Qwen2.5 72B Instruct vs Qwen3 VL 4B Thinking
Qwen2.5 72B Instruct and Qwen3 VL 4B Thinking are closely matched at 12.1 and 12.9 on the LLM Stats Score. Qwen3 VL 4B Thinking is 1.1x cheaper per token.
Alibaba Cloud / Qwen Team · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen2.5 72B Instruct and Qwen3 VL 4B Thinking are closely matched on the overall LLM Stats Score at 12.1 and 12.9.
The models split the 4 individual benchmarks reported for both models evenly.
On price, Qwen3 VL 4B Thinking is roughly 1.1x 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 Qwen2.5 72B Instruct
- you want predictable pricing at $0.35/M input and $0.40/M output
Choose Qwen3 VL 4B Thinking
- cost matters — it's about 1.1x 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
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for Qwen2.5 72B Instruct · 48 for Qwen3 VL 4B Thinking
Qwen2.5 72B Instruct outperforms in 2 benchmarks (IFEval, MMLU-Redux), while Qwen3 VL 4B Thinking is better at 2 benchmarks (GPQA, MMLU-Pro).
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, Qwen2.5 72B Instruct ($0.35/1M tokens) is 3.5x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, Qwen2.5 72B Instruct ($0.40/1M tokens) is 2.5x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen2.5 72B Instruct is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen2.5 72B Instruct has 68.7B more parameters than Qwen3 VL 4B Thinking, making it 1717.5% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to Qwen2.5 72B Instruct's 131,072 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Qwen2.5 72B Instruct is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 4B Thinking supports multimodal inputs, whereas Qwen2.5 72B Instruct does not.
Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
Qwen2.5 72B Instruct
Qwen3 VL 4B Thinking
License
Usage and distribution terms
Qwen2.5 72B Instruct is licensed under Qwen, 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.
Qwen
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Qwen2.5 72B Instruct was released on 2024-09-19, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 12 months newer than Qwen2.5 72B Instruct.
Sep 19, 2024
2.0 years ago
Sep 22, 2025
11 months ago
1.0yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Qwen2.5 72B Instruct is available from DeepInfra, Hyperbolic, Fireworks, Together. Qwen3 VL 4B Thinking is available from DeepInfra.
Qwen2.5 72B Instruct
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Qwen2.5 72B Instruct and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Qwen2.5 72B Instruct vs Qwen3 VL 4B Thinking.