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Hy4 matches Opus on Tencent's own agent test at 1/8 the price, but weights stay under lock

Tencent Hunyuan Hy4 Official Release: 1/8 of the Price Touches Opus 5.5, Can See Images and Operate a Computer, Weights Still Not Released

Author: lucas_AI

💡 One-sentence summary: On November 4, Tencent Hunyuan released the official Hy4—parameters are still 770B, but it has learned to "see images" and "operate a computer" for the first time, and the official line is that it has entered the ranks of the strongest open-source models for the first time. On Tencent's own agent benchmark, it scored 75.2, slightly above Claude Opus 5.5's 74.8, while its output price is only about 1/8 of the latter's. However, third-party benchmark rankings place it more in the middle tier, and model weights had not been released as of press time. The API is callable on the same day; weights need a few more days.

Introduction: A "Major Version" with No Parameter Increase

First, a number that's easy to misread: the official Hy4 has 770B total parameters and 49B activated—exactly the same as the Hy4 preview open-sourced on August 28.

So what does this upgrade change? The official explanation is "further expanding the scale of pre-training and post-training": the body hasn't moved; what changed is the data fed in and the training methods. Along the way, it fills the two gaps missing from the flagship model—visual understanding (previously the Hunyuan flagship couldn't process images) and Computer Use (previously it couldn't operate a computer; now both command-line and graphical interfaces work, and training specifically covered professional software like Blender and CAD).

From the late-August preview to the early-November official release, more than two months passed. After Hunyuan rebuilt its infrastructure in February this year, it has roughly maintained a cadence of one major version every two months: Hy3 was under 300B, Hy4 reached 770B, and the official preview indicates the next generation, Hy5, will be larger.

Want to try it yourself?

How to Read the Benchmarks: Strengths and Weaknesses Both on the Table

The official release includes a full dashboard of 12 benchmarks, with both highlights and weaknesses visible. First, the two most talked-about numbers.

The most striking set: On APEX-Agents v1.1 (an agent-task benchmark), Hy4 scored 75.2, while Tencent's same-methodology measurement of Claude Opus 5.5 was 74.8 and DeepSeek-V4.1-Flash-1017 was 72.9. Combined with pricing, the picture becomes more telling: Hy4's output costs $2.501/million tokens, while Opus 5.5 on OpenRouter is $20/million—roughly 1/8 the price. Even if you discount all of Tencent's self-reported numbers, the cost-performance narrative holds.

The most uncomfortable set: On Terminal-Bench 4.0 (a command-line programming test), Hy4 scored only 44.4, while Opus 5.5 and Kimi K3.1 Pro both sit around 66—a gap of more than 20 points. The official dashboard didn't hide this number, and the Hunyuan team's own words are blunt: "Although Hy4 is already one of the strongest open-source models, there is still a significant gap with top closed-source models"—most benchmarks do fall below GPT-6.1 Sol and Claude Opus 5.5.

The middle ground is its tussle with open-source peers: by official numbers, Hy4 exceeds DeepSeek-V4.1-Flash-1017 on ProgramBench, FrontierCode 2.0, Agents' Last Exam, and other benchmarks, with mixed results against Kimi K3.1 Pro. Third-party Agent Arena is cooler: 14th place (preview was 25th, an improvement of 3.6 percentage points), with a per-task cost of $0.55, about 60% of Kimi K3.1 Pro's $1.46, and a score gap within 1 percentage point—spends less, hangs in there. That's the most solid positioning for Hy4 in third-party data.

Vertical media A2Agent, after breaking down the official dashboard image by image, came to a similar conclusion: mixed wins against open-source peers (GLM 5.3, DeepSeek, Kimi), with some tasks still far from the closed-source frontier.

Office and Finance: The Two Scenarios Tencent Bets Heaviest On

Beyond the dashboard, Tencent promoted two "in-house evaluation" results. The numbers are eye-catching, but they should be read with an extra layer of judgment.

Office collaboration scenario: internal evaluation average 83.18 points, task pass rate 96.15%, with 9 of the 12 mainstream office scenarios ranked first domestically. The deployment form is Tencent's own WorkBuddy (first to integrate, free until November 15), which connects chat records, emails, documents, and schedules within WeChat Work, automatically organizing project progress.

Finance scenario: 213 experts (153 external) blindly tested 579 real financial tasks; Hy4 scored 3.04, beating Kimi K3.1 Pro and GLM-5.3's 2.68.

A clarification: both results come from evaluations organized by Tencent itself, not public benchmarks. What a model is good at, who sets the test questions, and how scenarios are chosen are all determined by the vendor—treat these as "the home field where the vendor is most confident." Third-party Agent Arena ranks it 14th, and the gap between the two measurement styles is itself worth pondering.

Can It Really Work? What Real Users Say

On release day, NetEase Technology's "Attitude" program did a hands-on test, picking down-to-earth scenarios: splitting a restaurant bill, comparing prices on shopping sites, turning messy screenshot guides into a webpage, and building an interactive page. The conclusion: "results were mixed, but most tasks were completed efficiently"—capable, but not omniscient.

Phoenix Net's hands-on test from the August preview period serves as a reference: long-document processing, code generation, data analysis, and game prototypes all "stood up to scrutiny," with the downsides being noticeable long-thought latency and not the lowest price. Community voices are more varied: a user on Linux.do said "I did a detailed review of the preview and found it mediocre," while others complained "the API price isn't very pretty"—the preview period pricing was 6 yuan/million input tokens and 18 yuan/million output tokens, and the official release continues that pricing.

Using it as a "productivity-specialized" model makes expectations more accurate: Tencent didn't set out to be an all-rounder; its bets are on code, office, and finance, the scenarios that can be monetized.

The Real Suspense: When Do the Weights Come?

This is the most dramatic part of the entire release.

On August 28, the preview was "released and open-sourced"—weights were on HuggingFace the same day, the HN post scored 387 points with 259 comments, and r/LocalLLaMA directly had 549 points of discussion. The November 4 official release, by contrast, was "API-first": callable on Tencent Cloud and OpenRouter the same day, with weights officially stated as "planned to be open-sourced in the coming days" along with the system card, but without a date.

The community is watching this gap. A vertical media outlet titled its piece "Newer Model, Less Open": the official release's comparison materials were thinner than the preview's—no general benchmark appendix, and internal suites don't disclose task counts. Overseas media also noticed the "API open but weights not public" status. On HN, some users praised Tencent for open-sourcing scientific models (this release also open-sourced HyCell and HyPDE, two scientific base models), while veteran users poured cold water: "open weights does not equal open source."

In fairness, during the preview period Tencent honored its open-source promise, and the "strongest open-source model" title is unlikely to hang in the air for long—but until the weights are released, all capability claims can only be trusted on an API basis. Local-deployment enthusiasts and reproduction-minded folks have nothing to play with for now. This is the watershed for judging this release: if you use the API, you can start today; if you want self-deployment, wait.

Three Judgments

References