Baidu ERNIE vs Deep Infra: Token Pricing, Speed & Intelligence
Full comparison of Baidu ERNIE and Deep Infra — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Baidu ERNIE
ERNIE 5 — Baidu's frontier Chinese-English bilingual LLM API
Baidu ERNIE is the LLM API from Baidu, China's largest search company. The ERNIE 5 series includes ERNIE-5.1 (balanced) and ERNIE-5.0 (frontier), both optimised for Chinese-English bilingual tasks, knowledge retrieval, and enterprise applications. Pricing is in USD via the international API.
Deep Infra
Ultra-cheap open-weight inference — Llama 4, DeepSeek V4 Pro, Qwen3 at rock-bottom prices
Deep Infra is an inference-focused API provider that consistently offers the lowest per-token prices for open-weight models. The 2026 catalog includes Llama 4 Scout/Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, and the full Llama 3.x family. Deep Infra is the go-to choice for cost-sensitive production inference where price is the primary constraint.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Baidu ERNIE
Deep Infra
Key differentiators
ERNIE 5 is the leading Chinese-English bilingual LLM API, backed by Baidu's search knowledge graph — the go-to choice for applications targeting Chinese-language users.
The most price-competitive inference API for open-weight models — Llama 4, DeepSeek V4 Pro, and Qwen3 at prices 30–50% below comparable providers, with zero migration friction via OpenAI-compatible API.
Frequently asked questions
Baidu ERNIE FAQs
What is ERNIE?
ERNIE (Enhanced Representation through Knowledge Integration) is Baidu's LLM series. ERNIE 5 is the current generation, with ERNIE-5.1 (balanced) and ERNIE-5.0 (frontier) available via the Wenxin Qianfan API.
How much does the ERNIE API cost?
ERNIE-5.1 is $0.56/$2.53 per 1M input/output tokens. ERNIE-5.0 is $1.40/$5.60 per 1M tokens. See the Baidu Cloud pricing page for current rates.
Is ERNIE good for English tasks?
ERNIE 5 handles English competently but is primarily optimised for Chinese. For English-only workloads, frontier Western models typically outperform ERNIE. For Chinese or bilingual Chinese-English tasks, ERNIE is among the strongest options available.
Deep Infra FAQs
What models does Deep Infra support in 2026?
Deep Infra hosts Llama 4 Scout and Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, the full Llama 3.x family, Mistral, Mixtral, and many others. The catalog is updated rapidly as new open-weight models are released.
How cheap is Deep Infra compared to other providers?
Deep Infra is consistently among the more affordable providers for open-weight models. Check deepinfra.com/pricing for current rates on Llama 3.1 8B, Llama 3.3 70B, and other models.
Is Deep Infra OpenAI-compatible?
Yes. Deep Infra provides an OpenAI-compatible API, so you can use the OpenAI SDK by pointing it at the Deep Infra endpoint. This makes migration from OpenAI or other providers straightforward.
Provider resources
Baidu ERNIE — ERNIE 5 — Baidu's frontier Chinese-English bilingual LLM API
Baidu ERNIE is the LLM API from Baidu, China's largest search company. The ERNIE 5 series includes ERNIE-5.1 (balanced) and ERNIE-5.0 (frontier), both optimised for Chinese-English bilingual tasks, knowledge retrieval, and enterprise applications. Pricing is in USD via the international API.
ERNIE 5 is the leading Chinese-English bilingual LLM API, backed by Baidu's search knowledge graph — the go-to choice for applications targeting Chinese-language users.
Deep Infra — Ultra-cheap open-weight inference — Llama 4, DeepSeek V4 Pro, Qwen3 at rock-bottom prices
Deep Infra is an inference-focused API provider that consistently offers the lowest per-token prices for open-weight models. The 2026 catalog includes Llama 4 Scout/Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, and the full Llama 3.x family. Deep Infra is the go-to choice for cost-sensitive production inference where price is the primary constraint.
The most price-competitive inference API for open-weight models — Llama 4, DeepSeek V4 Pro, and Qwen3 at prices 30–50% below comparable providers, with zero migration friction via OpenAI-compatible API.
Key strengths compared
Baidu ERNIE
- Strong Chinese-English bilingual performance — best-in-class for Chinese language tasks
- Deep integration with Baidu Search knowledge graph
- ERNIE-5.1 at $0.56/$2.53 per 1M tokens is competitive for a frontier-tier bilingual model
- Primarily optimised for Chinese — English performance trails frontier Western models
- Limited ecosystem outside China; fewer third-party integrations
Deep Infra
- Consistently lowest prices for open-weight models — often 30–50% below competitors
- 2026 catalog: Llama 4, DeepSeek V4 Pro, Qwen3-235B, Kimi K3
- Wide model catalog updated rapidly as new models release
- No proprietary models — open-weight only
- No enterprise SLAs or dedicated support
Key weaknesses compared
Baidu ERNIE
- Primarily optimised for Chinese — English performance trails frontier Western models
- Limited ecosystem outside China; fewer third-party integrations
- Documentation and support primarily in Chinese
Deep Infra
- No proprietary models — open-weight only
- No enterprise SLAs or dedicated support
- Smaller ecosystem than Together AI or Fireworks
Provider category context
Baidu ERNIE is a frontier lab, founded in 2000. Deep Infra is a inference api, founded in 2023. Baidu ERNIE as a frontier lab trains and serves its own proprietary models. Deep Infra as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.
How to choose between them
Choose Baidu ERNIE if you need Chinese NLP. Choose Deep Infra if you need consistently lowest prices for open-weight models — often 30–50% below competitors. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.