Llama 3.1 70B Instruct
Zero-eval
#1GSM-8K (CoT)
#1MBPP ++ base version
#1MATH (CoT)
+9 more
by Meta
+
+
+
+
About
Llama 3.1 70B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 74.7% across 18 benchmarks. It excels particularly in GSM-8K (CoT) (95.1%), ARC-C (94.8%), API-Bank (90.0%). It supports a 256K token context window for handling large documents. The model is available through 9 API providers. Released in 2024, it represents Meta's latest advancement in AI technology.
+
+
+
+
Pricing Range
Input (per 1M)$0.20 -$5.00
Output (per 1M)$0.20 -$10.00
Providers9
+
+
+
+
Timeline
AnnouncedJul 23, 2024
ReleasedJul 23, 2024
+
+
+
+
Specifications
Training Tokens15.0T
+
+
+
+
License & Family
License
Llama 3.1 Community License
Performance Overview
Performance metrics and category breakdown
Overall Performance
18 benchmarks
Average Score
74.7%
Best Score
95.1%
High Performers (80%+)
10Performance Metrics
Max Context Window
256.0KAvg Throughput
213.4 tok/sAvg Latency
0ms+
+
+
+
All Benchmark Results for Llama 3.1 70B Instruct
Complete list of benchmark scores with detailed information
| GSM-8K (CoT) | text | 0.95 | 95.1% | Self-reported | |
| ARC-C | text | 0.95 | 94.8% | Self-reported | |
| API-Bank | text | 0.90 | 90.0% | Self-reported | |
| IFEval | text | 0.88 | 87.5% | Self-reported | |
| Multilingual MGSM (CoT) | text | 0.87 | 86.9% | Self-reported | |
| MBPP ++ base version | text | 0.86 | 86.0% | Self-reported | |
| MMLU (CoT) | text | 0.86 | 86.0% | Self-reported | |
| BFCL | text | 0.85 | 84.8% | Self-reported | |
| MMLU | text | 0.84 | 83.6% | Self-reported | |
| HumanEval | text | 0.81 | 80.5% | Self-reported |
Showing 1 to 10 of 18 benchmarks