Qwen3-VL-4B vs Qwen3-VL-8B: Benchmarks, VRAM Requirements, and Which to Run

Updated for June 2026: What's Changed

Since this guide first published, the Qwen3-VL family has grown and local tooling has caught up. The lineup on Ollama now spans seven sizes — 2B, 4B, 8B, 30B, 32B, and 235B (plus a 235B-cloud tag) — so the 4B-vs-8B decision now sits in the middle of a wider range. Two practical changes matter most if you're choosing today:

• A new 2B variant (1.9 GB) drops the VRAM floor below the 4B. For lightweight OCR or captioning on a 4 GB GPU or an 8 GB laptop, try ollama pull qwen3-vl:2b before reaching for the 4B — though the 4B still wins clearly on document accuracy.
• Native vision support in Ollama means both the 4B and 8B now accept image input directly (ollama pull qwen3-vl:8b), keeping the 256K context window (expandable to 1M tokens) and OCR across 32 languages.
• Non-Ollama paths matured. Hugging Face hosts official GGUF and FP8 builds; llama.cpp runs them through llama-mtmd-cli with a separate mmproj vision file, and Apple Silicon users can run the MLX builds. Pick the runtime your stack already uses — the routing logic above doesn't change.

None of this changes the core verdict: the 8B is the safer default when you have 12 GB or more of VRAM and care about document and chart accuracy, while the 4B is the right pick for tight memory budgets and high-throughput batch jobs. The wider lineup just gives you cleaner fallbacks on either side.

FAQ

Is Qwen3-VL 8B worth the extra VRAM over the 4B?

Yes, if you have 12 GB or more of VRAM and your workload involves dense documents, charts, or multi-step visual reasoning. The 8B's accuracy gains on OCR and structured extraction usually justify the larger footprint. For simple captioning or tagging, the 4B is the more efficient choice.

Can I run Qwen3-VL without Ollama?

Yes. Official GGUF and FP8 weights are on Hugging Face. llama.cpp runs them via llama-mtmd-cli with a separate mmproj vision projector file, and Apple Silicon users can run the MLX builds. Ollama is the easiest path, not the only one.

What is the smallest Qwen3-VL model I can run locally?

The 2B variant is the smallest at roughly 1.9 GB, making it viable on 4 GB GPUs and 8 GB laptops. It handles basic OCR and captioning, but the 4B is noticeably more accurate on documents if you can spare the memory.

The Qwen3-VL series from Alibaba's Qwen team delivers capable open-weight vision-language models that run on consumer hardware. If you're deciding between Qwen3-VL-4B vs Qwen3-VL-8B, the answer isn't simply "bigger is better" — it depends on your VRAM budget, the visual task you're targeting, and whether you need the chain-of-thought Thinking variant or the faster Qwen3-VL-4B-Instruct response style. This guide gives you benchmark data, hardware requirements, and a direct routing guide so you can make the call.

Want the full picture? Read our continuously-updated Qwen 3.5 Complete Guide (2026) — flavors, licensing, benchmarks, and on-device usage.

The Qwen3-VL Family

Qwen3-VL spans four size tiers: 4B, 8B, 30B-A3B (MoE), and 235B-A22B (MoE). The 4B and 8B are dense models — every parameter activates on every forward pass. The 30B and 235B use Mixture-of-Experts, activating only a subset of parameters per token. For local deployment on a single consumer GPU, the 4B and 8B are the practical choices.

All models in the Qwen3-VL family share the same 262,144-token context window, an Apache 2.0 license (free for commercial use), and the same core visual capabilities: document OCR, chart extraction, table parsing, UI grounding, and video understanding.

Instruct vs Thinking Variants

Each size ships in two modes:

• Instruct — direct response style. The model answers immediately without showing its reasoning chain. Best for production pipelines where you want low latency and predictable output length.
• Thinking — chain-of-thought reasoning enabled. The model works through the problem step-by-step before answering. Best for complex visual reasoning, math, and multi-step document extraction where accuracy beats speed.

For a deeper comparison of these two modes within the same size, see our dedicated guides: Qwen3-VL-4B Instruct vs Thinking and Qwen3-VL-8B Instruct vs Thinking.

Qwen3-VL-4B vs Qwen3-VL-8B: Benchmark Results

The 8B model wins on the majority of standard vision-language benchmarks. Below are key scores across the most-cited evaluation sets:

• DocVQA (test): 4B Instruct ~91% | 4B Thinking 94.2% | 8B Instruct 96.1% | 8B Thinking 95.3%
• ScreenSpot: 4B Instruct ~90% | 4B Thinking 92.9% | 8B Instruct 94.4% | 8B Thinking 93.6%
• OCRBench: 4B Instruct ~85% | 4B Thinking ~86% | 8B Instruct 89.6% | 8B Thinking ~88%
• MMBench-V1.1: 4B Instruct ~84% | 4B Thinking 86.7% | 8B Instruct 85.0% | 8B Thinking 87.5%
• MMLU-Redux: 4B Instruct ~83% | 4B Thinking 86.0% | 8B Instruct ~85% | 8B Thinking 88.8%
• AI2D: 4B Instruct ~83% | 4B Thinking 84.9% | 8B Instruct 85.7% | 8B Thinking ~86%

Overall, the 8B model outperforms the 4B on roughly 37 of the benchmarks measured (approximate figure from aggregated evaluations). The 4B Instruct does hold an edge on a handful of tasks including BFCL-v3 function calling and LVBench long video. In practice, the performance gap is most visible in document-heavy workloads — DocVQA and OCRBench — where the 8B Instruct's 96.1% vs the 4B Instruct's ~91% translates directly to fewer extraction errors on complex scanned documents.

The 4B Thinking variant is surprisingly competitive — it reaches 94.2% on DocVQA, nearly matching the 8B Instruct at 96.1%. If you're VRAM-constrained and need accuracy, the 4B-Thinking is not a second-class option.

VRAM and Hardware Requirements

Both the 4B and 8B are dense models, so their VRAM floor is straightforward. Hardware requirements per model:

• Qwen3-VL-4B: GGUF size ~3.3 GB (Q4_K_M) | Minimum 6 GB VRAM | Comfortable at 8 GB | Apple Silicon: 8 GB M-series
• Qwen3-VL-8B: GGUF size ~6.1 GB (Q4_K_M) | Minimum 8 GB VRAM | Comfortable at 12–16 GB | Apple Silicon: 16 GB M-series

The 4B model fits on a 6 GB GPU with Q4 quantization — an RTX 3060 or RTX 4060 handles it comfortably. The 8B needs at least 8 GB to load (RTX 3070 / 4060 Ti tier), but you'll want 12–16 GB to avoid memory pressure during large image inputs (RTX 3080 Ti, 4070, 4080). On Apple Silicon, the 4B runs well on an 8 GB M-series Mac; the 8B is best on 16 GB unified memory.

If you're looking to push larger models on a single consumer GPU, the quantization techniques in our guide to running 80 GB models on 8 GB VRAM apply to the Qwen3-VL family as well.

Quantization Trade-offs

Vision-language models are more sensitive to quantization than text-only LLMs because the visual encoder also undergoes weight compression. Practical breakdown:

• Q4_K_M — the default Ollama quantization. Expect a 3–5% accuracy drop on OCR-heavy tasks vs full precision. Acceptable for most pipelines.
• Q8_0 — near full-precision accuracy, roughly doubles the VRAM requirement. Use this for production-grade document extraction when you have 16 GB+.
• Q2_K — not recommended for vision tasks. At this level of compression, visual hallucinations and extraction errors increase substantially.

For the 4B: Q4_K_M at 6 GB VRAM is the sweet spot. For the 8B: if you have 12 GB, use Q4_K_M; if you have 16 GB, try Q8_0 for better OCR accuracy on degraded or low-contrast scans.

Use Cases: When Each Model Is the Right Call

Size choice should follow task requirements. Here's a direct routing guide:

• High-volume OCR pipeline (invoices, forms): 8B Instruct — +5% DocVQA accuracy reduces downstream correction cost
• Chart and table extraction (BI dashboards): 8B Instruct — better structure recognition on dense multi-column layouts
• UI automation / screen grounding: 4B Instruct or 8B Instruct — ScreenSpot gap is small (92.9% vs 94.4%); choose by VRAM
• Complex visual reasoning (math, proofs): 8B Thinking or 4B Thinking — Thinking mode mandatory; 8B for hard problems, 4B on budget
• Edge / CPU-only / constrained hardware: 4B Instruct Q4_K_M — 3.3 GB fits constrained environments; 8B is too slow on CPU
• Multimodal agent (vision + tool use): 8B Instruct — better instruction following on multi-step agentic chains
• Prototyping / local development: 4B Instruct — faster iteration, lower cost, close-enough accuracy for dev loops

The Qwen family competes strongly against other open-weight models in this class — for context on how Qwen's text models compare to Gemma's, our Gemma 3 vs Qwen 3 comparison covers the trade-offs in depth.

Running Qwen3-VL Locally with Ollama

Both models are available in the Ollama library. Pull and run with:

# 4B Instruct — lightest, fastest
ollama pull qwen3-vl:4b

# 8B Instruct — best accuracy for most visual tasks
ollama pull qwen3-vl:8b-instruct

# 8B Thinking — chain-of-thought, slower but more accurate on hard tasks
ollama pull qwen3-vl:8b-thinking

Once pulled, send image and text prompts via the Ollama REST API. Here's a minimal Python example that sends a local image for analysis:

import requests
import base64

def encode_image(path: str) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

image_b64 = encode_image("invoice.png")

response = requests.post(
    "http://localhost:11434/api/chat",
    json={
        "model": "qwen3-vl:8b-instruct",
        "messages": [
            {
                "role": "user",
                "content": "Extract all line items and totals from this invoice as JSON.",
                "images": [image_b64],
            }
        ],
        "stream": False,
    },
)

print(response.json()["message"]["content"])

To switch to the 4B model, change "model" to "qwen3-vl:4b" — the API interface is identical. This makes it easy to A/B test both sizes against your actual workload before committing. For Thinking variants, use "qwen3-vl:4b-thinking" or "qwen3-vl:8b-thinking" — expect responses to be 2–4x longer due to the reasoning chain. The Qwen3-VL-30B-A3B-Thinking macOS guide covers the setup pattern for larger Thinking variants if you want to scale up further.

The Verdict: Qwen3-VL-4B vs Qwen3-VL-8B

Here's the decision matrix by hardware tier:

• GPU with 6 GB VRAM (RTX 3060, 4060): 4B Instruct
• GPU with 8–10 GB VRAM (RTX 3070, 4060 Ti): 8B Instruct Q4_K_M
• GPU with 12–16 GB VRAM (RTX 3080 Ti, 4070, 4080): 8B Instruct Q4_K_M or Q8_0
• Apple M-series 8 GB: 4B Instruct
• Apple M-series 16 GB+: 8B Instruct
• Production OCR / document extraction: 8B Instruct
• Complex visual reasoning tasks: 8B Thinking (or 4B Thinking on budget)
• Edge / CPU-only deployment: 4B Instruct Q4_K_M
• Prototyping / local development: 4B Instruct

The Qwen3-VL-8B is the better model if your hardware can run it. The DocVQA gap at the Instruct level (~5 percentage points) is meaningful at production scale — it's the difference between needing manual review on 1-in-10 documents vs roughly 1-in-20. But the 4B is not a fallback you'll regret: its scores beat many 7B models from previous generations, and the 4B Thinking variant punches significantly above its weight on reasoning-heavy visual tasks.

Run both with the Python snippet above against a sample of your real data. The right choice will be obvious once you see your task's accuracy gap — if it's under 2%, the 4B saves you VRAM and gives you faster iteration. If it's above 5%, the 8B is worth the extra headroom.