What is ERNIE 3.5?
ERNIE 3.5 is Baidu’s general-purpose large language model family. Baidu introduced it in May 2023 as a foundation for ERNIE Bot and later made it available through its Qianfan ModelBuilder and Wenxin Workshop cloud services. Rather than being a single immutable endpoint, ERNIE 3.5 is a group of related deployments with different context capacities, preview states, and dated versions.
The model is designed primarily for text work. It can accept instructions and written context, then generate responses for conversation, question answering, drafting, rewriting, summarization, information extraction, classification, and other language-based workflows. Baidu also documents plugin and search-enhanced application scenarios, allowing some deployments to use external tools or Baidu Search alongside ordinary model generation.
For a beginner, the practical distinction is that ERNIE 3.5 is best understood as a text-focused application model rather than an image, audio, or video system. Its main value is the combination of Chinese-language capability, general enterprise text processing, Baidu platform integration, and relatively low cited token prices.
Where ERNIE 3.5 fits in Baidu’s catalog
ERNIE 3.5 sits within Baidu’s ERNIE model family and is exposed through Baidu’s AI cloud services. It is associated with ERNIE Bot and can be used through Qianfan and Wenxin Workshop interfaces. The supplied documentation identifies the following principal API variants:
ernie-3.5-8k, the standard context variant documented for ordinary workloads.ernie-3.5-8k-preview, a preview deployment associated with a newer service state.ernie-3.5-128k, a long-context variant released on May 16, 2024.
Baidu also records dated identifiers such as ERNIE-3.5-8K-0613 and ERNIE-3.5-8K-0701. These are better treated as dated versions or deployment snapshots of the ERNIE 3.5 family than as separate model families. Lifecycle status can differ between identifiers: Baidu has marked some older dated deployments as retired or migrated, while later notices describe upgrades of standard 8K and 128K services to preview versions. Production users should therefore verify the exact endpoint, not only the family name.
Core capabilities and strengths
ERNIE 3.5’s documented capabilities center on understanding and generating Chinese and English text. The model is suitable for multi-turn dialogue, question answering, content creation, rewriting, information processing, and general instruction following. These capabilities support practical uses such as customer-service assistants, internal knowledge workflows, document transformation, and structured extraction from business text.
- Chinese and English text: The family is intended for bilingual language understanding and generation, with Chinese-language applications being a particularly relevant use case.
- Content creation: It can support drafting, rewriting, summarization, and other editorial workflows.
- Information processing: Applications can use it for extraction, classification, and transforming unstructured text into more usable forms.
- Reasoning and instruction following: Baidu positions the model for general reasoning and instruction-based tasks, although the supplied research does not provide an authoritative benchmark score.
- Streaming: The API supports streaming text responses, which can allow an application to display output progressively instead of waiting for the complete response.
- Search and plugins: Baidu documents plugin-enabled workflows and optional search enhancement for applicable ERNIE 3.5 interfaces.
These strengths make ERNIE 3.5 more relevant to ordinary language applications than to specialist multimodal workloads. It can be a reasonable fit when the main inputs and outputs are text and the application benefits from Baidu’s surrounding services.
Baidu Search and tool integration
One of ERNIE 3.5’s notable platform features is integration with Baidu Search. Search enhancement can retrieve fresher information than the model’s underlying training data, which is useful for questions involving current information. It is important to distinguish this external search feature from the model’s own learned knowledge: enabling search does not change the model’s knowledge cutoff.
The documentation describes search enhancement as enabled by default for applicable ERNIE 3.5 interfaces, with a disable_search parameter available to turn it off. Search-triggered requests may incur a separate per-call charge in addition to token usage. This creates a practical trade-off: search can improve freshness, but it can also increase cost and introduce dependence on the availability and quality of the external search service.
The research also supports tool or plugin use at the family level. However, it does not establish a universal, provider-defined function-calling specification that applies identically to every ERNIE 3.5 endpoint. Developers should confirm the supported tool format and parameters for the chosen Qianfan or Wenxin Workshop API.
Context and output limits
Context length depends on the variant. The ERNIE-3.5-8K deployments are intended for substantially smaller prompts than ERNIE-3.5-128K, while the 128K version supports a documented 128K-token context window. A context window includes the material supplied to the model and the generated response, subject to the endpoint’s own allocation rules.
Baidu’s older ERNIE 3.5 documentation lists a maximum output of 2,048 tokens for an earlier service configuration. That figure should not be treated as a universal output limit for the entire family because output limits may vary by endpoint, version, and service configuration. The supplied research does not establish one current maximum-output value that applies to every ERNIE 3.5 deployment.
In practical terms, choose an 8K deployment for ordinary prompts and shorter business interactions when its limits are sufficient. Choose the 128K variant when the application must process much longer documents or conversations, while checking the current endpoint documentation for its output allocation, pricing, and availability.
Pricing and API access
The cited Baidu pricing announcement gives post-adjustment prices of CNY 0.0008 per 1,000 input tokens and CNY 0.002 per 1,000 output tokens for both the ERNIE-3.5-8K and ERNIE-3.5-128K series. These figures are token-based rather than subscription prices. Input tokens are the text sent to the model; output tokens are the text it generates.
At those cited rates, one million input tokens would cost approximately CNY 0.80, while one million output tokens would cost approximately CNY 2.00. This calculation is only an interpretation of the published per-1,000-token figures, not a guarantee of a current invoice amount. Actual billing may depend on service mode, region, quotas, model version, account terms, and later pricing changes. Search enhancement can add a separate charge when it is triggered.
ERNIE 3.5 is accessed through Baidu’s Qianfan and Wenxin Workshop services rather than as a standalone downloadable model in the supplied documentation. Before deployment, developers should confirm the active model identifier, endpoint availability, token accounting rules, request limits, and whether the selected service has moved to a preview or successor deployment.
Modalities, reasoning, and coding suitability
The documented ERNIE 3.5 family is text-focused. The supplied research supports text input and text output, but does not establish native image, audio, or video input or output for the standard family. It should not be selected as a native image-generation, speech, audio, or video-generation system.
Baidu positions ERNIE 3.5 for general reasoning and instruction following. The research does not provide a provider-published reasoning benchmark or a dedicated reasoning mode, so its reasoning performance should be evaluated on the specific tasks that matter to an application. It can be used for ordinary analytical and information-processing workflows, but users needing a newer specialist reasoning model may find another option more appropriate.
Coding is possible as a text-generation use case, including code explanation, drafting, and transformation, but the supplied material does not identify ERNIE 3.5 as a dedicated coding model or provide coding benchmark results. The editorial coding assessment for this page is therefore comparative rather than an official Baidu specification. The same applies to the editorial scores for reasoning, speed, and cost.
Speed, cost, and capability trade-offs
ERNIE 3.5’s main practical trade-off is broad general-purpose capability at a low cited token price rather than specialization in a single advanced modality. The documented input and output rates make it potentially attractive for high-volume Chinese-language text workloads, especially when requests are relatively straightforward and the 8K context is sufficient.
The 128K variant can reduce the need to split long documents into many smaller requests, but a larger context window does not automatically mean better answers. Long prompts still need careful document selection and application design, and the larger deployment may have different operational costs or availability. Streaming can improve perceived responsiveness by returning partial text while generation continues, although it does not by itself establish a faster underlying generation rate.
Search integration improves access to current information but adds an external dependency and may add per-call cost. Preview and dated versions may offer different behavior or availability, so consistency-sensitive applications should pin and monitor the exact endpoint where Baidu permits it.
When to choose ERNIE 3.5
ERNIE 3.5 is a sensible option when the application has most of the following characteristics:
- It primarily needs Chinese or bilingual text understanding and generation.
- It requires dialogue, drafting, rewriting, summarization, extraction, or general question answering.
- It can benefit from optional Baidu Search enhancement.
- It needs streaming responses for an interactive interface.
- It is cost-sensitive and the cited Qianfan token prices are available to the account.
- Its prompts fit the selected 8K deployment, or it specifically benefits from the 128K variant.
Another option may be more appropriate when the application requires native image, audio, or video processing; a fixed and universally documented output limit; a specialist coding or reasoning model; or a lifecycle policy that does not involve preview and dated deployment changes. Applications needing current information should also compare the cost and reliability of Baidu Search enhancement with other retrieval designs rather than assuming that search integration is always the best choice.
Limitations and lifecycle considerations
The most important limitation is variation across the family. Context capacity, output limits, pricing, preview status, and retirement schedules can differ by model identifier. The 128K context claim applies to ERNIE-3.5-128K, not automatically to every ERNIE 3.5 endpoint. Similarly, the earlier 2,048-token output figure belongs to an older service configuration and should not be generalized.
The family is also not a native multimodal generation system according to the supplied documentation. Search-enhanced responses depend on an external service and can incur additional charges. Finally, Baidu’s update records show that dated deployments can be retired or migrated and that standard services can be upgraded to preview versions.
Overall, ERNIE 3.5 is best viewed as a Baidu-hosted, text-first model family for Chinese and English enterprise applications. Its strongest reasons for consideration are its language focus, Qianfan and Wenxin Workshop access, optional Baidu Search integration, streaming support, long-context 128K variant, and low cited token pricing. Its main cautions are endpoint-specific limits, changing lifecycle status, uncertain universal output limits, and the absence of documented native multimodal support.