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Measurement sources and verification status (11 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: 48

latency: 0.95

contextWindow: 131072

benchmarkMmlu: 88.6

benchmarkHumanEval: 89

benchmarkMath: 73.8

benchmarkGpqa: 51.1

reasoningScore: 62

codingScore: 89

benchmarkSweBench: 41.5

aaIntelligenceIndex: 7

Llama 3.1 405B Instruct

meta🔒 Proprietary

Published Humanity’s Last Exam leaderboard evidence.

LLM Stats Scores

62.0

Reasoning

89.0

Coding

🎯

51.1%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 ↗

4.0%

Humanity's Last Exam

41.5%

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

Collected 10/27/2026

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

2.19s

BenchLM measured

Collected 9/19/2026

Quality Score

76.0

Overall (0–100)

Speed

48 c/s

characters/second

Latency (TTFT)

0.95s

to first token

Input Price

—

per 1M tokens

Output Price

—

per 1M tokens

Context Window

131K

tokens

Release Date

2024-07

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond51.1%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.

🧠

MMLU88.6%Top tier

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

💻

HumanEval89.0%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 Benchmark73.8%Solid

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