ERNIE-5.5-Flash-Base API Pricing Review (2026): No API Yet

There is no official ERNIE-5.5-Flash-Base API pricing because Baidu published no hosted endpoint or rate card. Baidu published free-to-download weights under a restrictive license on November 5, 2026, but running the 280B checkpoint takes datacenter GPUs, and the custom license blocks the EU, UK, and South Korea outright. It is a research foundation, not a drop-in chat endpoint.

What you actually download

ERNIE-5.5-Flash-Base is the checkpoint taken at the end of pre-training, before any mid-training or post-training. Baidu states this explicitly: the post-trained ERNIE 5.5 Flash "will follow" with no date. You get text continuation, not instruction following, no chat template, no tool use, no safety tuning.

The headline spec is 280B total parameters with 14B activated per token, trained from scratch on 33.5T tokens with the Muon optimizer in FP8 mixed precision. Hugging Face sums the safetensors at about 286.2B parameters, slightly above Baidu's 280B headline figure.

Heavily Compressed Attention stores one cache entry per 128-token block on most layers, with only one layer in seven keeping full context. ECHO reuses the final layer as a draft model for speculative decoding instead of a separate helper.

Repositories are baidu/ERNIE-5.5-Flash-Base and baidu/ERNIE-5.5-Flash-Base-BF16, publicly listed with access granted after license acceptance via the license. The model page listed no inference provider at publication, and ModelCap showed 0 providers.

ERNIE-5.5-Flash-Base API pricing: $0 download, datacenter bill

If you searched for per-token ERNIE-5.5-Flash-Base API pricing, the short answer is $0 for weights and no published per-million-token rate. Baidu published no rate card and no hosted endpoint. The practical price is storage, GPUs, and engineering time. No hourly cloud estimate is given here because Baidu published no reference deployment to price against.

Baidu's launch examples assume 4-way parallelism. In practice, treat these capacity estimates as the floor (memory-based, not all Baidu-validated):

In Baidu's documented recipes, the full 1,048,576-token context does not fit on 4x80 GB cards: the 4xH100 recipe peaks at 262,144 tokens single-request at 0.93 memory utilization with no concurrency headroom, and the 1M recipe targets Blackwell with 0.85 utilization plus reduced concurrency. For multi-request production on 4xH100, keep 32K or shorter. Training stopped at 128K, so behavior past 128K needs extra tuning and testing. The reference full-model SFT setup in the technical profile is 64x GB300 (16 nodes x 4).

Self-hosting is primarily justified by data control or post-training needs, not by a demonstrated token-cost advantage.

The license blocks more teams than VRAM does

The weights use the ERNIE 5.5 Flash Base Model License Agreement v1.0, not Apache 2.0 or MIT.

• Territory is worldwide except the EU, UK, and South Korea. Section 5.3 also restricts use of outputs outside the licensed territory.
• Commercial use at or below $50M in related annual revenue (aggregated across affiliates, entire product revenue counts) needs a one-time email notice to Baidu before first use plus prominent "Powered by ERNIE" attribution in UI, About page, or API docs.
• Commercial use above $50M needs prior written authorization. Applying does not grant permission.
• Outputs, materials, and derivatives cannot be used to improve another AI model, except an ERNIE derivative. Quantized and fine-tuned builds inherit the license.
• Provided "AS IS" under Hong Kong law.

Local developers flagged this within hours of release:

"Baidu's new open model edges out DeepSeek on Baidu's own chart, and if you work in the EU, its licence doesn't let you use it." — @MartinSzerment on X

No one in the r/LocalLLaMA thread had served real traffic yet; questions were hardware fit, GGUF timing, and the instruct release date.

Benchmarks against DeepSeek V4 Flash Base, Hy3, and MiMo

All numbers are Baidu-run on a unified pipeline with common prompts and few-shot settings. There was no independent reproduction at publication, and ModelCap rates the model provisional at #127 of 352 with Index 51.2, 0% independent evidence share, and 15.1% support. Treat the table as vendor signal, not a leaderboard.

Baidu's full 30-row table puts ERNIE highest-scoring in 24 rows and trailing in 6: ChineseSimpleQA, BBH CoT, INCLUDE-lite, FullStackBench, HELMET-Rerank, and InfiniteBench. Head-to-head against DeepSeek-V4-Flash-Base alone it leads 26 of 30, losing only ChineseSimpleQA, INCLUDE-lite, HELMET-Rerank, and InfiniteBench. The excerpt below shows 15 key rows including all 6 best-in-field losses.

Code is the headline gap over DeepSeek, while long context splits: ERNIE takes RepoQA but drops HELMET-Rerank and InfiniteBench. Baidu compared against DeepSeek V4 Flash Base, not the newer V4.1 line. Base scores say nothing about instruction following or tool use, and the 1M context is config-enabled, not training-proven.

How to run it without wasting a weekend

Use /v1/completions, not /v1/chat/completions. There is no chat template. Send a raw prompt with a BOS token (add_special_tokens=True), temperature 0 for factual probes, and few-shot patterns instead of instructions. If output rambles, add examples and a stop sequence like "\nQ:". This is expected base-model behavior.

vLLM path (needs exactly 0.32.0 plus Baidu's plugin in a fresh env):

pip install vllm==0.32.0
pip install "git+https://github.com/baidu/vLLM-ERNIE.git"
vllm serve baidu/ERNIE-5.5-Flash-Base \
  --kv-cache-dtype fp8 \
  --enable-expert-parallel \
  --data-parallel-size 4 \
  --max-model-len 131072 \
  --port 30000 \
  --moe-backend humming

SGLang path:

docker run --gpus all -p 30000:30000 \
  lmsysorg/sglang:dev-ernie-55-flash \
  sglang serve \
  --model-path baidu/ERNIE-5.5-Flash-Base \
  --tp 4 --attn-dp-size 4 \
  --context-length 131072 \
  --max-running-requests 1024

• ernie5_5_moe is not supported — plugin or image version is wrong; use vLLM 0.32.0 plus the plugin or the dev SGLang image.
• Chat-template error — you called /v1/chat/completions; switch to /v1/completions.
• OOM on 4x80 GB — FP8 weights fill the cards; drop to 32K context for concurrent serving or move to 8xH100 / 4xH200.
• Download refused — log into Hugging Face, accept the license, or try ModelScope from an allowed territory.
• No GGUF / Ollama / LM Studio / MLX at release — documented as unsupported at publication.

Should you bother, or pick a hosted flash model

The open question is timing: if the instruct release keeps these territory and revenue terms, strong base numbers will not help most production teams.

Related reading: DeepSeek V4.1 Flash API pricing, GLM-5.3 Flash API pricing, Hy4 preview API pricing.

ERNIE-5.5-Flash-Base FAQ

Is there an official Baidu-hosted API for ERNIE-5.5-Flash-Base?

No. Baidu released weights with SGLang and vLLM recipes and no per-token rate card, and no inference provider served the model at publication.

How much does ERNIE-5.5-Flash-Base cost per million tokens?

Nothing published. Downloads are free under the custom license; the cost is self-hosting roughly 286B parameters plus KV cache and operations.

Is ERNIE-5.5-Flash-Base open source?

No, it is open-weight under a custom Baidu license with territory, revenue, attribution, and no-distill terms, not OSI open source.

Can it be used in the EU, UK, or South Korea?

No. The license excludes those territories for use of materials, derivatives, and outputs.

Is there a chat or instruct version?

Not at release. The checkpoint is pre-training only; Baidu says the post-trained ERNIE 5.5 Flash will follow without a date.