ERNIE 4.5 21B Benchmark & Review

Quick answer

ERNIE-4.5-21B-A3B-Thinking is a text-based Mixture-of-Experts (MoE) model from Baidu. It has 21 billion total parameters but activates about 3 billion per token. That makes it a strong, cost-aware choice for complex reasoning, math, and long-context tasks.

Read the model page: ERNIE-4.5-21B-A3B-Thinking on Hugging Face and the project announcement: Announcing ERNIE 4.5.

What is ERNIE 4.5 21B A3B?

ERNIE 4.5 21B A3B is part of Baidu s ERNIE 4.5 family. It mixes many expert subnetworks and routes tokens to a few experts per request. That is the Mixture-of-Experts idea. The model supports very long context windows and multi-modal capabilities when needed.

Key specs

Feature	Value
Params (Total / Activated)	21B / ~3B
Layers	28
Text Experts (Total / Activated)	64 / 6
Context Length	131,072 tokens

Source specs: Hugging Face model page and AI Studio listing.

Why this model matters

Parameter efficiency: It gives near state-of-the-art reasoning while using fewer active parameters per token.
Cost control: MoE routing reduces compute per request versus dense models of similar total size.
Thinking variant: The "Thinking" post-trained build targets multi-step reasoning tasks.
Open ecosystem: Available on Hugging Face and supported by Baidu s toolset like ERNIEKit and FastDeploy.

Benchmarks and what to expect

Baidu reports strong results on reasoning and knowledge tasks. Public references note competitive scores against larger models like Qwen3-30B. See the project page on GitHub for the team s benchmark summaries.

High-level benchmark notes

Strong on math and reasoning suites (examples: CMATH, BBH).
Competitive on instruction-following and QA tasks.
Smaller active compute per token than dense 30B+ models, which lowers inference cost.

Note: independent, reproducible benchmarks are still scarce. Use the model pages for official numbers: ERNIE on Hugging Face (PT) and the official repo.

How we recommend testing (method)

To compare ERNIE 4.5 21B A3B to other models, run the same prompts and measure accuracy, latency, and cost. Key steps:

Pick 3-4 benchmarks: BBH, CMATH, a QA set, and a code-generation sample.
Use the same tokenizer and prompt templates for each model.
Measure token-level latency and GPU memory per batch.
Log outputs and compute simple accuracy or pass/fail where appropriate.

Quick reproducible test (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
# Load tokenizer and model (adjust device_map / dtype for your setup)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
# Example prompt
prompt = "Solve: 12 * 17 ="
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This example is adapted from the Hugging Face code snippets on the model page: model page.

How to fine tune ERNIE 4.5 (brief)

Options include full SFT, LoRA, or DPO. Baidu provides ERNIEKit (training toolkit) and examples on AI Studio and the GitHub repo.

Get data in instruction-response format.
Start with LoRA if you want cheap compute.
Use ERNIEKit for alignment training; see the AI Studio entry for scripts.

Deploying for inference

Common deployment paths:

Use FastDeploy or a runtime optimized for MoE routing.
Run on multi-GPU hosts or use quantized builds for single-GPU inference.
Cloud VMs like DigitalOcean can host the model for prototypes; see DigitalOcean s writeup.

Hardware tips

For latency-sensitive workloads, use GPUs with plenty of memory (A100 / H100 class) or shard across multiple GPUs.
Quantize to int8/4 when possible to lower memory and cost.
The 131K context length is supported but raises memory needs—use only when you need very long context.

When to use ERNIE 4.5 21B A3B

You need strong math or multi-step reasoning but want lower per-request cost.
You plan to fine-tune or align with ERNIEKit on specific enterprise tasks.
You need a long context window for documents or code.

When to avoid

Creative fiction where narrative coherence matters long-term—some users report weaker fiction coherence compared to other models (see community notes on Reddit).

When a dense model control path is required and your infra cannot handle MoE routing.

Cost and comparison notes

Baidu claims ERNIE-4.5-21B-A3B-Base outperforms some 30B-class models on math and reasoning while being roughly 30% smaller. That suggests a favorable cost-per-query tradeoff, but your real cost depends on chosen runtime, batch size, and quantization.

For an official comparison and details, see the PaddlePaddle/ERNIE GitHub and the open release announcement at Baidu s blog.

Quick start checklist (30-minute prototype)

Download model from Hugging Face.
Spin up a VM with a GPU or use a cloud inference provider (see OpenRouter listing for providers).
Run the Python example above and test a few prompts.
Measure latency and costs, then tune batch size or quantization.

Limitations & community feedback

Community testers praise reasoning gains but note mixed results for creative tasks. See user threads on Reddit for early impressions.

Verdict

ERNIE 4.5 21B A3B is a pragmatic pick when you want strong reasoning at controlled cost. It is especially useful for math, structured QA, and long-context tasks. If you need fiction with high narrative coherence, test outputs head-to-head before committing.

Download benchmark scripts and starter configs from the official repo: PaddlePaddle/ERNIE on GitHub. For model hosting and examples, see the Hugging Face Thinking page.