Qwen2.5 7B Instruct

Text input Text output
Author's Description

Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 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
Oct 15, 2024
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Stop Presence Penalty Top P Temperature Structured Outputs Response Format Frequency Penalty Max Tokens
Features

This model supports the following features:

Structured Outputs Response Format
Performance Summary

Qwen2.5 7B Instruct demonstrates moderate speed performance, ranking in the 33rd percentile across various benchmarks. However, it consistently offers highly competitive pricing, placing in the 93rd percentile, making it a cost-effective option. The model exhibits strong reliability, with few technical issues, achieving an 87th percentile ranking. In terms of specific capabilities, Qwen2.5 7B Instruct shows a notable strength in coding, achieving 83.0% accuracy in the Coding (Baseline) benchmark, placing it in the 64th percentile. It also performs well in email classification with 94.0% accuracy. While its instruction following capability is moderate at 45.5% accuracy, the model's performance in Ethics (61.0% accuracy) and General Knowledge (76.5% accuracy) benchmarks is comparatively lower, ranking in the 19th and 25th percentiles respectively, indicating areas for potential improvement. Its ability to generate long texts and understand structured data are highlighted as general improvements over its predecessor.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.04
Completion $0.1

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
NextBit
NextBit | qwen/qwen-2.5-7b-instruct 32K $0.04 / 1M tokens $0.1 / 1M tokens
DeepInfra
DeepInfra | qwen/qwen-2.5-7b-instruct 32K $0.04 / 1M tokens $0.1 / 1M tokens
Phala
Phala | qwen/qwen-2.5-7b-instruct 32K $0.04 / 1M tokens $0.1 / 1M tokens
Together
Together | qwen/qwen-2.5-7b-instruct 32K $0.3 / 1M tokens $0.3 / 1M tokens
Novita
Novita | qwen/qwen-2.5-7b-instruct 32K $0.07 / 1M tokens $0.07 / 1M tokens
NextBit
NextBit | qwen/qwen-2.5-7b-instruct 65K $0.04 / 1M tokens $0.1 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Free Executions Accuracy Cost Duration
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