Tencent Hy4 Preview Reality Check

Tencent released and open-sourced Hy4 preview on August 28, 2026. It is a large Mixture-of-Experts language model aimed at coding, tool use, office work and research rather than a small local model: Tencent's model card lists 770B backbone parameters with 49B activated per token, 78 transformer layers, 256 routed experts plus one shared expert in each of the 77 MoE layers, top-8 routed experts per token, a native multi-token-prediction layer, and a 1,048,576-token context window. Tencent explicitly calls this an early version and lists overlong reasoning and over-verification among the known limitations.

Primary sources: Tencent release, August 28, 2026 and official Hy4 preview repository.

The open-weight release is significant, but several launch numbers are easy to overstate. The strongest coding and agent figures currently circulating are Tencent-reported results, not a matched independent reproduction of the exact checkpoint, harness and inference settings. The practical story is also split: hosted access is relatively inexpensive, while self-hosting the full model remains a serious systems project even after aggressive quantization.

What Tencent actually released

The official model card describes a 770B-parameter MoE backbone with 49B active parameters. A separate native MTP layer adds about 10B total parameters and 0.7B active parameters for speculative decoding, which explains why some storage listings describe the package around 780B while Tencent's headline model size is 770B for the backbone.

Hy4 preview uses Gated DeepSeek Sparse Attention with IndexCache and four residual streams. Tencent publishes both the instruct weights and an FP8 variant under Apache 2.0 and provides vLLM and SGLang deployment recipes. The official example for the FP8 checkpoint uses tensor parallelism across eight GPUs, which is a useful reminder that "open weights" does not mean "easy to run on an ordinary desktop."

Tencent says the model is available through WorkBuddy, CodeBuddy, Yuanbao, ima, Tencent Cloud TokenHub and OpenRouter in addition to self-hosting.

SWE-bench: Pro is reported; Verified is not

Tencent's launch benchmark appendix reports 65.7 on SWE-bench Pro and 82.9 on SWE-bench Multilingual for Hy4 preview. Public benchmark transcriptions of Tencent's chart describe the SWE-bench Pro run as using a SWE-agent-style scaffold, but Tencent has not published enough task-level artifacts, retries, token budgets and exact serving configuration for this run to count as an independent reproducible evaluation.

The provenance therefore matters more than the headline:

SWE-bench Verified and SWE-bench Pro are different test sets. I found no reliable Tencent-published Hy4 preview score for SWE-bench Verified in the reviewed launch material, and I did not infer one from the 65.7 Pro result.

A secondary benchmark index that transcribes Tencent's launch data is useful for tracking the named harnesses, but it remains secondary evidence rather than an independent rerun: Lab Index Hy4 preview.

Terminal-Bench 2.1: 85.4 is not a Terminal-Bench 4.0 score

Tencent reports 85.4 on Terminal-Bench 2.1. That result belongs to the older 2.1 benchmark family. Terminal-Bench 2.1 contains 89 tasks; the current Terminal-Bench 4.0 benchmark has 66 tasks, including revised environments and tasks, and is evaluated through the Harbor harness.

The current 4.0 leaderboard includes very different absolute resolution rates—for example, the leading public entries are around the high-50% range—because this is a different benchmark revision. There is therefore no valid arithmetic comparison such as "Hy4 85.4 beats a 58% Terminal-Bench 4.0 model." Those numbers do not measure the same test.

I found no accepted Hy4 preview Terminal-Bench 4.0 result during this review. Until one appears with a pinned checkpoint, agent, effort level and task-level run data, the correct label is:

• Terminal-Bench 2.1: 85.4, Tencent/vendor-reported
• Terminal-Bench 4.0: not reported / not independently verified for Hy4 preview

Current 4.0 methodology and leaderboard: Terminal-Bench 4.0.

Tencent's 163-person internal study is useful, but it is still internal

Tencent also ran a blind side-by-side evaluation with 163 internal experts on 203 engineering tasks. Hy4 preview averaged 2.99, compared with 2.92 for GLM 5.3 and 2.94 for Kimi K3. Against GLM 5.3, Tencent reports 46.8% wins, 12.8% ties and 40.4% losses; against Kimi K3, 51.2% wins, 7.9% ties and 40.9% losses.

This is more informative than an unspecified anecdote because Tencent discloses the evaluator and task counts. It is still a company-run evaluation inside Tencent's productivity environment, and the margins are narrow. Tencent itself describes Hy4 as only slightly ahead in those comparisons. It should not be rephrased as a decisive independent win.

The 213.66GiB quantization is impressive—and still enormous

The most interesting post-launch development is Tencent's AngelSlim mixed-precision GGUF work. The current AngelSlim repository lists:

The MIX-STQ1_0 recipe uses 1.3125-bit STQ1_0 for routed expert gate/up projections in 29 layers and 2.0625-bit IQ2_XXS in the other 48. Tencent's August 29 quantization post described the result as roughly 200GiB and reported only modest movement on four selected internal/vendor benchmark rows:

• MCP-Atlas: 83.7 → 83.2
• SWE-bench Multilingual: 82.9 → 81.3
• MRCR: 81.3 → 81.1
• IFBench: 73.5 → 72.5

Those deltas are promising but remain Tencent/AngelSlim-reported selected evaluations, not an independent full-regression suite. They do not establish that every capability, very-long-context behavior, tool-use pattern or production workload survives 2.38-bit mixed quantization equally well.

The exact GGUF and runtime notes are published here: AngelSlim Hy4-preview-GGUF. The repository documents a patched llama.cpp workflow for the Hy4 architecture/STQ format. A 213.66GiB weight file by itself is also far beyond normal consumer-GPU VRAM, before considering runtime overhead and long-context KV cache. CPU/RAM or mixed offload can change the hardware equation, so a single VRAM requirement should not be invented.

Tencent's original quantization discussion is dated August 29, 2026: Tencent Hy on X.

Hosted economics are much friendlier than self-hosting

OpenRouter currently lists tencent/hy4-preview at:

• $0.834 per million input tokens
• $2.501 per million output tokens
• $0.042 per million cache-read tokens
• 1,048,576-token context
• up to 64,000 output tokens

It supports tool calling and JSON-schema structured output. These are current provider prices and limits, not a guarantee that every provider or Tencent direct endpoint has identical economics.

Current provider page: OpenRouter — Tencent Hy4 preview.

I did not find a reliable independent exact-checkpoint latency/throughput benchmark with a disclosed provider, hardware, prompt distribution and reasoning setting that I would use to compare Hy4 fairly with GPT-6 Astra, Fable 5.1, GLM 5.3 or Kimi K3. Tencent has discussed inference-system optimizations, but a vendor infrastructure uplift is not the same thing as an end-user tokens-per-second figure.

Public feedback: hardware and value dominate, but the sample is thin

Tencent's launch post on X on August 28 invited users to try the preview and report what breaks: launch post. The following day's quantization post drew attention because it made a 770B-class model substantially more storable.

Accessible Reddit discussion is still small and self-selected. An August 31 thread about the roughly 200GiB quantization focused heavily on whether hardware such as older GPUs or Apple/MLX setups could realistically run it. Another pricing discussion questioned where Hy4 fits against cheaper or similarly priced alternatives. These are useful signals about buyer concerns, not measured quality evidence, and the small samples do not justify a community-consensus claim.

Public discussion: r/AIGuild, August 31, 2026 and r/AIToolsPerformance, August 31, 2026.

I did not find a stable, independently reproducible X thread with pinned Hy4 checkpoint, harness and task traces that was strong enough to treat as benchmark evidence. Most high-visibility launch-day X material I found came from Tencent or integration partners.

Practical verdict

Hy4 preview is a credible open-weight frontier-scale release with unusually large context, broad agent/coding ambition and competitive vendor-reported benchmark results. Its 65.7 SWE-bench Pro and 85.4 Terminal-Bench 2.1 should be reported exactly with those benchmark names and provenance. They should not be silently converted into SWE-bench Verified or Terminal-Bench 4.0 results.

The 213.66GiB MIX-STQ1_0 artifact is technically notable because it cuts storage dramatically while Tencent reports small losses on four selected tests, but it remains a heavy deployment and the quantization quality evidence is still vendor-run. For most developers, hosted API access is the practical way to test Hy4 today.

The most valuable next evidence would be: an independent exact-checkpoint SWE-bench Pro reproduction; a true SWE-bench Verified run if someone chooses to publish one; Terminal-Bench 4.0 with a pinned agent and effort setting; matched latency/throughput and cost-per-success tests against current peers; and a broader independent low-bit regression suite covering long context, tool use and software engineering rather than four selected benchmarks.