GPT-4o mini vs Qwen2.5 7B Instruct
GPT-4o mini and Qwen2.5 7B Instruct are closely matched at 3.6 and 2.5 on the LLM Stats Score. GPT-4o mini is 1.1x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT-4o mini and Qwen2.5 7B Instruct are closely matched on the overall LLM Stats Score at 3.6 and 2.5.
In the 3 individual benchmarks reported for both models, GPT-4o mini wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-4o mini is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen2.5 7B Instruct also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-4o mini
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- cost matters — it's about 1.1x cheaper per token
Choose Qwen2.5 7B Instruct
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Sep 2024
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for GPT-4o mini · 14 for Qwen2.5 7B Instruct
GPT-4o mini outperforms in 2 benchmarks (GPQA, HumanEval), while Qwen2.5 7B Instruct is better at 1 benchmark (MATH).
GPT-4o mini shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4o mini ($0.15/1M tokens) is 2.0x cheaper than Qwen2.5 7B Instruct ($0.30/1M tokens).
For output processing, GPT-4o mini ($0.60/1M tokens) is 2.0x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
In conclusion, Qwen2.5 7B Instruct is more expensive than GPT-4o mini.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen2.5 7B Instruct accepts 131,072 input tokens compared to GPT-4o mini's 128,000 tokens. GPT-4o mini can generate longer responses up to 16,384 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4o mini supports multimodal inputs, whereas Qwen2.5 7B Instruct does not.
GPT-4o mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4o mini
Qwen2.5 7B Instruct
License
Usage and distribution terms
GPT-4o mini is licensed under a proprietary license, while Qwen2.5 7B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT-4o mini was released on 2024-07-18, while Qwen2.5 7B Instruct was released on 2024-09-19.
Qwen2.5 7B Instruct is 2 months newer than GPT-4o mini.
Jul 18, 2024
2.2 years ago
Sep 19, 2024
2.0 years ago
2mo newerKnowledge Cutoff
When training data ends
GPT-4o mini has a documented knowledge cutoff of 2023-10-01, while Qwen2.5 7B Instruct's cutoff date is not specified.
We can confirm GPT-4o mini's training data extends to 2023-10-01, but cannot make a direct comparison without Qwen2.5 7B Instruct's cutoff date.
Oct 2023
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Provider Availability
GPT-4o mini is available from Azure. Qwen2.5 7B Instruct is available from Together.
GPT-4o mini
Qwen2.5 7B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4o mini and Qwen2.5 7B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o mini vs Qwen2.5 7B Instruct.