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Measurement sources and verification status (15 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: 0.48
contextWindow: 400000
benchmarkMmlu: 92.8
benchmarkHumanEval: 94
benchmarkMath: 96.5
benchmarkGpqa: 80.5
reasoningScore: 89
codingScore: 94
benchmarkSweBench: 62
aaIntelligenceIndex: 26
aaCostPerTask: 0.57
aaOutputSpeed: 166
aaLatency: 0.86
aaEndToEndTime: 15.9

GPT-5.5 Instant (June 2026)

openai🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

7.6
LLM Stats Scores
89.0
Reasoning
94.0
Coding
🎯
80.5%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.
62.0%
SWE-bench
Artificial Analysis measurements
Published operational results. Cost per task, speed, and latency are kept separate from provider API prices.
View source ↗
Intelligence
26
AA Index
Cost / task
$0.57
AA measured
Output speed
166
tokens / second
TTFT
0.86s
AA measured
Response time
15.90s
end to end
Collected 11/11/2026
Quality Score
91.0
Overall (0–100)
Speed
80 c/s
characters/second
Latency (TTFT)
0.48s
to first token
Input Price
—
per 1M tokens
Output Price
—
per 1M tokens
Context Window
400K
tokens
Release Date
2025-08

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy
0%25%50%75%100%GPQA ◆MMLUHumanEvalMATH
What do these benchmarks test?
🎯
GPQA Diamond80.5%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.
🧠
MMLU92.8%Top tier
The classic academic breadth benchmark. Top frontier models now score above 85%, so it best distinguishes mid-tier from strong generalist models.
💻
HumanEval94.0%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 Benchmark96.5%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%.

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AI Score

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9.5/10

"Industry‑leading quality and versatility, setting the benchmark for 2026 LLMs."

🤖 AI Analysis