Tencent's Hy4 preview: a 770B open-weight AI model built for agents

Tencent is putting its Hy4 model forward as its new productivity flagship. The Hy4 preview, released and open-sourced on August 28, 2026, is a 770-billion-parameter model that activates 49 billion parameters per token and reads up to 1 million tokens of context. The sources available for this report document that preview release, its access channels and its early test results. They do not cover any later company showcase or a global rollout of an office agent, so this briefing sticks to what is on the record.

Tencent describes the model as built for software engineering, office work, game development and scientific research. It says the model is co-designed with its own products CodeBuddy and WorkBuddy “so that gains in the model show up in the work people actually do with it”. The headline evidence is mostly Tencent’s own. That includes a blind evaluation in which Hy4 preview edges out Kimi K3 and GLM 5.3 by small margins, plus one early third-party coding leaderboard result.

What Hy4 preview adds over Hy3

Hy4 preview is the successor to the Hy3 line, and Tencent is again shipping “preview first, official release later”. In its model card, Tencent says it scaled Hy4 on three fronts: model size, context length and training data. It calls the result “the largest generation-over-generation gain we’ve measured, and enough to put Hy4 preview at the open-source frontier”.

The clearest spec change is context. OpenCode’s model data lists Hy4 preview at a 1M-token context, against 256K for Hy3. Output limits move the other way in that listing: 64K for Hy4 preview versus 128K for Hy3.

The model is a mixture-of-experts (MoE) design. In an MoE model, the network is split into many “expert” sub-networks, and a router picks a few of them to run for each token. According to the model card, Hy4 preview has 78 layers. The first uses a standard dense layer and the remaining 77 each contain 256 routed experts and 1 shared expert. Every token activates the top 8 routed experts plus the shared expert. A built-in multi-token prediction (MTP) layer, with 10B total parameters and 0.7B active, supports speculative decoding. This technique drafts several tokens ahead to raise generation speed.

For attention, Tencent says it was “inspired by DeepSeek and GLM.” It uses Gated DeepSeek Sparse Attention (Gated DSA) with IndexCache to reuse sparse indices across layers, and identity Hyper-Connections (iHC) on the residual pathway.

Tencent also says it built training data with in-house software engineers, game developers, finance analysts and security experts, around the work those teams ship. According to eesel’s review, Tencent claims Hy4 took part in its own development by proposing and running experiments on training methods and low-level operators. Tencent also says the model optimized its own inference stack for a 31.8% throughput gain, which it calls “an early-stage recursive self-improvement loop”. That claim comes from Tencent alone and has no independent check in the sources.

Spec	Hy4 preview (as stated)
Architecture	Mixture-of-experts
Total / active parameters	770B / 49B per token
Layers	78
Experts	256 routed + 1 shared; top-8 routed per token
Context length	1M tokens
Max output	64K (OpenCode, Amux listings)
Builds	BF16 and official FP8
Preview release	August 28, 2026

Not every source agrees on the architecture. Zeniteq reports that the technical table in the official model card lists about 779 billion total parameters, 144 layers and two shared experts. The model card text reviewed here lists 770B, 78 layers and 1 shared expert, and notes that its table covers backbone parameters only, excluding the MTP layer. That exclusion may explain part of the gap, but the sources do not settle it.

Tencent’s benchmarks and its blind evaluation

The comparison Tencent leads with is a blind side-by-side evaluation. In it, 163 internal experts rated model outputs on 203 engineering tasks. Hy4 preview scored 2.99 on average:

• Against GLM 5.3: 2.99 vs 2.92, with 46.8% wins, 12.8% ties and 40.4% losses.
• Against Kimi K3: 2.99 vs 2.94, with 51.2% wins, 7.9% ties and 40.9% losses.

Tencent itself describes this as “slightly ahead”. eesel’s review calls the 0.05-point gap on a 4-point scale “a near-tie, not a knockout”.

On standard benchmarks, eesel reports these scores, which it says Hugging Face surfaces as verified:

• GPQA Diamond: 92.3
• SWE-bench Multilingual: 82.9% resolved
• SWE-bench Pro: 65.7
• Deep SWE: 64.3

Tencent’s own public chart compares Hy4 preview with Hy3, Qwen 3.8 Max, DeepSeek V4 Pro, GPT-5.6 Sol, GLM-5.3, Kimi K3 and Claude Opus 5. eesel’s reading of that chart is that Hy4 is usually behind Claude Opus 5 and GPT-5.6 Sol, and trades wins and losses with Kimi K3 and GLM-5.3. A reader on Reddit’s r/LocalLLaMA counted the chart and wrote that “against kimi k3 it’s behind on 7 of the 12 benchmarks”.

The independent evidence so far

The main third-party result is from Arena. According to Zeniteq, Hy4 preview placed about fifth on Arena’s WebDev AutoEval with 1,633 points, up from about 31st for Hy3. It ranked around third among models in Arena’s open category. Zeniteq adds that Reuters independently reported the same placement.

Zeniteq also cautions that WebDev AutoEval is an automated web-development test. It “cannot establish how reliably Hy4 patches mature repositories, navigates an unfamiliar terminal, recovers from failed tool calls, writes tests, or maintains a long agent workflow”. Leaderboard positions can also shift as Arena adds models.

Usage data offers a different kind of signal. On OpenCode, an AI coding tool that publishes model usage, Hy4 preview accounts for 243B tokens processed. That is 50.7% of Tencent model usage there, ahead of Hy3 at 236B. Amux AI, an API gateway, reports an average time to first token of 1.3 seconds and 23.3 tokens per second for Hy4 preview served by Tencent Cloud.

Where Hy4 preview is available

Access as stated in the sources:

• Open weights: on Hugging Face, in BF16 and an official FP8 quantized build.
• Tencent Cloud: Tencent Cloud International TokenHub lists Hy4 Preview, and Tencent advises developers to verify current model information before testing. Amux AI routes requests to Tencent Cloud as a provider.
• Third-party APIs: SiliconFlow offers Hy4-preview as a serverless model. B.AI announced API access through its official channels on September 2.
• Tencent products: Zeniteq reports Hy4 is integrated into WorkBuddy, CodeBuddy, Yuanbao, ima and Tencent Cloud.

On features, SiliconFlow lists native tool calls, structured output and context caching. Its spec table marks image input as not supported. Listed prices on SiliconFlow are $0.834 per million input tokens, $2.501 per million output tokens and $0.042 per million cached-read tokens. eesel says that undercuts Qwen 3.8 Max and Kimi K3 but is 5 to 6 times pricier than Hy3, and that DeepSeek V4 Flash remains far cheaper.

Licence terms and hardware: the practical catches

Which licence?

Sources conflict on the licence. eesel’s review describes an Apache 2.0 licence. Zeniteq reports a custom Tencent Hy Community License instead. It says that licence grant excludes the European Union, the United Kingdom and South Korea, and that products or services with more than 100 million monthly active users must request a separate commercial licence from Tencent. Teams in those regions, or at large scale, should read the licence file shipped with the weights before building on it. “Open-weight” is the safer term until then.

What it takes to run

MoE reduces compute per token, but not memory. All 770B weights have to sit in GPU memory because the router can pick any expert for the next token. Zeniteq puts the BF16 weights at about 1.56TB and the FP8 release at about 1.16TB. It says Tencent’s official serving examples start at eight GPUs, before extra memory for long prompts and concurrent users. eesel estimates 16 B200 or 8 B300 GPUs for the full model. The model card lists vLLM and SGLang deployment guides.

What it means for builders

Hy4 preview targets agentic coding and long, multi-step work. Amux describes it as designed for “coding agents, complex tool usage workflows, and productivity tasks that require planning, contextual continuity, and sustained multi-step execution”. Tencent’s office pitch is specific: the model takes “messy context spread across many files” and turns it into documents, spreadsheets and presentations.

Tencent’s own research-agent guide is candid about the limits. It says Tencent’s description of coordinating multiple sessions is “product-direction evidence, not a public benchmark or proof that Hy4 contains an experiment scheduler.” Developers need an external control plane to handle queueing, hardware, isolation and artifact tracking. The guide also warns that the model “cannot promote correlation to causation” and should keep a human responsible for changing direction.

Tencent lists known issues for this preview. The model spends “longer than necessary reasoning through complex tasks” and has “a tendency to over-verify its own work”. For agent builders, both affect latency and token cost. Measure them on your own workloads before switching.

What to watch next

• The official Hy4 release. Tencent says this is an early version with “real headroom left in both pre-training and post-training,” and that it will keep iterating on the known issues.
• Independent agent testing. Zeniteq notes that repository-level and agent tests still need to verify reliability, long-context performance and deployment costs.
• Licence clarity. Watch whether the regional exclusions and the 100-million-user threshold reported by Zeniteq hold up.
• Agents inside Tencent’s consumer apps. In June, Método Viral, citing the Financial Times, reported that Tencent was testing an AI agent inside WeChat. Users would reach it by swiping right on the home screen, and it could act across the app’s mini-apps. China’s generative AI approvals left the launch date open. Whether Hy4 powers that agent is not stated in the sources.