Tencent Hy4 Intelligence, Performance and Price Analysis

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Image, Speech, Video

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Tencent

•Proprietary model

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Model summary

IntelligenceUpdated

#43 / 106

35

Artificial Analysis Intelligence Index

3 out of 4 units for Intelligence.

Performance

N/A

Time per Intelligence Index task (minutes)

Unknown out of 4 units for Performance.

Cost

In -Out -

N/A

Cost per Intelligence Index task

Unknown out of 4 units for Cost.

Verbosity

#94 / 104

Input tokens 6.08BOutput/input 2.623%

160M

Output tokens from Intelligence Index

4 out of 4 units for Verbosity.

Comparison Summary

Tencent Hy4 is above average in intelligence when comparing to other models. The model supports text and image input, outputs text, and has a 1M tokens context window.

Tencent Hy4 scores 35 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (median: 30). When evaluating the Intelligence Index, it generated 160M tokens, which is very verbose in comparison to the median of 75M.

ReasoningYesThis page shows the reasoning version of this model.

Context window1M~1573 A4 pages of size 12 Arial font

725 models in this class

Metrics are compared against models of the same class:

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

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Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Open Weights

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

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Most attractive quadrant

Pareto line

Thinking MachinesXiaomiOpenAIAnthropicMetaInclusionAISpaceXAIKimiReflectionGoogleAlibabaZ AIMiniMaxNVIDIAMistralDeepSeek

Cost per Intelligence Index Task

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Measures the performance of models on specific capabilities and industries

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

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Benchmarks

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexPersonalityLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more

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AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 2.0New

Agentic software engineering in open-source repositories

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

Intelligence Evaluation Relevance

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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AA-Briefcase Elo

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

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AA-Omniscience Net Score

AA-Omniscience v1.1 Net Score (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

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No publicly available API

AnswerReasoning

Output Tokens per Intelligence Index Task

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

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No publicly available API

AnswerReasoningCache WriteCache HitInput

Cost per Intelligence Index Task

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

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OutputReasoningCache WriteCache ReadNon-Cache Input

Cost to Run Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

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Cache HitInputOutput

Cache Hit

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

4 more notes

Context Window

Context WindowIntelligence Index vs. Context Window

Context Window

Context window: tokens limit · Higher is better

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Context Window for RAG

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Context Window

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Tencent Hy4 was released on November 3, 2026.

Tencent Hy4 scores 35 on the Artificial Analysis Intelligence Index, placing it above average among other models (median: 30).

When evaluated on the Intelligence Index, Tencent Hy4 generated 160M output tokens, which is at the higher end compared to other models (median: 75M).

Yes, Tencent Hy4 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Tencent Hy4 supports text and image input.

Yes, Tencent Hy4 supports image input and can analyze, describe, and answer questions about images.

Yes, Tencent Hy4 is multimodal. It can process text and image input and generate text output.

Tencent Hy4 has a context window of 1M tokens. This determines how much text and conversation history the model can process in a single request.

No, Tencent Hy4 is proprietary. The model weights are not publicly available.

Tencent Hy4 is a proprietary model and Tencent has not disclosed the model size or parameter count.

Tencent Hy4 achieves a score of 35 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.

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