Baidu ERNIE vs Moonshot AI: Token Pricing, Speed & Intelligence
Full comparison of Baidu ERNIE and Moonshot AI — 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.
Moonshot AI
Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Baidu ERNIE
Moonshot AI
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.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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.
Moonshot AI FAQs
What is Kimi K3?
Kimi K3 is Moonshot AI's latest 1-trillion-parameter mixture-of-experts model, succeeding K2. It activates approximately 32B parameters per token and leads on coding and agentic benchmarks as of September 2026.
What is the difference between Kimi K3 and K2.6?
Kimi K3 is the latest generation with improved benchmark scores. Kimi K2.6 is a stable, battle-tested version suitable for production workloads that prioritise reliability over cutting-edge performance.
Is Kimi K3 open-weight?
Yes. Kimi K3 weights are publicly available on Hugging Face, making it one of the largest open-weight models available. Teams can self-host it on multi-GPU clusters or access it via the Moonshot API.
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.
Moonshot AI — Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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
Moonshot AI
- Kimi K3: 1T MoE open-weight model with strong coding and agentic benchmark scores
- Kimi K2.6 available as stable production option
- Competitive on agentic and tool-use benchmarks
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
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
Moonshot AI
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
- Fewer third-party integrations available
Provider category context
Baidu ERNIE is a frontier lab, founded in 2000. Moonshot AI is a frontier lab, founded in 2023. Both are frontier lab providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Baidu ERNIE and Moonshot AI are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: Baidu ERNIE leads on strong chinese-english bilingual performance — best-in-class for chinese language tasks, while Moonshot AI leads on kimi k3: 1t moe open-weight model with strong coding and agentic benchmark scores. For cost-sensitive workloads, compare the cheapest model tier from each provider in the pricing table above — the gap between efficient-tier models is often larger than between flagship models.