📡 LLM Pulse|🆕 New on leaderboard: Baidu: ERNIE-Light-DT-1.0 by Baidu — $1.13/M tokensLearn more →1/10

Measurement sources and verification status (17 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: 0.49

contextWindow: 500000

inputPrice: 2

outputPrice: 6

benchmarkMmlu: 90.5

benchmarkHumanEval: 91

benchmarkMath: 94.2

benchmarkGpqa: 78.2

reasoningScore: 86

codingScore: 91

benchmarkSweBench: 52

aaIntelligenceIndex: 46

aaCostPerTask: 1.03

aaOutputSpeed: 110

aaLatency: 4.07

aaEndToEndTime: 10.68

SpaceXAI: Grok 4.7

x-ai🔒 Proprietary

LLM Stats Scores

86.0

Reasoning

91.0

Coding

🎯

78.2%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 ↗

43.1%

Humanity's Last Exam

52.0%

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

Cost / task

$1.03

AA measured

Output speed

110

tokens / second

TTFT

4.07s

AA measured

Response time

10.68s

end to end

Collected 11/11/2026

BenchLM performance measurements

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

View source ↗

Output speed

101

tokens / second

TTFT

40.11s

BenchLM measured

Collected 11/11/2026

Quality Score

88.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.49s

to first token

Input Price

per 1M tokens

Output Price

per 1M tokens

Context Window

500K

tokens

Release Date

2026-09-21

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond78.2%Solid

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.

🧠

MMLU90.5%Top tier

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

💻

HumanEval91.0%Top tier

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

∑

MATH Benchmark94.2%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%.