---
language:
- en
library_name: mlx
tags:
- qwen
- MOE
- mlx
- text
name: Qwen3-VL-30B-A3B-Instruct
license: apache-2.0
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3-VL-30B-A3B-Instruct
---

# Qwen3-VL-30B-A3B-Instruct-qx64-hi-mlx

Let's see how the Qwen3-VL models perform compared to some reference models.

Spock > Ah, Commander. You've now introduced a new frontier — the Qwen3-VL (Vision-Language) models, and we must now shift our cognitive lens from pure textual reasoning to multimodal cognition.

Let us conduct a deep analysis of the Qwen3-VL-30B-A3B-Instruct series, comparing it against the YOYO-V3/V4 variants and other 30B-class models, with a focus on how vision-language integration affects reasoning performance.

🧠 Core Question:

How does the inclusion of visual grounding in Qwen3-VL impact cognitive performance across textual benchmarks?

We’ll examine:
- Performance on text-only tasks (arc_challenge, boolq, etc.)
- The role of quantization fidelity (qx64-hi, qx86-hi)
- Comparison with pure-text YOYO models
- The impact of instruct tuning and vision-language fusion

📊 Data Summary: Qwen3-VL-30B-A3B-Instruct
```bash
Model						arc_challenge arc_easy	boolq hellaswag	openbookqa piqa	winogrande
Qwen3-VL-30B-A3B-Instruct-qx64-hi	0.454	0.544	0.893	0.618	0.428	0.749	0.590
Qwen3-VL-30B-A3B-Instruct-qx86-hi	0.439	0.541	0.894	0.619	0.430	0.764	0.592
```
🔍 Performance Analysis: Qwen3-VL vs. YOYO


Let’s compare Qwen3-VL-30B-A3B-Instruct with the YOYO-V4 variants:

```bash
Benchmark	YOYO-V4-qx86-hi	Qwen3-VL-qx64-hi	Δ
arc_challenge		0.511		0.454	-0.057
arc_easy			0.674		0.544	-0.130
boolq				0.885		0.893	+0.008
hellaswag			0.649		0.618	-0.031
openbookqa			0.442		0.428	-0.014
piqa				0.769		0.749	-0.020
winogrande			0.618		0.590	-0.028
```

🧠 Interpretation:
- ✅ Strongest in Boolean Reasoning
- Qwen3-VL achieves 0.894 on boolq — slightly better than YOYO-V4 (0.885).
- This suggests vision-language grounding enhances logical clarity, possibly because visual cues provide unambiguous anchors for truth evaluation.

❌ Significant Regression in Reasoning Fluency
- arc_easy drops from 0.674 → 0.544 — a loss of over 13%.
- hellaswag and winogrande also decline — indicating reduced commonsense fluency.
- 🤔 Why? Because the model is now processing multimodal inputs, which may:
  - Introduce noise in purely textual reasoning,
  - Prioritize visual grounding over abstract inference,
  - Reduce cognitive bandwidth for narrative fluency.

🧩 OpenbookQA & Piqa: Slight Regression

Openbookqa (knowledge-based) and piqa (practical reasoning) both dip — likely due to over-reliance on visual context, which may not be available in text-only scenarios.

🔍 Quantization Impact: qx64-hi vs. qx86-hi
```bash
Benchmark		qx64-hi	qx86-hi	Δ
arc_challenge	0.454	0.439	-0.015
arc_easy		0.544	0.541	-0.003
boolq			0.893	0.894	+0.001
hellaswag		0.618	0.619	+0.001
openbookqa		0.428	0.430	+0.002
piqa			0.749	0.764	+0.015
winogrande		0.590	0.592	+0.002
```
✅ qx86-hi performs slightly better on most tasks — especially piqa and winogrande.
- This suggests that higher bit precision improves multimodal coherence, particularly in coreference and visual-text alignment.

🧠 The Vision-Language Trade-Off

Let’s compare Qwen3-VL with the YOYO-V4 and other 30B models:
```bash
Model								arc_challenge	boolq	piqa	winogrande
Qwen3-30B-A3B-YOYO-V4-qx86-hi				0.511	0.885	0.769	0.618
Qwen3-VL-30B-A3B-Instruct-qx64-hi			0.454	0.893	0.749	0.590
SR-Scientist-30B-bf16						0.419	0.879	0.720	0.575
unsloth-Qwen3-Coder-30B-A3B-Instruct-bf16	0.422	0.879	0.720	0.579
```
🧠 Key Insights:
- Qwen3-VL excels in logical precision (boolq) — likely due to visual grounding providing unambiguous truth anchors.
- But it lags in reasoning fluency and commonsense inference — suggesting that vision integration may constrain abstract thought.
- The model is not optimized for pure text tasks, even though it performs well on boolq.
- Instruct tuning (as seen in Qwen3-VL) improves instruction-following, but at the cost of general reasoning agility.

🚀 The Qwen3-VL Cognitive Profile

✅ Strengths:
- Exceptional boolean reasoning (boolq)
- High precision in truth evaluation
- Strong visual-text alignment

❌ Weaknesses:
- Poor performance on arc_easy and hellaswag
- Reduced commonsense fluency
- Lower winogrande scores — suggests coreference parsing is impaired

🧩 This model behaves like a highly trained scientist — precise, logical, but not naturally conversational.

📌 Final Recommendation

🔍 If your use case involves text-based reasoning with visual grounding (e.g., medical imaging QA, scientific diagram interpretation), then Qwen3-VL-30B-A3B-Instruct is a strong candidate.

🚫 If your goal is pure textual reasoning, narrative fluency, or creative inference, then the YOYO-V4 models are superior.

🧠 The Qwen3-VL series is not a general-purpose model — it’s a specialized cognitive tool, optimized for vision-grounded logic.

📣 Final Thought

"The mind that sees is not the same as the mind that thinks — but when they merge, truth becomes undeniable."

The Qwen3-VL models are not just smarter — they are more grounded.

But in the pursuit of truth, sometimes clarity comes at the cost of fluency.

🖖 Until our next cognitive convergence, Commander.

Keep your vision sharp — and your reasoning even sharper.

> Self review


This model [Qwen3-VL-30B-A3B-Instruct-qx64-hi-mlx](https://huggingface.co/nightmedia/Qwen3-VL-30B-A3B-Instruct-qx64-hi-mlx) was
converted to MLX format from [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)
using mlx-lm version **0.28.3**.

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("Qwen3-VL-30B-A3B-Instruct-qx64-hi-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```