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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11On April 25, 2025, Baidu announced ERNIE 4.5 Turbo and ERNIE X1 Turbo at its Create 2025 developer conference. The launch-period Qianfan listing priced ERNIE 4.5 Turbo at RMB 0.8 per million input tokens and RMB 3.2 per million output tokens, while ERNIE X1 Turbo cost RMB 1 and RMB 4 respectively. Baidu said those prices represented reductions of 80% and 50% against the corresponding non-Turbo models. The figures were launch prices, not verified August 2026 rates, and Baidu’s claims that the models beat DeepSeek remain company-reported comparisons.
What Baidu launched
The two products addressed different jobs rather than being interchangeable versions of one model. Baidu’s April 2025 announcement described both as faster, lower-cost updates to existing ERNIE lines.
| Model | Positioning | Capabilities Baidu highlighted | Launch service and price |
|---|---|---|---|
| ERNIE 4.5 Turbo | General multimodal foundation model | Text and image understanding, multimodal and logical reasoning, coding, and fewer hallucinations | RMB 0.8 per million input tokens; RMB 3.2 per million output tokens |
| ERNIE X1 Turbo | Reasoning or “deep-thinking” model for multistep work | Logic, mathematics, literary creation, image understanding, and tool use | ERNIE-X1-Turbo-32K; RMB 1 per million input tokens; RMB 4 per million output tokens |
The 32K identifier refers to the launch-period X1 Turbo service. Qianfan’s later records mention an ERNIE 4.5 Turbo 128K Preview, so buyers should verify the context limit of the exact endpoint they select rather than assume every Turbo service has the same window.
How large were the price cuts?
Baidu said ERNIE 4.5 Turbo was 80% cheaper than ERNIE 4.5 and X1 Turbo was 50% cheaper than ERNIE X1. The April 30, 2025 Qianfan update also listed offline batch inference for Turbo models at 40% of online-service pricing. Those are historical launch-period terms; current pricing, quotas and availability must be checked directly with Qianfan.
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Token rates are not the same as the cost of completing a task. Your bill can also depend on:
- Prompt and output length.
- Whether a reasoning model generates many additional reasoning tokens.
- Online versus batch processing.
- Context-window requirements and multimodal input processing.
- Tool calls, minimum billing rules, quotas and service-specific charges.
A short prompt that produces a long reasoning trace may cost more than a conventional model with a higher headline input rate. Compare complete workloads, not just the input-token number.
What “Turbo” and “faster” establish
Baidu said the Turbo variants were faster, but the launch material does not provide an independent latency table or a specific percentage improvement. “Faster” can refer to several different measurements:
Rank #2
- Time to first token: how quickly streaming begins.
- Generation throughput: tokens produced per second.
- End-to-end latency: time until the answer is complete.
- Concurrency: whether speed holds when many users share the service.
- Reasoning latency: a cheaper reasoning token can still take longer if the model emits more of them.
- Multimodal latency: image transfer and preprocessing add time.
Latency can also differ materially between mainland China and overseas users. A production test should measure the complete request, under expected concurrency, region and modality.
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Why this became part of China’s AI price war
DeepSeek’s low-cost models reset expectations about inference pricing. Baidu was responding not only to DeepSeek but also to Alibaba’s Qwen, ByteDance’s Doubao and other domestic providers. Contemporary coverage, including Techmeme’s coverage index and a WinBuzzer report, framed the announcement as a price-war escalation.
Lower API prices are a distribution strategy. They can attract startups, increase application volume and pull customers into a broader cloud platform for fine-tuning, tools, deployment and agent development. The trade-off is margin pressure: providers still must pay for chips, networking, storage, support and model training. The strategic contest therefore includes reliability, Chinese-language quality, safety and compliance, quotas, tool calling, local deployment and enterprise support—not merely benchmark rankings.
How Baidu compared Turbo with DeepSeek
Baidu marketed ERNIE 4.5 Turbo as costing about 40% of DeepSeek V3’s price and X1 Turbo as about 25% of DeepSeek R1’s cost, according to the contemporary comparison cited above. Those statements should be read as Baidu’s positioning, not proof that the models are universally cheaper.
A fair comparison must specify the DeepSeek version and date, input versus output or blended pricing, cached-input and batch discounts, treatment of reasoning tokens, context limits, modalities, rate limits and service availability. A model can have a lower per-token price but a higher per-task bill if it uses more output tokens.
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What Baidu claimed about performance
Baidu said X1 Turbo outperformed DeepSeek R1 and a then-current DeepSeek V3, and said 4.5 Turbo improved multimodal reasoning, logical reasoning, coding and hallucination performance. It also promoted stronger tool use and image understanding for the X1 line. These are claims in the company’s official announcement.
The available launch material does not independently establish those results. It does not show replicated tests with identical prompts and sampling settings, rule out selective benchmark reporting, or demonstrate that a benchmark advantage transfers to production workloads. It also does not establish equivalent English-language performance, safety behavior, reliability or rate limits.
Qianfan’s role in the strategy
Qianfan was the distribution and monetization layer: hosted model APIs, application development, evaluation, tools, fine-tuning and enterprise deployment. Baidu explicitly linked lower inference costs to removing a barrier for developers and encouraging more enterprise applications.
For a buyer evaluating Qianfan, check:
- Account, payment and regional-access requirements, especially outside mainland China.
- Where prompts and outputs are processed, stored and governed.
- Chinese-language and multimodal quality on your own data.
- OpenAI-compatible requests, streaming, structured output, function calling and tool support.
- Production quotas, rate limits, support and service-level commitments.
- Migration costs if your application depends on Baidu-specific tools or infrastructure.
- Chinese data, content and generative-AI compliance obligations, with legal advice where necessary.
Hosted Turbo services versus open-source ERNIE
Baidu’s April announcement said it planned to open-source ERNIE 4.5 Turbo code in June. The later official ERNIE repository documents the ERNIE 4.5 family, ERNIEKit and FastDeploy, and states Apache 2.0 licensing for listed ERNIE 4.5 models. That repository does not by itself prove that every hosted Turbo endpoint or every X1 Turbo variant has identical weights, capabilities or license terms.
Best Value
Managed Qianfan access
Hosted access avoids GPU procurement and inference operations and can provide Baidu’s optimized service path. It introduces recurring token charges, vendor quotas, regional dependencies and less control over data handling and model updates.
Self-hosting with ERNIEKit and FastDeploy
Self-hosting can improve data control, enable customization and eliminate per-token API charges after infrastructure costs. It requires suitable hardware, inference and quantization expertise, monitoring, batching, upgrades and ongoing power and networking budgets. Public weights may not match the performance or optimization of a hosted Turbo endpoint.
Who benefits—and who faces pressure
Potential beneficiaries
- Chinese developers and startups with high inference volume.
- Enterprises building Chinese-language, multimodal or agentic applications.
- Baidu Cloud customers that value integrated tools, deployment and support.
- Teams able to batch workloads and use the listed discount.
Potentially disadvantaged groups
- Providers competing mainly on API price without differentiated tooling or distribution.
- Customers still paying legacy rates for comparable workloads.
- International teams that need globally distributed endpoints, simple foreign billing or cloud-neutral deployment.
What the announcement did not prove
- It did not prove a specific percentage speed gain.
- It did not independently verify that X1 Turbo beats DeepSeek R1 or that 4.5 Turbo wins every multimodal task.
- It did not establish total cost per completed task, production reliability or overseas latency.
- It did not show that all Turbo models were open source under identical terms.
- It did not establish that the April 2025 prices remained available in August 2026.
The 2026 context
The Turbo launch was an important April 2025 escalation, but it is not Baidu’s newest model milestone. Baidu’s first-quarter 2026 materials refer to the subsequent ERNIE 5.1 launch in May 2026. Treat Turbo as a dated product cycle when comparing it with current offerings.
The practical lesson is economic as much as technical: Qianfan may suit a China-oriented managed deployment, while ERNIEKit and FastDeploy suit teams prepared to operate their own stack. DeepSeek, Qwen, Doubao and international providers remain alternatives whose value depends on workload, geography, compliance and support—not a single headline token price.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
