# Tencent Hy4 Open-Weights Model: Benchmarks and Access > Source: > Published: 2026-08-30 08:11:00+00:00 Tencent released Hy4 preview on August 28 — a 770B parameter open-weight model published under Apache 2.0 and available on [Hugging Face](https://huggingface.co/tencent/Hy4-preview) right now. The model uses sparse Mixture-of-Experts routing: only 49B parameters activate per token, keeping inference costs well below what 770B total parameters would suggest. With a 1 million token context window and a built-in speculative decoding layer, it targets software engineering and agentic coding — and Tencent is offering free access on WorkBuddy and CodeBuddy for the next two weeks. The competitive positioning is real. Hy4 scores 82.9 on SWE-bench Multilingual, ahead of DeepSeek V4 Pro (77.3), Kimi K3 (80.8), and GLM-5.3 (81.3). API pricing through OpenRouter starts at $0.83 per million input tokens — roughly one-eighth of what GPT-5.6 Sol charges per output token. Apache 2.0 licensing means no field-of-use restrictions and no geographic carve-outs, which is a genuine differentiator in this space. ## How to Access Tencent Hy4 Now The fastest path is the free trial on [WorkBuddy and CodeBuddy](https://www.tencent.com/tencent-releases-and-open-sources-tencent-hy4-preview/), Tencent’s developer products. Access runs two weeks from launch, meaning the window closes around September 11. No significant friction beyond creating an account — useful for quick evaluation before committing to API costs. For API access, OpenRouter is the launch provider. Pricing: $0.834 per million input tokens, $2.501 per million output tokens. Cached input tokens cost $0.042 per million — a 20x discount that compounds quickly if you’re passing large system prompts or repeated context. Tencent Cloud TokenHub is also available as an alternative endpoint. Self-hosting is technically possible under Apache 2.0 but infrastructure-intensive. The FP8 quantized variant requires approximately 720GB of VRAM — achievable on a single 8xH200 node, but beyond what 8xH100 nodes offer (640GB max). Prebuilt Docker images exist for both vLLM and SGLang. For most teams, OpenRouter is the practical starting point. ``` docker run --gpus all -p 8000:8000 --ipc=host \ vllm/vllm-openai:hy4-preview tencent/Hy4-preview-FP8 \ --tensor-parallel-size 8 --served-model-name hy4-preview ``` ## Tencent Hy4 Benchmarks: Where It Wins and Loses Hy4 leads open-weight peers where multi-language code matters. Its 82.9 on SWE-bench Multilingual beats DeepSeek V4 Pro (77.3), Kimi K3 (80.8), and GLM-5.3 (81.3). However, Terminal-Bench 2.1 tells a different story: Hy4 scores 85.4 while GLM-5.3 hits 88.2 and Kimi K3 reaches 88.3. For agentic coding tasks requiring sustained tool use, it’s competitive but not top-tier. The generational jump from Hy3 is substantial. DeepSWE — a measure of real-world software engineering — climbed from 28.0 on Hy3 to 64.3 on Hy4, a 2.3x improvement in one model cycle. Claude Opus 5 still leads on SWE-bench Pro (79.2 vs Hy4’s 65.7), so if maximum coding accuracy is the requirement, the gap remains real. One important caveat: all Hy4 benchmark scores come from [Tencent’s internal evaluators](https://technode.com/2026/08/28/tencent-open-sources-hy4-preview-with-770b-parameters-and-a-1m-token-context/) on proprietary task sets. No independent leaderboard verification exists yet. ## The Preview Caveats Worth Knowing Tencent labels this an early version, and the limitations are real. Hy4 defaults to deep chain-of-thought reasoning and tends to over-verify its own work on straightforward tasks — inflating latency where none is needed. The fix is a single parameter change when calling the API: ``` response = client.chat.completions.create( model="tencent/hy4-preview", messages=[{"role": "user", "content": "Review this function for bugs"}], extra_body={"chat_template_kwargs": {"reasoning_effort": "no_think"}} ) ``` Additionally, [OpenRouter measured roughly 86% availability](https://explainx.ai/blog/tencent-hy4-preview-770b-moe-1m-context-august-2026) in the model’s first three days — still stabilizing after launch. Tencent acknowledges headroom remains in both pre-training and post-training, meaning a more capable final version follows this preview. For production workloads with uptime requirements, wait for the stable release. ## Key Takeaways - Hy4 preview is live now: free on WorkBuddy and CodeBuddy until around September 11, then $0.83 per million input tokens via OpenRouter - It leads open-weight peers on SWE-bench Multilingual (82.9) but trails on Terminal-Bench 2.1 — strong for multi-language code review, middle tier for agentic tasks - Apache 2.0 licensing is genuinely unrestricted — commercial use, modifications, and self-hosting all permitted without carve-outs - Set `reasoning_effort: "no_think"` for direct responses; the default deep reasoning mode adds unnecessary latency on simple tasks - Self-hosting needs 8xH200 GPUs minimum for the FP8 variant — most teams will use OpenRouter until infrastructure scales