📡 LLM Pulse|🆕 New on leaderboard: Baidu: ERNIE-Light-DT-1.0 by Baidu — $1.13/M tokensLearn more →1/10

Measurement sources and verification status (14 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: 80

latency: 0.48

contextWindow: 256000

benchmarkMmlu: 86.1

benchmarkHumanEval: 86.4

benchmarkMath: 83.1

benchmarkGpqa: 49

reasoningScore: 66

codingScore: 86.4

benchmarkSweBench: 41

aaIntelligenceIndex: 20

aaOutputSpeed: 126

aaLatency: 2.71

aaEndToEndTime: 6.06

Qwen3.5 Omni Plus

qwen🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

66.0

Reasoning

86.4

Coding

🎯

49.0%GPQA Diamond — graduate-level science accuracy

Published benchmark evidence

Task scores are reported independently by each publisher; they are not combined into a hidden quality score.

41.0%

SWE-bench

Artificial Analysis measurements

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

View source ↗

Intelligence

AA Index

Output speed

126

tokens / second

TTFT

2.71s

AA measured

Response time

6.06s

end to end

Collected 11/11/2026

Quality Score

76.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.48s

to first token

Input Price

—

per 1M tokens

Output Price

—

per 1M tokens

Context Window

256K

tokens

Release Date

2024-09

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond49.0%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.

🧠

MMLU86.1%Solid

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

💻

HumanEval86.4%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 Benchmark83.1%Top tier

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