ERNIE 4.5: June 2025 Open-Weight Multimodal Model Family. This reference distinguishes the original release from hosted endpoints, downloadable weights and subsequent version changes. It is written for people deciding whether an exact model or checkpoint is suitable for a real application, not for leaderboard marketing.
What the sources actually establish
Baidu’s ERNIE team announced the open release of ERNIE 4.5 on June 30, 2025, describing ten variants including MoE models and a small dense model. The largest disclosed variant has 424B total parameters, while alternative MoE configurations activate roughly 47B or 3B. This was a family release, so those sizes must not be conflated into one downloadable artifact.
A useful application and evaluation scenario
For multilingual document understanding, select an exact text or multimodal checkpoint and run a set containing Chinese, English and mixed-language financial or technical documents. Include photographed tables where the selected variant actually accepts image input, then compare extracted line items with a human-verified ground truth. If deployed locally, follow the appropriate PaddlePaddle/inference documentation for that checkpoint rather than assuming conventional transformer servers all work unmodified.
Limits, risks and uncertainties
The publisher describes its architecture and efficiency; the stated FLOPs-utilization result is part of its own training report, not a verified operational promise. ERNIE’s hosted services and open checkpoint availability are different access surfaces. The fact that source weights are downloadable does not settle rights for customer data, trademark, redistribution or use in restricted sectors.
Alternatives and the decision rule
Choose the small variant for constrained hardware when it meets task thresholds; choose a larger multimodal checkpoint only if image/text use requires it. For 2026 improvements, evaluate newer ERNIE lines independently rather than rewriting ERNIE 4.5’s 2025 card as if it always had later features.
A reproducible test before adoption
Prepare a small held-out set from data the organization is authorized to process. Include simple, ambiguous and deliberately unanswerable requests; specify the expected answer or evidence and a human-reviewed scoring rule. Record model identifier, endpoint and region, tokenizer or SDK version, prompt, tool settings, output budget and whether external retrieval was allowed. Do not present a provider-supplied benchmark as an independently measured result. Separate model mistakes from retrieval, OCR and application validation failures.
Operational checklist and dated evidence
A production transition must test 429 responses, timeouts, refusals, invalid structured output and model-unavailable errors. Keep a reversible rollout or safe fallback instead of silently substituting a model behind an old identifier. Measure latency and usage on the actual workload before planning budgets; join dated token quantities to the AI 10 price ledger. Verify enterprise residency, region, support entitlement and data retention with the actual contract, not a public release story.
Sources & Further Reading
- ernie.baidu.com — documentation/reference (supplier-provided reference; not independently checked in this pass)
- ernie.baidu.com — documentation/reference (supplier-provided reference; not independently checked in this pass)
Source documentation establishes vendor-published facts, not independently measured model quality, tenant access, contractual rights or current prices. Use a dated pricing ledger and an account-specific entitlement check for deployment decisions.