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Measurement sources and verification status (18 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: 0.55

contextWindow: 1000000

inputPrice: 2

outputPrice: 10

benchmarkMmlu: 88.5

benchmarkHumanEval: 92

benchmarkMath: 92.4

benchmarkGpqa: 70.2

reasoningScore: 81

codingScore: 92

benchmarkHle: 64.5

benchmarkSweBench: 70.3

aaIntelligenceIndex: 56

aaCostPerTask: 0.35

aaOutputSpeed: 141

aaLatency: 1.15

aaEndToEndTime: 5.58

Anthropic: Claude Sonnet 5.5

anthropic🔒 Proprietary

LLM Stats Scores

81.0

Reasoning

92.0

Coding

🎯

70.2%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 ↗

64.5%

Humanity's Last Exam

70.3%

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

Cost / task

$0.35

AA measured

Output speed

141

tokens / second

TTFT

1.15s

AA measured

Response time

5.58s

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

138

tokens / second

Collected 11/11/2026

Quality Score

86.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.55s

to first token

Input Price

per 1M tokens

Output Price

$10

per 1M tokens

Context Window

tokens

Release Date

2026-09-28

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond70.2%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.

🧠

MMLU88.5%Top tier

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

💻

HumanEval92.0%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 Benchmark92.4%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%.