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Measurement sources and verification status (9 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: 0.5

contextWindow: 128000

benchmarkMmlu: 78

benchmarkHumanEval: 78

benchmarkMath: 74

benchmarkGpqa: 60

reasoningScore: 67

codingScore: 78

Body Builder (beta)

openrouter🔒 Proprietary

Transform your natural language requests into structured OpenRouter API request objects. Describe what you want to accomplish with AI models, and Body Builder will construct the appropriate API calls. Example:...

LLM Stats Scores

67.0

Reasoning

78.0

Coding

🎯

60.0%GPQA Diamond — graduate-level science accuracy

Quality Score

73.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

—

per 1M tokens

Output Price

—

per 1M tokens

Context Window

128K

tokens

Release Date

2025-12-05

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond60.0%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.

🧠

MMLU78.0%Solid

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

💻

HumanEval78.0%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 Benchmark74.0%Solid

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