tencent
Tencent: Hy4
tencent/hy4
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter's catalog describes tencent/hy4 as Tencent's official Hy4 release, positioned as a major improvement over Hy4 Preview for agentic work and long-horizon productivity. The catalog lists text and image inputs, text outputs, a roughly one-million-token context window, optional reasoning enabled by default at high effort, and API controls for tools and structured outputs. Reported benchmark coverage includes Design Arena categories and an Artificial Analysis intelligence index, but coding and agentic index values are absent. An independent model card was not available in this source pack.[1]
Strengths
- Source-reported capability: accepts text and image inputs and produces text.[1]
- Source-reported controls: optional reasoning defaults to high effort, with low and off settings listed.[1]
- Source-reported interface: a roughly one-million-token context window plus API parameters for tools, structured outputs, response format, and generation controls.[1]
Limitations
- Evidence is catalog-only: no independent model card was supplied, so reported capabilities require endpoint validation.[1]
- Measured coverage is incomplete: Design Arena results exist for several categories, but the supplied coding and agentic index fields are empty.[1]
- The listing does not supply license terms, hardware requirements, knowledge cutoff, safety guarantees, or direct comparisons with other models.[1]
Where to use it
- Evaluate for long-context assistants or document workflows if image input and the very large context window fit the workload.[1]
- Trial for tool-using or structured-output applications after confirming the listed API parameters behave as required.[1]
- Test for design or code prototyping only as a hypothesis; Design Arena coverage spans 3D, games, UI, websites, and code categories, so representative validation remains necessary.[1]
Sources
- [1] OpenRouter catalog · Tencent: Hy4Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
The official release of Hy4 delivers comprehensive and substantial improvements over Hy4 Preview, with particularly strong gains in agentic capabilities and performance across a wide range of long-horizon, real-world productivity...
Specifications
- Model ID
- tencent/hy4
- Canonical ID
- tencent/hy4-20261104
- Creator
- tencent
- Added to OpenRouter
- 04.11.2026
- Context length
- 1,048,576
- Modality
- text+image->text
- Input modalities
- text, image
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.417 / out $1.25
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | AA — next 20% of the cohort | reasoning: optional · default high · efforts: high, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default high · efforts: high, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 34.6 | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 48.5% · rank 26 | 1,259Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 47.7% · rank 39 | 1,256Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 49.3% · rank 26 | 1,268Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 38% · rank 65 | 1,194Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 47.6% · rank 42 | 1,253Elo | BB — next 30% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Tencentfp4 | 1,048,576 | $0.417 | $1.25 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.