gpt-oss 20B vs ERNIE 4.5 21B-A3B Thinking
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | gpt-oss 20B | ERNIE 4.5 21B-A3B Thinking |
|---|---|---|
| Parameters | 21B | 21B |
| Author | OpenAI | Baidu |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 13 GB | 13 GB |
| VRAM at Q5 | 16 GB | 16 GB |
| VRAM at Q8 | 23 GB | 23 GB |
| VRAM at FP16 | 42 GB | 42 GB |
| Use cases | chat, general, reasoning, moe, small | reasoning, moe |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
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.
About ERNIE 4.5 21B-A3B Thinking
Baidu's compact reasoning MoE with 3B active parameters out of 21B total. Fast inference thanks to the small active set, with Chinese-language strength. Strengths: Around 13 GB VRAM at Q4, Compact MoE optimized for reasoning, Strong Chinese-language performance, 128k context window.
How they compare
gpt-oss 20B comes from OpenAI and ERNIE 4.5 21B-A3B Thinking from Baidu, they belong to the gpt-oss and ERNIE 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.
gpt-oss 20B and ERNIE 4.5 21B-A3B Thinking share the same 21B parameter class. Both need about 13 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
The two models target different sweet spots: gpt-oss 20B is tuned for chat, general, reasoning, moe, small, while ERNIE 4.5 21B-A3B Thinking leans toward reasoning, moe. 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, ERNIE 4.5 21B-A3B Thinking offers the bigger window (128k vs 125k tokens).
Memory, quantization & throughput
Across quantization levels, gpt-oss 20B requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 23 GB, FP16 ≈ 42 GB, while ERNIE 4.5 21B-A3B Thinking requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 23 GB, FP16 ≈ 42 GB. In practice gpt-oss 20B needs a 16 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, gpt-oss 20B needs roughly 18 GB of system RAM to run on CPU and ERNIE 4.5 21B-A3B Thinking 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 55 tokens/sec from gpt-oss 20B and 40 from ERNIE 4.5 21B-A3B Thinking, scaling up to 130 and 100 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 gpt-oss 20B or ERNIE 4.5 21B-A3B Thinking to the card you actually own:
- On a 16 GB GPU: gpt-oss 20B runs at Q5 (16 GB); ERNIE 4.5 21B-A3B Thinking runs at Q5 (16 GB).
- On a 24 GB GPU: gpt-oss 20B runs at Q8 (23 GB); ERNIE 4.5 21B-A3B Thinking runs at Q8 (23 GB).
Bottom line: which should you pick?
- Pick ERNIE 4.5 21B-A3B Thinking for long-context work (up to 128k tokens).
- Pick gpt-oss 20B if your workload is chat, general, small.
Which GPU should you buy to run gpt-oss 20B?
To run gpt-oss 20B locally at Q4, you need ~13 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Frequently asked questions
What is the difference between gpt-oss 20B and ERNIE 4.5 21B-A3B Thinking?
The headline differences: both are 21B models; their context windows differ (125k vs 128k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can gpt-oss 20B and ERNIE 4.5 21B-A3B Thinking run on a 24 GB GPU?
At a Q4 quantization, gpt-oss 20B needs about 13 GB of VRAM and fits comfortably on a 24 GB GPU; ERNIE 4.5 21B-A3B Thinking needs about 13 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
What licenses do gpt-oss 20B and ERNIE 4.5 21B-A3B Thinking use?
gpt-oss 20B is licensed under Apache 2.0 and ERNIE 4.5 21B-A3B Thinking under Apache 2.0.
Which has the longer context window, gpt-oss 20B or ERNIE 4.5 21B-A3B Thinking?
ERNIE 4.5 21B-A3B Thinking has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.