A powerful multimodal model from Baidu, offering strong intelligence and a large context window at a competitive price, but hampered by slow generation speeds.
ERNIE 5.0 Thinking Preview is the latest flagship large language model from Chinese technology giant Baidu. Positioned as a powerful, multimodal system, it aims to compete at the higher end of the market by combining strong reasoning capabilities with a massive 128,000-token context window. This combination makes it theoretically well-suited for complex tasks involving long documents, detailed instructions, or a mix of input types including text, images, and even video. The "Thinking Preview" designation suggests this is an advanced, perhaps experimental, version of the model, offering a glimpse into Baidu's cutting-edge research and development.
On the Artificial Analysis Intelligence Index, ERNIE 5.0 scores a respectable 53, placing it comfortably above the class average of 44. This puts it in the top third of the 101 models benchmarked, indicating a solid capacity for reasoning, instruction-following, and knowledge-based tasks. However, this intelligence comes with a significant quirk: extreme verbosity. During our evaluation, the model generated a staggering 81 million tokens, nearly three times the average of 28 million. This tendency to produce lengthy, detailed outputs is a critical factor to consider, as it has major implications for both cost and speed.
Financially, ERNIE 5.0 presents a compelling but deceptive picture. The per-token pricing is exceptionally competitive. At $0.84 per million input tokens and $3.37 per million output tokens, it ranks among the cheapest models in its intelligence class, with prices far below the respective averages of $1.60 and $10.00. The total cost to run our intelligence benchmark was $322.90. While these numbers are attractive, they are offset by the model's performance characteristics. The primary drawback is its speed; with an output of just 16 tokens per second, it is one of the slowest models we've tested, falling far short of the 71 tokens/second average. This, combined with a high latency of over 3.5 seconds to get the first token, makes it unsuitable for any real-time or interactive applications.
In essence, ERNIE 5.0 Thinking Preview is a model of trade-offs. It offers developers access to high-tier intelligence, multimodality, and a large context window at a very low per-token price. However, users must be prepared to manage its slow performance and exceptionally verbose nature, which can inflate final costs and create a sluggish user experience. It is best viewed as a specialized tool for complex, asynchronous tasks where depth and detail are valued more than speed and brevity.
53 (31 / 101)
16.0 tokens/s
0.84 $ / 1M tokens
3.37 $ / 1M tokens
81M tokens
3.55 seconds
| Spec | Details |
|---|---|
| Owner | Baidu |
| License | Proprietary |
| Context Window | 128,000 tokens |
| Input Modalities | Text, Image, Video |
| Output Modalities | Text |
| Intelligence Index | 53 / 100 |
| Intelligence Rank | #31 / 101 |
| Provider | ZenMux |
| Input Price | $0.84 / 1M tokens |
| Output Price | $3.37 / 1M tokens |
| Blended Price (3:1) | $1.47 / 1M tokens |
| Output Speed | 16.0 tokens/s |
| Latency (TTFT) | 3.55 seconds |
Currently, ERNIE 5.0 Thinking Preview has been benchmarked through a single API provider, ZenMux. This makes the choice of provider straightforward, but it also means there are no alternative options to optimize for different priorities like speed or regional availability.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Balanced | ZenMux | The sole benchmarked provider, offering access to the model's full feature set at its advertised competitive price. | No other provider options exist to compare performance or pricing against. |
| Lowest Cost | ZenMux | Provides access to ERNIE 5.0's very low input ($0.84) and output ($3.37) per-token prices. | The model's inherent high verbosity can lead to high total costs despite the low rate. |
| Highest Speed | ZenMux | The only available option for accessing the model. | Performance is dictated by the model itself, which is notably slow (16 tokens/s) with high latency. |
| Feature Access | ZenMux | Enables use of the model's key features, including the 128k context window and multimodal inputs. | Performance and reliability are dependent on a single provider's infrastructure. |
Provider benchmarks reflect performance at a specific point in time. Performance and pricing may vary. The 'Pick' is based on the data available and the stated priority.
The abstract per-token price of a model can be misleading. To understand the true cost of using ERNIE 5.0 Thinking Preview, it's crucial to model real-world scenarios. The examples below illustrate how the model's high verbosity and 4:1 output-to-input price ratio affect the final cost of common tasks.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Article Summarization | 20,000 tokens (long article) | 2,000 tokens (verbose summary) | A common task leveraging the large context window. | ~$0.024 |
| RAG Q&A | 4,100 tokens (context + query) | 500 tokens (detailed answer) | Represents a knowledge retrieval and synthesis task. | < $0.01 |
| Creative Content Drafting | 500 tokens (prompt) | 4,000 tokens (verbose draft) | A generation-heavy task where output far exceeds input. | ~$0.014 |
| Image Analysis & Description | 2,500 tokens (image + detailed prompt) | 1,500 tokens (verbose description) | A typical multimodal use case. | ~$0.007 |
The takeaway is clear: while individual job costs are low, the final price is highly sensitive to the number of output tokens. Generation-heavy tasks like content drafting are significantly more expensive than they would be with a less verbose model, even if that model had a higher per-token output price.
Given ERNIE 5.0's unique profile of low price, high intelligence, high verbosity, and low speed, a specific strategy is required to use it effectively and affordably. The key is to maximize its strengths (context, intelligence) while mitigating its weaknesses (verbosity, speed).
Your primary cost control lever is managing the model's tendency for verbose outputs. This must be done through careful prompt engineering.
The 128k context window is not just for single large documents; it's a powerful tool for cost efficiency on smaller, related tasks.
The model's high latency (3.55s) and slow generation speed (16 t/s) make it unsuitable for any user-facing, real-time application. Attempting to use it for chat will result in a poor user experience.
Do not rely on the blended price of $1.47/1M tokens. Your actual cost will be determined by your specific input-to-output ratio.
ERNIE 5.0 Thinking Preview is a high-end, multimodal large language model developed by Baidu. It features a large 128,000-token context window and can process text, image, and video inputs to generate text outputs. The "Preview" tag suggests it is an early or experimental release.
This model is best for developers and businesses that require deep reasoning on complex, long-form content and can perform these tasks asynchronously. Use cases like in-depth document analysis, complex report generation, or multimodal data processing are a good fit, provided the user can tolerate slow response times.
ERNIE 5.0 has very competitive per-token pricing at $0.84 per 1M input tokens and $3.37 per 1M output tokens. However, its extreme verbosity means it generates many output tokens, which can lead to higher-than-expected total costs for tasks that require a lot of generation.
The slow performance, characterized by a high 3.55-second latency and a low 16 token/second output speed, could be due to several factors. These may include the inherent complexity of the model's architecture, a lack of optimization for speed as a "Preview" version, or the infrastructure of the API provider. It is not suitable for real-time applications.
In practice, high verbosity means the model tends to provide much longer and more detailed answers than necessary unless specifically instructed otherwise. This can be a benefit for tasks requiring exhaustive detail but becomes a significant cost driver and can increase wait times for users.
No, it is highly discouraged. The combination of high latency (a long wait for the first word) and slow output speed (slow typing effect) would create a very poor and frustrating user experience. It should be used for background, non-interactive tasks.