 2.0. The hardware story is arguably the bigger news: V4 was trained entirely on Huawei Ascend 950PR chips, zero NVIDIA. That's a geopolitical and technical milestone — it validates China's domestic AI compute stack at frontier scale. The Engram Memory System gives V4 conditional context recall (94% at 128K tokens vs ~45% for V3.2), enabling genuinely long-context reasoning. V4-Flash at 284B parameters (13B active) is the cheaper, faster sibling for production use. Pricing is expected around $0.30/M tokens for Pro. The timing — released to HN today with 99+ points within hours — confirms this as an immediate conversation in the developer community about whether open-weight frontier models have finally matched proprietary ones.
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 with 1M context and frontier-level benchmarks changes the commercial calculus entirely. Self-host for sensitive workloads, use the API for production — the 49B active params means reasonable inference costs if you have the hardware.”
“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.”
“Benchmark claims from DeepSeek have historically been hard to independently replicate at launch. The Huawei chip story is compelling but also means the Western open-source deployment story requires significant hardware work. And 1.6T parameters is not consumer hardware territory.”
“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.”
“V4's Nvidia-free training stack is a geopolitical inflection point as much as a technical one. It proves the export control strategy isn't containing China's AI progress — and gives the global open-source community a frontier model with no licensing restrictions.”
“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.”
“A 1M-token context model at $0.30/MTok Apache 2.0 means long-form creative projects — novels, screenplays, brand bibles — can finally be processed holistically. The Flash variant's low cost makes it accessible even for creative side projects with tight budgets.”
“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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