o1 vs Qwen3 VL 4B Instruct
Both models are evenly matched across the benchmarks. Qwen3 VL 4B Instruct is 116.7x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
o1 outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 1 benchmark (SimpleQA). Both models are evenly matched across the benchmarks.
On price, Qwen3 VL 4B Instruct is roughly 116.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Instruct also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose o1
- you want predictable pricing at $15.00/M input and $60.00/M output
Choose Qwen3 VL 4B Instruct
- cost matters — it's about 116.7x cheaper per token
- 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.
Performance Benchmarks
Comparative analysis across standard metrics
o1 outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 1 benchmark (SimpleQA).
Both models are evenly matched across the benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, o1 ($15.00/1M tokens) is 150.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, o1 ($60.00/1M tokens) is 100.0x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, o1 is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to o1's 200,000 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while o1 is limited to 100,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Instruct supports multimodal inputs, whereas o1 does not.
Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
o1
Qwen3 VL 4B Instruct
License
Usage and distribution terms
o1 is licensed under a proprietary license, while Qwen3 VL 4B 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
o1 was released on 2024-12-17, while Qwen3 VL 4B Instruct was released on 2025-09-22.
Qwen3 VL 4B Instruct is 9 months newer than o1.
Dec 17, 2024
1.7 years ago
Sep 22, 2025
11 months ago
9mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
o1 is available from Azure, OpenAI. Qwen3 VL 4B Instruct is available from DeepInfra.
o1
Qwen3 VL 4B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against o1 and Qwen3 VL 4B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about o1 vs Qwen3 VL 4B Instruct.