Model Comparison
Qwen2.5 7B Instruct vs Qwen3 VL 4B ThinkingWhich is better in 2026?
Qwen3 VL 4B Thinking significantly outperforms across most benchmarks. Qwen2.5 7B Instruct is 1.1x cheaper per token.
Verdict: Qwen2.5 7B Instruct vs Qwen3 VL 4B Thinking — which is better?
Qwen2.5 7B Instruct (by Alibaba Cloud / Qwen Team) and Qwen3 VL 4B Thinking (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Qwen2.5 7B Instruct outperforms in 0 benchmarks, while Qwen3 VL 4B Thinking is better at 4 benchmarks (GPQA, IFEval, MMLU-Pro, MMLU-Redux). Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.
On price, Qwen2.5 7B Instruct 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.
Choose Qwen2.5 7B Instruct if…
- cost matters — it's about 1.1x cheaper per token
Choose Qwen3 VL 4B Thinking if…
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
Qwen2.5 7B Instruct outperforms in 0 benchmarks, while Qwen3 VL 4B Thinking is better at 4 benchmarks (GPQA, IFEval, MMLU-Pro, MMLU-Redux).
Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Qwen2.5 7B Instruct ($0.30/1M tokens) is 3.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, Qwen2.5 7B Instruct ($0.30/1M tokens) is 3.3x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking is more expensive than Qwen2.5 7B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen2.5 7B Instruct has 3.6B more parameters than Qwen3 VL 4B Thinking, making it 90.3% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to Qwen2.5 7B Instruct's 131,072 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Thinking supports multimodal inputs, whereas Qwen2.5 7B 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 7B Instruct
Qwen3 VL 4B Thinking
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Qwen2.5 7B 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 7B Instruct.
Sep 19, 2024
1.7 years ago
Sep 22, 2025
8 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 7B Instruct is available from Together. Qwen3 VL 4B Thinking is available from DeepInfra.
Qwen2.5 7B Instruct
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
Qwen2.5 7B Instruct
View detailsAlibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
| Feature | | |
|---|
FAQ
Common questions about Qwen2.5 7B Instruct vs Qwen3 VL 4B Thinking.