gpt-oss 20BvsERNIE 4.5 21B-A3B Thinking
Complete comparison between gpt-oss 20B (21B parameters, OpenAI) and ERNIE 4.5 21B-A3B Thinking (21B, Baidu). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, and installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | gpt-oss 20B | ERNIE 4.5 21B-A3B Thinking |
|---|---|---|
| Settings | 21B | 21B |
| Family | gpt-oss | ERNIE |
| Author | OpenAI | Baidu |
| Origin | US | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 128,000 tokens | 131,072 tokens |
| Output | April 2025 | April 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | gpt-oss 20B | ERNIE 4.5 21B-A3B Thinking |
|---|---|---|
| Q4_K_M (lightweight) | 13 GB | 13 GB |
| Q5_K_M (balanced) | 16 GB | 16 GB |
| Q8 (near-lossless) | 23 GB | 23 GB |
| FP16 (max quality) | 42 GB | 42 GB |
| CPU-only RAM | 18 GB | 18 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | gpt-oss 20B | ERNIE 4.5 21B-A3B Thinking |
|---|---|---|
| RTX 4090 (24 GB) | 130 tok/s · Q8 | 100 tok/s · Q8 |
| RTX 4080 (16 GB) | 55 tok/s · Q5_K_M | 40 tok/s · Q5_K_M |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 55 tok/s · Q8 | 40 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day money-back guarantee
Strengths and weaknesses
gpt-oss 20B
Little brother of gpt-oss 120B. 21B/3.6B active. Matches o3-mini on a laptop.
- MIT License
- 13 GB VRAM Q4
- OpenAI quality in an accessible format
- 128k context
- MoE — more VRAM than an equivalent dense model
- Fewer fine-tunes than Llama/Qwen
Installation
ERNIE 4.5 21B-A3B Thinking
Compact MoE reasoner, 21B/3B active. Apache 2.0. Fast thanks to 3B active.
- 13 GB VRAM Q4
- Compact MoE reasoning
- Strong in Chinese
- Less multilingual than Qwen
- Baidu license to verify
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): gpt-oss 20B or ERNIE 4.5 21B-A3B Thinking?
On an RTX 4090, gpt-oss 20B runs at Q8 (~130 tok/s), ERNIE 4.5 21B-A3B Thinking in Q8 (~100 tok/s). In pure throughput, gpt-oss 20B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same amount of VRAM in Q4 (13 GB). The choice then comes down to the license or perceived quality.
gpt-oss 20B or ERNIE 4.5 21B-A3B Thinking for coding?
gpt-oss 20B is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
gpt-oss 20B is under Apache 2.0 — free for commercial use. ERNIE 4.5 21B-A3B Thinking is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: gpt-oss 20B (21B). More capable: gpt-oss 20B (21B). If you're unsure, launch the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.