📡 LLM Pulse|🆕 New on leaderboard: Baidu: ERNIE-Light-DT-1.0 by Baidu — $1.13/M tokens1/10
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: 160
latency: 0.28
contextWindow: 262144
inputPrice: 0.042
outputPrice: 0.1232
benchmarkMmlu: 68
benchmarkHumanEval: 68
benchmarkMath: 64
benchmarkGpqa: 50
reasoningScore: 57
codingScore: 68
aaIntelligenceIndex: 23
aaOutputSpeed: 345
aaLatency: 1.93
aaEndToEndTime: 9.17
I

Ling 3.0 Flash Fin

inclusionai🔒 Proprietary
7.1
LLM Stats Scores
57.0
Reasoning
68.0
Coding
🎯
50.0%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 ↗
22.6%
Humanity's Last Exam
Artificial Analysis measurements
Published operational results. Cost per task, speed, and latency are kept separate from provider API prices.
View source ↗
Intelligence
23
AA Index
Output speed
345
tokens / second
TTFT
1.93s
AA measured
Response time
9.17s
end to end
Collected 10/27/2026
Quality Score
63.0
Overall (0–100)
Speed
160 c/s
characters/second
Latency (TTFT)
0.28s
to first token
Input Price
$0.042
per 1M tokens
Output Price
$0.1232
per 1M tokens
Context Window
262K
tokens
Release Date
2026-08-27

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy
0%25%50%75%100%GPQA ◆MMLUHumanEvalMATH50.0%68.0%68.0%64.0%
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%.

💰 Cost Calculator

Input cost
$0.042
Output cost
$0.062
Total / month
$0.10
Annual cost
$1.24
Input: $0.042/1M
Output: $0.1232/1M
Rate: 1 USD = 1.00 USD · 2026-11-10
96.8% cheaper than average
Cheaper alternatives:
Z.ai: GLM 5.2$0.007/1M · Q73

AI Score

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6.5/10

"Offers large context and low cost but lags behind leading models in reasoning and generation quality."

🤖 AI Analysis