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Measurement sources and verification status (16 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: 0.28

contextWindow: 1048576

inputPrice: 2

outputPrice: 10

benchmarkMmlu: 68

benchmarkHumanEval: 68

benchmarkMath: 64

benchmarkGpqa: 50

reasoningScore: 57

codingScore: 68

aaIntelligenceIndex: 60

aaCostPerTask: 6.59

aaOutputSpeed: 114

aaLatency: 3.23

aaEndToEndTime: 11.62

Google: Gemini 4 Carbon

google🔓 Open Source

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

57.0

Reasoning

68.0

Coding

🎯

50.0%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

$6.59

AA measured

Output speed

114

tokens / second

TTFT

3.23s

AA measured

Response time

11.62s

end to end

Collected 11/10/2026

BenchLM performance measurements

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

View source ↗

Output speed

100

tokens / second

TTFT

97.48s

BenchLM measured

Collected 11/11/2026

Quality Score

63.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.28s

to first token

Input Price

per 1M tokens

Output Price

$10

per 1M tokens

Context Window

tokens

Release Date

2026-11-10

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond50.0%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.

🧠

MMLU68.0%Below avg

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

💻

HumanEval68.0%Below avg

Measures whether a model writes functionally correct code — not just plausible-looking syntax. Strong scores (>85%) indicate reliable day-to-day coding assistance.

∑

MATH Benchmark64.0%Solid

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