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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: 48

latency: 0.85

contextWindow: 512000

benchmarkMmlu: 82

benchmarkHumanEval: 82

benchmarkMath: 90

benchmarkGpqa: 64

reasoningScore: 77

codingScore: 82

aaIntelligenceIndex: 11

aaCostPerTask: 0.12

aaOutputSpeed: 367

aaLatency: 1.16

aaEndToEndTime: 7.97

Trinity Large Thinking

external🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

77.0

Reasoning

82.0

Coding

🎯

64.0%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

$0.12

AA measured

Output speed

367

tokens / second

TTFT

1.16s

AA measured

Response time

7.97s

end to end

Collected 10/28/2026

BenchLM performance measurements

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

View source ↗

Output speed

206

tokens / second

TTFT

11.09s

BenchLM measured

Collected 8/22/2026

Quality Score

80.0

Overall (0–100)

Speed

48 c/s

characters/second

Latency (TTFT)

0.85s

to first token

Input Price

—

per 1M tokens

Output Price

—

per 1M tokens

Context Window

512K

tokens

Release Date

2024-09

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond64.0%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.

🧠

MMLU82.0%Solid

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

💻

HumanEval82.0%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 Benchmark90.0%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%.