Baidu's ERNIE 4.5 Outperforms GPT-4.5 at a Fraction of the Cost

Did you know that Baidu’s latest AI breakthrough is redefining what “value” means in large language models? Ernie 4.5 not only challenges OpenAI’s dominance but does so with a dramatically lower price tag.

The Wake-Up Call for OpenAI

The AI landscape is evolving faster than ever, and Baidu’s Ernie 4.5 just raised the stakes. For years, OpenAI’s GPT series—from GPT-3.5 to GPT-4—set the benchmark for natural language understanding, code generation, and creative tasks. Yet every leap in performance often meant a corresponding jump in cost. Now that Ernie 4.5 has matched or even surpassed GPT-4.5 on key academic and practical benchmarks, industry leaders must rethink their reliance on expensive models. As enterprises weigh performance, scalability, and budget constraints, Baidu’s strategy highlights a new metric: performance per dollar.

How Ernie 4.5 Outperformed GPT-4.5

Baidu’s internal tests show that Ernie 4.5 excels across standard AI benchmarks:

• MMLU (Massive Multitask Language Understanding): Ernie 4.5 scored 87.3% versus GPT-4.5’s 86.8%.
• CMMLU (Chinese MMLU): Ernie achieved 85.2%, demonstrating robust bilingual reasoning.
• GSM8K (Math Problem Solving): Ernie posted 89.6%, outpacing GPT-4.5’s 88%.

Despite these wins, GPT-4.5 retains a slight edge in code synthesis, as measured by HumanEval, and in certain open-domain reasoning tasks where its English-focused training offers advantages. Overall, Ernie’s performance reveals that cost-efficient models can nearly match—and sometimes exceed—the capabilities of pricier alternatives.

The Advantage of Multimodal Capability

One of Ernie 4.5’s standout features is true full-spectrum multimodal support. While GPT-4.5 handles text and images, Ernie extends to audio and video as well. In practical terms, this means:

• Analysing a short video clip, generating a concise summary, and answering follow-up questions based on both audio and visual cues.
• Creating new multimedia assets—images or clips—from text or image prompts.
• Integrating seamlessly with workflows that require cross-modal reasoning, such as automated video editing or interactive virtual assistants.

Multimodal AI is no longer a niche experiment. By enabling unified processing across data types, Ernie 4.5 paves the way for next-generation applications in entertainment, education, and enterprise analytics.

The Shocking Cost Difference

Cost remains the biggest barrier to widespread AI adoption. Baidu’s pricing for Ernie 4.5 via its Kenfon enterprise suite is strikingly low:

• Input Tokens: $0.55 per million tokens
• Output Tokens: $2.20 per million tokens

OpenAI’s corresponding costs for GPT-4.5 through its API are much higher:

• Input Tokens: $75 per million tokens
• Output Tokens: $150 per million tokens

This makes Ernie 4.5 roughly 30 to 70 times more affordable, depending on the use case. Organizations that process millions of tokens daily could slash their AI expenses by orders of magnitude without sacrificing accuracy or capabilities.

Implications Beyond China

Although Ernie 4.5 originates from Baidu, its impact is global. Businesses and governments worldwide may soon demand similar performance-per-cost ratios from Western providers like OpenAI, Google’s Gemini, and Anthropic’s Claude. In turn, open-source projects—Mistral, Meta’s LLaMA, and others—could receive fresh impetus as enterprises look for cost-effective, customizable alternatives. The result? A more competitive market where pricing power shifts toward users rather than incumbents.

OpenAI's Response to the Competitive Landscape

OpenAI has yet to publicly address Ernie 4.5, but internal signals suggest it’s aware of the challenge. The rollout of GPT-4 Turbo aimed to reduce latency and lower per-token costs, while enterprise tiers of ChatGPT promise enhanced collaboration features. Despite these moves, GPT-4.5’s pricing remains comparatively high, leaving space for rivals to attract budget-conscious startups and academic institutions. Meanwhile, other Chinese tech giants—Tencent, Alibaba, Inspur—are accelerating their own AI roadmaps, intensifying global competition.

The Future of AI: A Performance-Per-Dollar Paradigm

The AI race is no longer just about raw performance—it’s about cost efficiency. As models like Ernie 4.5 prove that breakthroughs in AI do not require exorbitant budgets, we can expect a shift toward democratized access. More developers, researchers, and small businesses will be empowered to integrate advanced AI into products and services. This paradigm encourages innovation by lowering financial barriers and spurring continuous improvements in both public and private sectors.

“AI has to be cheaper to be useful at scale. Inference costs can be reduced by more than 90% over 12 months.”
— Robin Lee, CEO of Baidu

Takeaway:

• Evaluate and integrate cost-effective models like Ernie 4.5 to maximize your AI performance per dollar spent.

What do you think about the evolving competition between Ernie, GPT, and other AI models? Share your thoughts below and let’s discuss the future of cost-efficient intelligence.