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Measurement sources and verification status (12 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: 160

latency: 0.3

contextWindow: 1048576

inputPrice: 0.1

outputPrice: 0.4

benchmarkMmlu: 87

benchmarkHumanEval: 89

benchmarkMath: 89

benchmarkGpqa: 42

reasoningScore: 80

codingScore: 89

benchmarkSweBench: 46

Google: Gemini 2.5 Flash Lite

google🔒 Proprietary

Published Humanity’s Last Exam leaderboard evidence.

LLM Stats Scores

80.0

Reasoning

89.0

Coding

🎯

42.0%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 ↗

6.8%

Humanity's Last Exam

46.0%

SWE-bench

Quality Score

84.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.30s

to first token

Input Price

$0.1

per 1M tokens

Output Price

$0.4

per 1M tokens

Context Window

tokens

Release Date

2025-Q2

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond42.0%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.

🧠

MMLU87.0%Top tier

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

💻

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 Benchmark89.0%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%.