Guide · 2026-06-16

Llama 3.2 Vision 11B vs Qwen 2.5 VL 7B — Multimodal Compared

Last updated 2026-06-16

Two open-weight vision-language models, two very different philosophies. We benchmark OCR, document Q&A, VRAM, and throughput to pick a winner.

By Mohamed Meguedmi · 11 min read

The open-weight vision-language model (VLM) space matured fast in 2025. By mid-2026, two models still dominate the “runs on a single consumer GPU” bracket: Meta’s Llama 3.2 Vision 11B and Alibaba’s Qwen 2.5 VL 7B. They target similar hardware (8–16 GB VRAM) but represent opposite design choices.

This guide settles the question developers keep asking us: which one should you actually deploy? We rebuilt the comparison with quantized GGUFs on identical hardware, ran the standard VLM benchmarks, and pressure-tested both on real document and screenshot workloads. The short answer is at the top. The long answer — with numbers — is below.

Architectures: cross-attention vs native multimodal

The two models do not solve multimodality the same way, and that single decision drives almost every downstream tradeoff.

Llama 3.2 Vision 11B is a Llama 3.1 8B text decoder welded to a vision adapter via cross-attention layers. Meta froze the text weights and added ~3B parameters of new vision-language adapter layers. The image encoder is a ViT that produces visual tokens which the cross-attention layers query during decoding. The text backbone is unchanged from Llama 3.1 8B Instruct — same tokenizer, same reasoning, same English fluency. Read the architecture details on the official HuggingFace model card.

Qwen 2.5 VL 7B takes the opposite route. It is a Qwen 2.5 7B decoder trained jointly with a redesigned ViT encoder featuring dynamic resolution and window attention. Visual tokens enter the decoder context directly, with no cross-attention shortcut. The model handles arbitrary aspect ratios up to 12.8 megapixels of effective input and was trained with native bounding-box and OCR supervision. Specs are documented on the Qwen 2.5 VL 7B HuggingFace card and the Qwen 2.5 VL technical report.

Why this matters in practice: cross-attention freezes the text model, which preserves Llama’s English chat quality but bottlenecks how much visual information reaches the decoder. Qwen’s in-context approach lets the model reason fluidly across text and image tokens — which is exactly what OCR, table parsing, and chart reading need.

Benchmark scoreboard

We compiled scores from the official model cards, the Qwen 2.5 VL paper, and our own re-runs on the 4-bit GGUFs at full FP16 vision encoder. Higher is better in all columns.

Benchmark	What it tests	Llama 3.2 Vision 11B	Qwen 2.5 VL 7B	Delta
MMMU (val)	College-level multimodal reasoning	50.7	58.6	Qwen +7.9
DocVQA (test)	Document question answering	88.4	95.7	Qwen +7.3
ChartQA (test)	Chart reading and reasoning	83.4	87.3	Qwen +3.9
OCRBench	Mixed OCR tasks	753	864	Qwen +111
InfoVQA	Infographic VQA	63.5	82.6	Qwen +19.1
MathVista	Visual math reasoning	51.5	68.2	Qwen +16.7
TextVQA	Reading text in natural scenes	73.1	84.9	Qwen +11.8
MMBench-EN	General multimodal QA	65.8	83.5	Qwen +17.7

Qwen 2.5 VL 7B sweeps every public benchmark we tracked. The gap is largest where visual detail matters — OCR, infographics, math — and narrowest on free-form reasoning. This matches what we observe in production: Llama’s frozen text core keeps it competitive on linguistic tasks, but it cannot match Qwen’s visual acuity.

Note: the artificialanalysis.ai composite places Qwen 2.5 VL 7B at intelligence index 64 versus 50 for Llama 3.2 11B Vision — a 28% margin.

Hardware requirements and VRAM

Both models target single-GPU deployment, but their memory profiles differ in ways that matter when you are choosing between an 8 GB and a 12 GB card.

Configuration	Llama 3.2 Vision 11B	Qwen 2.5 VL 7B
FP16 weights	22 GB	16.5 GB
Q8_0 GGUF	~12 GB	~9 GB
Q4_K_M GGUF	~7.3 GB + vision encoder ~2.5 GB	~4.7 GB + vision encoder ~1.8 GB
Minimum practical VRAM	12 GB (RTX 3060 12GB, 4070)	8 GB (RTX 3060 Ti, 4060)
Comfortable VRAM	16 GB	10–12 GB
Max image resolution	1120 × 1120 (4 tiles)	up to 12.8 MP, dynamic aspect
Context window	128K tokens	128K (32K vision tokens)

Three details that bite people in practice:

• Llama 3.2 Vision tiles every image into four 560×560 crops plus a global thumbnail — the visual token count is fixed regardless of input size. Tall documents and wide infographics get squashed.
• Qwen 2.5 VL preserves aspect ratio and scales visual tokens with input area. A 1080p screenshot consumes meaningfully more tokens than a 480p photo, which matters for context budgeting.
• The Llama vision encoder cannot be offloaded to CPU cleanly in llama.cpp as of mid-2026. Plan VRAM accordingly.

For a per-card breakdown, our cost calculator models electricity and amortization for both setups.

Throughput and latency

We measured both models on an RTX 4090 24 GB at Q4_K_M, llama.cpp build b4520, single concurrent request, 512-token output, one 1024×1024 image input.

Metric	Llama 3.2 Vision 11B	Qwen 2.5 VL 7B
Prefill (image encode + prompt)	410 ms	270 ms
Time to first token	520 ms	340 ms
Decode throughput	72 tok/s	96 tok/s
End-to-end 512 tok response	7.6 s	5.7 s
Power draw during decode	~280 W	~245 W

Qwen is roughly 33% faster end-to-end. The decode speed difference tracks the parameter count gap; the prefill difference is bigger because Llama’s cross-attention adds work on every layer the vision tokens pass through.

Where each model actually shines

Tasks where Qwen 2.5 VL 7B wins decisively

• OCR of dense documents: invoices, receipts, contracts, multi-column PDFs. Qwen reads tiny fonts that Llama hallucinates.
• Chart and graph reading: extracting series, axis labels, legends, and trend descriptions.
• UI screenshots and agent workflows: Qwen was trained with explicit GUI grounding data and outputs accurate bounding boxes for buttons and form fields.
• Table extraction: round-trip a screenshot of a table to Markdown or JSON. Qwen preserves structure; Llama drops columns.
• Non-English text in images: Qwen handles Chinese, Japanese, Korean, Arabic, and Cyrillic far better.
• Object localization: Qwen outputs usable XYXY coordinates; Llama refuses or guesses.

Tasks where Llama 3.2 Vision 11B holds its own

• Long-form English captioning: alt text, accessibility descriptions, narrative scene description. The Llama 3.1 8B backbone produces more idiomatic English prose.
• Image + text chat where text dominates: if 90% of your tokens are conversation and 10% is “here is a screenshot,” Llama’s text quality matters more than its visual ceiling.
• Safety-tuned outputs: Meta’s RLHF is stricter, which some enterprise workflows prefer.
• Existing Llama tooling: if you already deploy Llama 3.1 8B text-only, the 11B Vision variant slots into the same templates and tokenizer.

Quantization quality cliff

VLMs degrade more aggressively under quantization than text-only models, because the vision-to-text projection layer is sensitive to rounding error. We tested both models at Q8_0, Q5_K_M, Q4_K_M, and Q3_K_M against the FP16 reference on a 200-image internal OCR set.

Quant	Llama 3.2 Vision 11B (OCR F1)	Qwen 2.5 VL 7B (OCR F1)
FP16	0.81	0.93
Q8_0	0.80 (-1%)	0.92 (-1%)
Q5_K_M	0.77 (-5%)	0.91 (-2%)
Q4_K_M	0.71 (-12%)	0.88 (-5%)
Q3_K_M	0.58 (-28%)	0.79 (-15%)

Two observations. First, Qwen 2.5 VL is meaningfully more robust to quantization — you can drop to Q4_K_M and still beat Llama’s FP16 score. Second, the vision projection in Llama 3.2 should be kept at FP16 even when the language layers are quantized; the GGUF defaults already do this, so do not override unless you know what you are breaking.

Editorial note: scores in this section come from our internal eval harness. The full methodology, including image set composition and grading rubric, is documented on the methodology page. Raw scores are available via the BestLLMfor public API (CC BY 4.0) and our open-source MCP server.

How to run each model locally

Both models work with llama.cpp, Ollama, and vLLM. Ollama is the lowest-friction path.

Qwen 2.5 VL 7B with Ollama

ollama pull qwen2.5vl:7b
ollama run qwen2.5vl:7b "Describe this chart" --image chart.png

Confirm the tag on the official Ollama page for Qwen 2.5 VL. The default pull is Q4_K_M and runs comfortably in 8 GB VRAM.

Llama 3.2 Vision 11B with Ollama

ollama pull llama3.2-vision:11b
ollama run llama3.2-vision:11b "What is in this image?" --image photo.jpg

The Llama vision tag is documented on the Llama 3.2 Vision Ollama page. Allocate 12 GB VRAM as the comfortable minimum.

vLLM for production serving

Both ship with vLLM support. Qwen 2.5 VL has better batched throughput because of its uniform attention pattern; Llama 3.2 Vision’s cross-attention creates uneven memory pressure across the batch. For multi-tenant API serving, Qwen scales better. Browse our full catalog for sibling VLMs (Pixtral, MiniCPM-V, InternVL) that may also be worth a look.

Verdict

Use case	Recommendation
Document Q&A, OCR, invoices, contracts	Qwen 2.5 VL 7B
Charts, graphs, infographics	Qwen 2.5 VL 7B
UI screenshots, agent loops, bounding boxes	Qwen 2.5 VL 7B
Non-English text in images	Qwen 2.5 VL 7B
Long-form English alt text and captions	Llama 3.2 Vision 11B
Drop-in upgrade for existing Llama text stack	Llama 3.2 Vision 11B
8 GB VRAM budget	Qwen 2.5 VL 7B (only option)
16+ GB VRAM, English-only chat with images	Tie — test both on your workload

For most readers building anything that processes documents, charts, or screenshots, Qwen 2.5 VL 7B is the correct default. It is smaller, faster, more accurate, more multilingual, and more robust under quantization. Llama 3.2 Vision 11B remains a reasonable pick only when you specifically value the Llama text persona or already have Llama-3-shaped infrastructure to slot it into.

If you want to compare cost of ownership across hardware tiers, our cost calculator handles both models. For methodology details and the public benchmark API, see the about page.