📡 LLM Pulse|🆕 New on leaderboard: Baidu: ERNIE-Light-DT-1.0 by Baidu — $1.13/M tokens1/10
Measurement sources and verification status (14 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: 0.42
contextWindow: 131072
inputPrice: 0.72
outputPrice: 0.72
benchmarkMmlu: 83.6
benchmarkHumanEval: 86
benchmarkMath: 68
benchmarkHellaSwag: 86.6
benchmarkGpqa: 40.3
benchmarkMmluPro: 56
reasoningScore: 57
codingScore: 80.5
benchmarkSweBench: 34

Meta: Llama 3.1 70B Instruct

meta-llama🔒 Proprietary
5.9
LLM Stats Scores
57.0
Reasoning
80.5
Coding
🎯
40.3%GPQA Diamond — graduate-level science accuracy
56.0%MMLU-Pro
Published benchmark evidence
Task scores are reported independently by each publisher; they are not combined into a hidden quality score.
View HLE source ↗
4.5%
Humanity's Last Exam
34.0%
SWE-bench
Quality Score
70.0
Overall (0–100)
Speed
80 c/s
characters/second
Latency (TTFT)
0.42s
to first token
Input Price
$0.72
per 1M tokens
Output Price
$0.72
per 1M tokens
Context Window
131K
tokens
Release Date
2024-07-23

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy
0%25%50%75%100%GPQA ◆MMLU-ProMMLUHumanEvalMATHHellaSwag40.3%56.0%83.6%86.0%68.0%86.6%
What do these benchmarks test?
🎯
GPQA Diamond40.3%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.
📚
MMLU-Pro56.0%Below avg
The harder successor to MMLU — more options per question eliminate single-answer guessing. Tests depth of professional knowledge across 14 disciplines.
🧠
MMLU83.6%Solid
The classic academic breadth benchmark. Top frontier models now score above 85%, so it best distinguishes mid-tier from strong generalist models.
💻
HumanEval86.0%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 Benchmark68.0%Solid
Tests multi-step mathematical reasoning under strict correctness. Even a 60% score requires strong chain-of-thought; top reasoning models now exceed 90%.
🌐
HellaSwag86.6%Solid
Tests grounded, everyday common sense rather than memorised facts. Most frontier models now score 90%+, so it best separates small/mid models from large ones.

💰 Cost Calculator

Input cost
$0.72
Output cost
$0.36
Total / month
$1.08
Annual cost
$12.96
Input: $0.72/1M
Output: $0.72/1M
Rate: 1 USD = 1.00 USD · 2026-11-10
67.1% cheaper than average
Cheaper alternatives:
Z.ai: GLM 5.2$0.007/1M · Q73

AI Score

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8.3/10

"Industry‑leading 70B instruct model with strong benchmark results, affordable pricing, and wide ecosystem support"

🤖 AI Analysis