📡 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 (20 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: 1000000

inputPrice: 5

outputPrice: 25

benchmarkMmlu: 96

benchmarkHumanEval: 97.5

benchmarkMath: 98

benchmarkGpqa: 95

reasoningScore: 86

codingScore: 92.5

benchmarkHle: 64.7

benchmarkSweBench: 72

benchmarkAutoBench: 26

benchmarkBrowseComp: 90.8

aaIntelligenceIndex: 63

aaCostPerTask: 0.43

aaOutputSpeed: 62

aaLatency: 2.34

aaEndToEndTime: 10.91

Claude Opus 5

anthropic🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

86.0

Reasoning

92.5

Coding

🎯

95.0%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.

View HLE source ↗

64.7%

Humanity's Last Exam

72.0%

SWE-bench

26.0%

AutoBench

90.8%

BrowseComp

Artificial Analysis measurements

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

Intelligence

AA Index

Cost / task

$0.43

AA measured

Output speed

tokens / second

TTFT

2.34s

AA measured

Response time

10.91s

end to end

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

59.73s

BenchLM measured

Collected 11/11/2026

Quality Score

89.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.48s

to first token

Input Price

per 1M tokens

Output Price

$25

per 1M tokens

Context Window

tokens

Release Date

2026-07-24

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond95.0%Top tier

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.

🧠

MMLU96.0%Top tier

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

💻

HumanEval97.5%Top tier

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

∑

MATH Benchmark98.0%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%.