Qwen 2 72B Instruct

Text input Text output
Author's Description

Qwen2 72B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning. It features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization. For more details, see this [blog post](https://qwenlm.github.io/blog/qwen2/) and [GitHub repo](https://github.com/QwenLM/Qwen2). Usage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).

Key Specifications
Cost
$$$$
Context
32K
Parameters
500B (Rumoured)
Released
Jun 06, 2024
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Stop Presence Penalty Logit Bias Top P Temperature Min P Frequency Penalty Max Tokens
Performance Summary

Qwen 2 72B Instruct demonstrates a balanced performance profile, excelling in certain areas while showing room for improvement in others. In terms of speed, the model performs among the faster options, ranking in the 62nd percentile across various benchmarks. Its pricing is competitive, positioned at the 42nd percentile, making it a cost-effective choice for many applications. Notably, its reliability is a significant strength, with an 83rd percentile ranking, indicating consistent and usable responses with minimal technical failures. Analyzing benchmark results, Qwen 2 72B Instruct shows exceptional performance in Ethics, achieving perfect accuracy and being the most accurate model at its price point and speed. It also performs very well in Email Classification (99% accuracy), highlighting its strong capabilities in structured classification tasks. Instruction Following and Reasoning show moderate accuracy (53% and 64% respectively), suggesting solid foundational understanding. However, the model exhibits weaknesses in Coding (28% accuracy) and General Knowledge (20% accuracy), indicating these areas may require further refinement. Overall, its strengths lie in ethical reasoning, classification, and reliability, while coding and broad general knowledge are areas for development.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.9
Completion $0.9

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
Together
Together | qwen/qwen-2-72b-instruct 32K $0.9 / 1M tokens $0.9 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Free Executions Accuracy Cost Duration
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