Qwen 3 30B-A3B vs gpt-oss 20B

The two models at a glance

About Qwen 3 30B-A3B

Alibaba's Qwen 3 MoE with 30B total and just 3B active parameters, supporting hybrid thinking mode. MMLU 81.4, AIME24 80.4, 100+ languages, Apache 2.0. Strengths: 3B active parameters keeps inference fast and cheap, MMLU 81.4 and AIME24 80.4 — strong on both general and reasoning, Apache 2.0, Hybrid thinking toggle per request.

About gpt-oss 20B

OpenAI's compact open-weight MoE with 3.6B active out of 21B total parameters. Matches o3-mini on a laptop-class GPU under Apache 2.0. Strengths: Apache 2.0 with full commercial freedom, Around 13 GB VRAM at Q4 — runs on a 16 GB card, OpenAI quality in an accessible footprint, Native 128k context.

How they compare

Qwen 3 30B-A3B comes from Alibaba and gpt-oss 20B from OpenAI, they belong to the Qwen and gpt-oss 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 30B vs 21B parameters, Qwen 3 30B-A3B is the larger of the two. At Q4, gpt-oss 20B fits in about 13 GB of VRAM versus 19 GB for the other — a 6 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Qwen 3 30B-A3B is tuned for chat, general, reasoning, multilingual, moe, while gpt-oss 20B leans toward chat, general, reasoning, moe, small. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, gpt-oss 20B pushes roughly 55 tokens/sec versus 40, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3 30B-A3B offers the bigger window (128k vs 125k tokens).

Memory, quantization & throughput

Across quantization levels, Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB, while gpt-oss 20B requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 23 GB, FP16 ≈ 42 GB. In practice Qwen 3 30B-A3B wants a 24 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 3 30B-A3B needs roughly 32 GB of system RAM to run on CPU and gpt-oss 20B about 18 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 40 tokens/sec from Qwen 3 30B-A3B and 55 from gpt-oss 20B, scaling up to 100 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 3 30B-A3B or gpt-oss 20B to the card you actually own:

• On a 16 GB GPU: Qwen 3 30B-A3B does not fit; gpt-oss 20B runs at Q5 (16 GB).
• On a 24 GB GPU: Qwen 3 30B-A3B runs at Q5 (23 GB); gpt-oss 20B runs at Q8 (23 GB).

Benchmark scores

Reported benchmarks for Qwen 3 30B-A3B: MMLU (base) 81.38, AIME 2024 80.4.

Bottom line: which should you pick?

• Pick Qwen 3 30B-A3B for long-context work (up to 128k tokens).
• Pick gpt-oss 20B for lower VRAM and faster inference; pick Qwen 3 30B-A3B for maximum headline quality.
• Pick Qwen 3 30B-A3B if your workload is multilingual.
• Pick gpt-oss 20B if your workload is small.

Which GPU should you buy to run Qwen 3 30B-A3B?

To run Qwen 3 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).

Frequently asked questions

What is the difference between Qwen 3 30B-A3B and gpt-oss 20B?

The headline differences: Qwen 3 30B-A3B is a 30B model and gpt-oss 20B is 21B; their context windows differ (128k vs 125k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Qwen 3 30B-A3B and gpt-oss 20B run on a 24 GB GPU?

At a Q4 quantization, Qwen 3 30B-A3B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; gpt-oss 20B needs about 13 GB and fits comfortably on a 24 GB GPU. gpt-oss 20B is the lighter option for tight VRAM budgets.

Which is faster, Qwen 3 30B-A3B or gpt-oss 20B?

gpt-oss 20B is the smaller model (21B vs 30B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

What licenses do Qwen 3 30B-A3B and gpt-oss 20B use?

Qwen 3 30B-A3B is licensed under Apache 2.0 and gpt-oss 20B under Apache 2.0.

Which has the longer context window, Qwen 3 30B-A3B or gpt-oss 20B?

Qwen 3 30B-A3B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.