ERNIE 4.5 21B-A3B Thinking vs Trinity Mini 26B-A3B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | ERNIE 4.5 21B-A3B Thinking | Trinity Mini 26B-A3B |
|---|---|---|
| Parameters | 21B | 26B |
| Author | Baidu | Arcee AI |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 13 GB | 15 GB |
| VRAM at Q5 | 16 GB | 18 GB |
| VRAM at Q8 | 23 GB | 28 GB |
| VRAM at FP16 | 42 GB | 52 GB |
| Use cases | reasoning, moe | chat, general, 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 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.
About Trinity Mini 26B-A3B
Arcee AI's US-built MoE with 3B active parameters out of 26B total. Apache-licensed, fast in practice, and tuned for agent-style workloads. Strengths: Efficient MoE with around 3.5B active parameters, 131k context window, Tuned for agent and tool-use workflows, Apache 2.0.
How they compare
ERNIE 4.5 21B-A3B Thinking comes from Baidu and Trinity Mini 26B-A3B from Arcee AI, they belong to the ERNIE and Trinity 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 21B vs 26B parameters, Trinity Mini 26B-A3B is the larger of the two. At Q4, ERNIE 4.5 21B-A3B Thinking fits in about 13 GB of VRAM versus 15 GB for the other — a 2 GB difference that matters on consumer GPUs.
The two models target different sweet spots: ERNIE 4.5 21B-A3B Thinking is tuned for reasoning, moe, while Trinity Mini 26B-A3B leans toward chat, general, moe. Match the model to your dominant workload rather than to raw size.
The smaller model, ERNIE 4.5 21B-A3B Thinking, will generally generate tokens faster on the same hardware.
Memory, quantization & throughput
Across quantization levels, ERNIE 4.5 21B-A3B Thinking requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 23 GB, FP16 ≈ 42 GB, while Trinity Mini 26B-A3B requires Q4 ≈ 15 GB, Q5 ≈ 18 GB, Q8 ≈ 28 GB, FP16 ≈ 52 GB. In practice ERNIE 4.5 21B-A3B Thinking 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, ERNIE 4.5 21B-A3B Thinking needs roughly 18 GB of system RAM to run on CPU and Trinity Mini 26B-A3B about 24 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 ERNIE 4.5 21B-A3B Thinking and 40 from Trinity Mini 26B-A3B, scaling up to 100 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 ERNIE 4.5 21B-A3B Thinking or Trinity Mini 26B-A3B to the card you actually own:
- On a 16 GB GPU: ERNIE 4.5 21B-A3B Thinking runs at Q5 (16 GB); Trinity Mini 26B-A3B runs at Q4 (15 GB).
- On a 24 GB GPU: ERNIE 4.5 21B-A3B Thinking runs at Q8 (23 GB); Trinity Mini 26B-A3B runs at Q5 (18 GB).
Bottom line: which should you pick?
- Pick ERNIE 4.5 21B-A3B Thinking for lower VRAM and faster inference; pick Trinity Mini 26B-A3B for maximum headline quality.
- Pick ERNIE 4.5 21B-A3B Thinking if your workload is reasoning.
- Pick Trinity Mini 26B-A3B if your workload is chat, general.
Which GPU should you buy to run Trinity Mini 26B-A3B?
To run Trinity Mini 26B-A3B locally at Q4, you need ~15 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 ERNIE 4.5 21B-A3B Thinking and Trinity Mini 26B-A3B?
The headline differences: ERNIE 4.5 21B-A3B Thinking is a 21B model and Trinity Mini 26B-A3B is 26B. Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can ERNIE 4.5 21B-A3B Thinking and Trinity Mini 26B-A3B run on a 24 GB GPU?
At a Q4 quantization, ERNIE 4.5 21B-A3B Thinking needs about 13 GB of VRAM and fits comfortably on a 24 GB GPU; Trinity Mini 26B-A3B needs about 15 GB and fits comfortably on a 24 GB GPU. ERNIE 4.5 21B-A3B Thinking is the lighter option for tight VRAM budgets.
Which is faster, ERNIE 4.5 21B-A3B Thinking or Trinity Mini 26B-A3B?
ERNIE 4.5 21B-A3B Thinking is the smaller model (21B vs 26B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do ERNIE 4.5 21B-A3B Thinking and Trinity Mini 26B-A3B use?
ERNIE 4.5 21B-A3B Thinking is licensed under Apache 2.0 and Trinity Mini 26B-A3B under Apache 2.0.