GPT-4o mini vs Qwen3 VL 4B Thinking
Qwen3 VL 4B Thinking leads the LLM Stats Score 12.9 to 3.6. GPT-4o mini is 1.2x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3 VL 4B Thinking leads the overall LLM Stats Score 12.9 to 3.6, ranking #248 overall.
The models split the 2 individual benchmarks reported for both models evenly.
On price, GPT-4o mini is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Thinking also accepts a larger context window (262,144 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
- cost matters — it's about 1.2x cheaper per token
Choose Qwen3 VL 4B Thinking
- overall performance matters — it scores 12.9 and ranks #248 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
- 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 · 48 for Qwen3 VL 4B Thinking
GPT-4o mini outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Thinking is better at 1 benchmark (GPQA).
Both models are evenly matched across the 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 1.5x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, GPT-4o mini ($0.60/1M tokens) is 1.7x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking 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
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to GPT-4o mini's 128,000 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while GPT-4o mini is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-4o mini and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-4o mini
Qwen3 VL 4B Thinking
License
Usage and distribution terms
GPT-4o mini is licensed under a proprietary license, while Qwen3 VL 4B Thinking 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 Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 14 months newer than GPT-4o mini.
Jul 18, 2024
2.2 years ago
Sep 22, 2025
11 months ago
1.2yr newerKnowledge Cutoff
When training data ends
GPT-4o mini has a documented knowledge cutoff of 2023-10-01, while Qwen3 VL 4B Thinking'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 Qwen3 VL 4B Thinking's cutoff date.
Oct 2023
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Provider Availability
GPT-4o mini is available from Azure. Qwen3 VL 4B Thinking is available from DeepInfra.
GPT-4o mini
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4o mini and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o mini vs Qwen3 VL 4B Thinking.