Qwen 2.5 VL 7B vs LLaDA 2.0 Uni 16B

The two models at a glance

About Qwen 2.5 VL 7B

A 7B vision-language model from Alibaba with state-of-the-art results in its class, scoring 95.7 on DocVQA. Handles hour-long video, bounding-box grounding, and multilingual OCR. Strengths: State-of-the-art vision performance at the 7B tier, Excellent multilingual OCR, Long video input (over 1 hour), Apache 2.0.

About LLaDA 2.0 Uni 16B

Ant Group's first open Apache 2.0 diffusion LLM: a 16B/1B MoE paired with a 6.2B diffusion decoder, unifying text and vision generation and editing. Released April 2026. Strengths: The first Apache 2.0 open diffusion LLM, Unified text, vision, generation, and editing, Interleaved 'thinking' mode during diffusion, Decoder-turbo distillation runs 8 diffusion steps instead of 50.

How they compare

Qwen 2.5 VL 7B comes from Alibaba and LLaDA 2.0 Uni 16B from Ant Group / inclusionAI, they belong to the Qwen and LLaDA families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

At 7B vs 16B parameters, LLaDA 2.0 Uni 16B is the larger of the two. At Q4, Qwen 2.5 VL 7B fits in about 6 GB of VRAM versus 18 GB for the other — a 12 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Qwen 2.5 VL 7B is tuned for vision, chat, general, while LLaDA 2.0 Uni 16B leans toward chat, vision, general, moe. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, LLaDA 2.0 Uni 16B pushes roughly 60 tokens/sec versus 25, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 2.5 VL 7B offers the bigger window (125k vs 8k tokens).

Memory, quantization & throughput

Across quantization levels, Qwen 2.5 VL 7B requires Q4 ≈ 6 GB, Q5 ≈ 7 GB, Q8 ≈ 10 GB, FP16 ≈ 18 GB, while LLaDA 2.0 Uni 16B requires Q4 ≈ 18 GB, Q5 ≈ 22 GB, Q8 ≈ 30 GB, FP16 ≈ 47 GB. In practice Qwen 2.5 VL 7B fits an 8 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Qwen 2.5 VL 7B needs roughly 10 GB of system RAM to run on CPU and LLaDA 2.0 Uni 16B about 36 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 25 tokens/sec from Qwen 2.5 VL 7B and 60 from LLaDA 2.0 Uni 16B, scaling up to 60 and 130 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Qwen 2.5 VL 7B or LLaDA 2.0 Uni 16B to the card you actually own:

• On a 8 GB GPU: Qwen 2.5 VL 7B runs at Q5 (7 GB); LLaDA 2.0 Uni 16B does not fit.
• On a 12 GB GPU: Qwen 2.5 VL 7B runs at Q8 (10 GB); LLaDA 2.0 Uni 16B does not fit.
• On a 16 GB GPU: Qwen 2.5 VL 7B runs at Q8 (10 GB); LLaDA 2.0 Uni 16B does not fit.
• On a 24 GB GPU: Qwen 2.5 VL 7B runs at FP16 (18 GB); LLaDA 2.0 Uni 16B runs at Q5 (22 GB).

Benchmark scores

Reported benchmarks for Qwen 2.5 VL 7B: DocVQA 95.7, ChartQA 87.3, OCRBench 86.4.

Bottom line: which should you pick?

• Pick Qwen 2.5 VL 7B for long-context work (up to 125k tokens).
• Pick Qwen 2.5 VL 7B for lower VRAM and faster inference; pick LLaDA 2.0 Uni 16B for maximum headline quality.
• Pick LLaDA 2.0 Uni 16B if your workload is moe.

Which GPU should you buy to run LLaDA 2.0 Uni 16B?

To run LLaDA 2.0 Uni 16B locally at Q4, you need ~18 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).

Frequently asked questions

What is the difference between Qwen 2.5 VL 7B and LLaDA 2.0 Uni 16B?

The headline differences: Qwen 2.5 VL 7B is a 7B model and LLaDA 2.0 Uni 16B is 16B; their context windows differ (125k vs 8k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Qwen 2.5 VL 7B and LLaDA 2.0 Uni 16B run on a 24 GB GPU?

At a Q4 quantization, Qwen 2.5 VL 7B needs about 6 GB of VRAM and fits comfortably on a 24 GB GPU; LLaDA 2.0 Uni 16B needs about 18 GB and fits comfortably on a 24 GB GPU. Qwen 2.5 VL 7B is the lighter option for tight VRAM budgets.

Which is faster, Qwen 2.5 VL 7B or LLaDA 2.0 Uni 16B?

Qwen 2.5 VL 7B is the smaller model (7B vs 16B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

What licenses do Qwen 2.5 VL 7B and LLaDA 2.0 Uni 16B use?

Qwen 2.5 VL 7B is licensed under Apache 2.0 and LLaDA 2.0 Uni 16B under Apache 2.0.

Which has the longer context window, Qwen 2.5 VL 7B or LLaDA 2.0 Uni 16B?

Qwen 2.5 VL 7B has the larger context window (125k vs 8k tokens), so it handles longer documents and codebases in a single prompt.