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Measurement sources and verification status (16 fields)

Collected third-party figures are not independent tests. A source link and collection date do not establish reproducibility. Exact model version, dataset and test settings may be missing. No independent verification date is supplied.

speed: 90

latency: 0.5

contextWindow: 262144

inputPrice: 0.132

outputPrice: 0.528

benchmarkMmlu: 78

benchmarkHumanEval: 78

benchmarkMath: 74

benchmarkGpqa: 60

reasoningScore: 67

codingScore: 78

aaIntelligenceIndex: 25

aaCostPerTask: 0.07

aaOutputSpeed: 94

aaLatency: 40.47

aaEndToEndTime: 29.25

Tencent: Hy3

tencent🔒 Proprietary

Hy3 preview is a high-efficiency Mixture-of-Experts model from Tencent designed for agentic workflows and production use. It supports configurable reasoning levels across disabled, low, and high modes, allowing it to...

LLM Stats Scores

67.0

Reasoning

78.0

Coding

🎯

60.0%GPQA Diamond — graduate-level science accuracy

Artificial Analysis measurements

Published operational results. Cost per task, speed, and latency are kept separate from provider API prices.

View source ↗

Intelligence

AA Index

Cost / task

$0.07

AA measured

Output speed

tokens / second

TTFT

40.47s

AA measured

Response time

29.25s

end to end

Collected 11/11/2026

BenchLM performance measurements

Published output speed and first-token timing, kept separate from provider pricing and other benchmark sources.

View source ↗

Output speed

tokens / second

TTFT

26.41s

BenchLM measured

Collected 11/11/2026

Quality Score

73.0

Overall (0–100)

Speed

90 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

$0.132

per 1M tokens

Output Price

$0.528

per 1M tokens

Context Window

262K

tokens

Release Date

2026-07-06

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond60.0%Below avg

Designed so that even PhD-level human experts average ~65–70%. A score above 80% signals near-expert scientific reasoning that goes far beyond memorisation.

🧠

MMLU78.0%Solid

The classic academic breadth benchmark. Top frontier models now score above 85%, so it best distinguishes mid-tier from strong generalist models.

💻

HumanEval78.0%Solid

Measures whether a model writes functionally correct code — not just plausible-looking syntax. Strong scores (>85%) indicate reliable day-to-day coding assistance.

∑

MATH Benchmark74.0%Solid

Tests multi-step mathematical reasoning under strict correctness. Even a 60% score requires strong chain-of-thought; top reasoning models now exceed 90%.