DiffusionGemma 26B-A4B vs Qwen3 VL 8B Thinking
DiffusionGemma 26B-A4B leads the LLM Stats Score 19.4 to 16.3.
Google · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DiffusionGemma 26B-A4B leads the overall LLM Stats Score 19.4 to 16.3, ranking #204 overall.
In the 5 individual benchmarks reported for both models, DiffusionGemma 26B-A4B wins 4; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DiffusionGemma 26B-A4B
- overall performance matters — it scores 19.4 and ranks #204 on LLM Stats
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
- you want the most recent training data — it shipped Jun 2026
Choose Qwen3 VL 8B Thinking
- you want predictable pricing at $0.18/M input and $2.09/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DiffusionGemma 26B-A4B · 50 for Qwen3 VL 8B Thinking
DiffusionGemma 26B-A4B outperforms in 4 benchmarks (GPQA, LiveCodeBench v6, MathVision, MMLU-Pro), while Qwen3 VL 8B Thinking is better at 1 benchmark (MMMU-Pro).
DiffusionGemma 26B-A4B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DiffusionGemma 26B-A4B has 16.2B more parameters than Qwen3 VL 8B Thinking, making it 180.0% larger.
Context Window
Maximum input and output token capacity
Only Qwen3 VL 8B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 8B Thinking specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both DiffusionGemma 26B-A4B and Qwen3 VL 8B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DiffusionGemma 26B-A4B
Qwen3 VL 8B 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
DiffusionGemma 26B-A4B was released on 2026-06-10, while Qwen3 VL 8B Thinking was released on 2025-09-22.
DiffusionGemma 26B-A4B is 9 months newer than Qwen3 VL 8B Thinking.
Jun 10, 2026
3 months ago
8mo newerSep 22, 2025
11 months ago
Knowledge Cutoff
When training data ends
DiffusionGemma 26B-A4B has a documented knowledge cutoff of 2025-01-01, while Qwen3 VL 8B Thinking's cutoff date is not specified.
We can confirm DiffusionGemma 26B-A4B's training data extends to 2025-01-01, but cannot make a direct comparison without Qwen3 VL 8B Thinking's cutoff date.
Jan 2025
—
Outputs Comparison
Judge for yourself.
Run your own prompts against DiffusionGemma 26B-A4B and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DiffusionGemma 26B-A4B vs Qwen3 VL 8B Thinking.