Baidu ERNIE 4.0 Launch: Catching Up or Overtaking for Chinese LLMs?

Baidu ERNIE 4.0 officially launched on April 5th.

The official announcement was quite ambitious: “Benchmarking against GPT-4.5, multiple capabilities reaching international leading levels.” Honestly, I’ve seen this “benchmarking” narrative too many times. Every time a domestic LLM launches, it has to compare itself to GPT. But in actual usage, the gap is often noticeable.

This time, I decided to test it seriously.

First, the most obvious change: inference speed. ERNIE 4.0 officially claims “40% reduction in inference latency.” In my testing, generating a 500-word response took about 2-3 seconds, noticeably faster than ERNIE 3.5’s 4-5 seconds. Compared to GPT-4.5’s 1-2 seconds, the gap is no longer significant. This speed improvement matters a lot for real-time conversation scenarios.

Second is multimodal capability. ERNIE 4.0 emphasizes “image-text understanding,” with official examples showing it can comprehend complex charts, flow diagrams, and technical drawings. I tested several product prototypes and data analysis charts — ERNIE 4.0 accurately recognized text and structures in images and answered questions based on image content. But honestly, GPT-4V had this capability long ago. ERNIE 4.0 is just catching up, not breaking new ground.

The third upgrade is long context. ERNIE 4.0 supports a 128K context window, processing approximately 100,000 words at once. I fed it a 30,000-word technical document and asked, “What are the core arguments of this article?” It summarized accurately. This capability is quite useful for document analysis and code understanding scenarios.

But here’s the problem: GPT-4.5 and Claude 4.5 basically have all these capabilities, and possibly do them better.

I ran a simple comparison test: gave ERNIE 4.0, GPT-4.5, and Claude 4.5 the same 10 questions, covering code generation, logical reasoning, creative writing, and knowledge Q&A.

Results:

In code generation, the gap was smallest among the three. I asked them to write a Python script “scraping data from web pages and storing it in a database.” ERNIE 4.0’s code ran but had rough error handling. GPT-4.5 and Claude 4.5’s code was more standardized, proactively adding comments and unit tests.

In logical reasoning, the gap became apparent. I gave a variant of the classic “wolf, goat, and cabbage crossing the river” problem. ERNIE 4.0 made a logical leap during reasoning, resulting in a wrong final answer. GPT-4.5 and Claude 4.5 both gave correct answers with clearer reasoning processes.

In creative writing, ERNIE 4.0’s style leaned “official,” like writing government documents. When I asked it to write a sci-fi novel opening, it gave me something resembling a press release. GPT-4.5 showed significantly more creativity, while Claude 4.5 had the best text quality.

In knowledge Q&A, ERNIE 4.0 performed well in Chinese contexts, especially regarding Chinese history, culture, and geography, with high accuracy. But on international news, tech frontiers, and niche domain knowledge, it occasionally had “outdated information” issues. GPT-4.5 and Claude 4.5 had more timely knowledge updates.

So overall, ERNIE 4.0 is indeed “catching up,” and the gap has narrowed. But to claim it’s “benchmarking” or even “overtaking”? I don’t think we’re there yet.

However, my personal feeling is that the “benchmarking” mindset itself might be problematic.

Domestic LLMs have been chasing GPT’s standards — GPT releases a capability, we need it too; GPT upgrades, we benchmark. This “follower strategy” has clear benefits: well-defined goals and paths. But the downside is you’re always chasing, always proving “we can do it too.”

But in AI, what’s more important may be “finding your own differentiated scenarios.”

For example, ERNIE 4.0 indeed has advantages over GPT in Chinese contexts, Chinese cultural understanding, and domestic enterprise application scenarios. If your users are primarily in China, if your data can’t leave the country, if your business scenarios require deep understanding of Chinese semantics, then ERNIE 4.0 might be more suitable than GPT.

I’ve met many enterprise clients in Shenzhen. Their criteria for choosing LLMs isn’t “who’s the best” but “who’s most suitable for my business.” Some chose ERNIE not because it’s better than GPT, but because ERNIE is easier to deploy domestically, has better Chinese support, and more responsive after-sales service.

This “scenario fit” approach may be more practical than “benchmarking GPT.”

Of course, this doesn’t mean domestic LLMs can “lie flat.” The technical gap objectively exists — reasoning capability, generalization ability, multimodal understanding — these hard indicators still need to be pursued. But while catching up, what should be considered more is: where exactly is the “moat” for domestic LLMs?

Is it data? Compute? Application scenarios? Or ecosystem?

GPT’s moat is already clear: first-mover advantage + global developer ecosystem + sustained technical leadership. For domestic LLMs to truly “overtake,” they can’t just rely on “benchmarking” — they need to find what GPT can’t do or can’t do well, and excel there.

This “differentiated competition” approach may be the way out for domestic LLMs.

So back to the initial question: Do you think the gap between domestic LLMs and top international models is “narrowing” or “still widening”?

My answer is: The gap is indeed narrowing, but “narrowing the gap” doesn’t equal “establishing advantage.” Catching up is just the first step; finding your own “irreplaceability” is the real overtaking.