Model Comparison

GPT-4o mini vs Qwen3 VL 4B Thinking

Both models are evenly matched across the benchmarks. GPT-4o mini is 1.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

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.

Tue Apr 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GPT-4o mini costs less

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

Lowest available price from all providers

Tue Apr 21 2026 • llm-stats.com

GPT-4o mini

Input tokens$0.15

Output tokens$0.60

Best providerAzure

Qwen3 VL 4B Thinking

Input tokens$0.10

Output tokens$1.00

Best providerDeepinfra

Notice missing or incorrect data?Start an Issue→

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.

GPT-4o mini

Input128,000 tokens

Output16,384 tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Tue Apr 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

Text

Images

Audio

Video

Qwen3 VL 4B Thinking

Text

Images

Audio

Video

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.

GPT-4o mini

Proprietary

Closed source

Qwen3 VL 4B Thinking

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.

GPT-4o mini

Jul 18, 2024

1.8 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

7 months ago

1.2yr newer

Knowledge 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.

GPT-4o mini

Oct 2023

Qwen3 VL 4B Thinking

—

Provider Availability

GPT-4o mini is available from Azure. Qwen3 VL 4B Thinking is available from DeepInfra.

GPT-4o mini

Azure

Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M

Qwen3 VL 4B Thinking

Deepinfra

Input Price:Input: $0.10/1MOutput Price:Output: $1.00/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

GPT-4o mini

View details

OpenAI

Less expensive output tokens

Higher MMLU score (82.0% vs 81.5%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Less expensive input tokens

Has open weights

Higher GPQA score (64.1% vs 40.2%)

Detailed Comparison

AI Model Comparison Table
Feature	GPT-4o mini	Qwen3 VL 4B Thinking

FAQ

Common questions about GPT-4o mini vs Qwen3 VL 4B Thinking

Both models are evenly matched across the benchmarks. GPT-4o mini is made by OpenAI and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

GPT-4o mini scores HumanEval: 87.2%, MGSM: 87.0%, MMLU: 82.0%, DROP: 79.7%, MATH: 70.2%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

Qwen3 VL 4B Thinking is 1.5x cheaper for input tokens. GPT-4o mini costs $0.15/M input and $0.60/M output via azure. Qwen3 VL 4B Thinking costs $0.10/M input and $1.00/M output via deepinfra.

GPT-4o mini supports 128K tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Key differences include context window (128K vs 262K), input pricing ($0.15 vs $0.10/M), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

GPT-4o mini is developed by OpenAI and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.

GPT-4o mini vs Qwen3 VL 4B Thinking

Performance Benchmarks

Arena Performance

Pricing Analysis

Context Window

Input Capabilities

GPT-4o mini

Qwen3 VL 4B Thinking

License

Release Timeline

Knowledge Cutoff

Provider Availability

GPT-4o mini

Qwen3 VL 4B Thinking

Outputs Comparison

Key Takeaways

GPT-4o mini

Qwen3 VL 4B Thinking

Detailed Comparison

FAQ

Which is better, GPT-4o mini or Qwen3 VL 4B Thinking?

How does GPT-4o mini compare to Qwen3 VL 4B Thinking in benchmarks?

Is GPT-4o mini cheaper than Qwen3 VL 4B Thinking?

What are the context window sizes for GPT-4o mini and Qwen3 VL 4B Thinking?

What are the main differences between GPT-4o mini and Qwen3 VL 4B Thinking?

Who makes GPT-4o mini and Qwen3 VL 4B Thinking?