What Is ERNIE 5.5 Flash? Specs, Pricing & Benchmarks

What Is ERNIE 5.5 Flash? The 280B MoE Activating 14B Parameters

ERNIE 5.5 Flash Preview is a sparse Mixture-of-Experts (MoE) foundation model developed by Baidu that delivers high-throughput reasoning, software coding, and agent-driven workflows across a 1-million-token context window. Officially announced on November 11, 2026, the model packages 280 billion total parameters while activating only 14 billion parameters per token. It serves as the primary instruction-tuned checkpoint preceding the permanent, general-availability release of ERNIE 5.5 Flash, offering cloud API inference speeds above 200 tokens per second ahead of an Apache 2.0 open-weights release scheduled for November 25, 2026.

Key Takeaways

• Sparse MoE Architecture: The model uses 280 billion total parameters across 43 transformer layers, routing each token through specialized experts so only 14 billion parameters activate at inference.
• 1M-Token Context Window: Designed for repository-scale programming and document ingestion, supporting up to 1,048,576 context tokens.
• High Throughput at Low Cost: Hosted endpoints deliver generation speeds above 200 tokens per second, priced at $0.28 per 1M input tokens and $1.13 per 1M output tokens ($0.028 per 1M cached tokens).
• Provisional #2 Open Model in WebDev: Debuted on the Code Arena WebDev leaderboard at 1668 points, edging past DeepSeek-V4.1-Flash (1664 points).
• Upcoming Apache 2.0 Weights: Currently accessible through hosted APIs on Baidu Qianfan and cloud routers, with weights listed for release on Hugging Face by November 25, 2026.

What Is ERNIE 5.5 Flash?

ERNIE 5.5 Flash represents Baidu’s push into high-efficiency, long-context language modeling. In Baidu's release terminology, the system consists of three developmental milestones: ERNIE 5.5 Flash Base, ERNIE 5.5 Flash Preview, and the eventual final ERNIE 5.5 Flash production model.

The foundation layer, ERNIE 5.5 Flash Base, was published on November 5, 2026. It was pre-trained from scratch on 33.5 trillion tokens using a training context of up to 128,000 tokens before expanding to a 1M-token inference context. That base checkpoint contained no reinforcement learning or mid-training instruction tuning.

On November 11, 2026, Baidu released ERNIE 5.5 Flash Preview as the first post-trained checkpoint of that base foundation. Post-training equipped the raw weights with reasoning behaviors, tool execution capabilities, multi-turn agent coordination, and specialized code synthesis pipelines.

ERNIE 5.5 Flash Preview pairs a 280-billion-parameter MoE capacity with a 14-billion-parameter active execution footprint to sustain sub-dollar long-context reasoning. The preview tag reflects its status as an intermediate candidate undergoing live evaluation. Baidu is collecting telemetry and real-world developer feedback from early hosted API deployments before freezing the final weights.

ERNIE 5.5 Flash at a Glance

Specification	Detail	Source / Status
Developer	Baidu	Confirmed
Model Type	Sparse Mixture-of-Experts (MoE)	Confirmed
Total Parameters	280 billion (280B)	Confirmed
Active Parameters	14 billion (14B) per token	Confirmed
Layers & Dimensions	43 Transformer layers (1 dense, 42 MoE), 64 heads, hidden size 4096	Confirmed
Input Modalities	Text only	Confirmed
Output Modalities	Text only (supports tool calls and structured JSON)	Confirmed
Context Window	1,000,000 tokens (1,048,576 tokens)	Confirmed
Generation Speed	>200 tokens/second per user (Qianfan hosted)	Confirmed
API Pricing	$0.28 / 1M input, $1.13 / 1M output, $0.028 / 1M cached input	Confirmed
License	Proprietary hosted API; Apache 2.0 open weights planned	Confirmed timeline
Availability Date	API launched Nov 10–11, 2026; Weights due Nov 25, 2026	Confirmed

How ERNIE 5.5 Flash Works: Sparse MoE and Hybrid Attention

The core engineering objective behind ERNIE 5.5 Flash is decoupling parameter capacity from compute cost. Activating all 280 billion parameters for every forward pass would demand massive hardware clusters and create unacceptable latency for real-time applications. To solve this, Baidu implemented two primary structural optimizations: a sparse mixture-of-experts backbone and Latent-Gated Sparse Attention.

Sparse MoE Backbone

The model consists of 43 Transformer layers configured with a hidden dimension of 4096 and 64 attention heads. Rather than making every layer an MoE block, the architecture deploys one initial dense layer followed by 42 sparse MoE layers.

The dense layer ensures that baseline lexical representation and syntactic structure are uniformly processed for every token. Across the subsequent 42 MoE layers, dynamic gating routers evaluate incoming tokens and distribute them across specialized sub-networks. By routing each token to a subset of experts, the system only activates 14 billion parameters during inference. This represents an active ratio of roughly 5% of its total parameter count.

This parameter-activation ratio mirrors trends seen in models like Inkling 2 Small, balancing substantial pre-trained world knowledge with minimal floating-point operations per generated token.

Latent-Gated Sparse Attention

Long contexts create severe bottlenecks in key-value (KV) cache memory. To process 1 million tokens without overwhelming GPU memory, ERNIE 5.5 Flash introduces Latent-Gated Sparse Attention, which blends Multi-Head Latent Attention (MLA) with Heavily Compressed Attention (HCA).

Instead of maintaining full, uncompressed KV caches across every layer, the architecture adopts a 1-in-7 ratio:

• Full-Context Layers (MLA): Only one out of every seven transformer layers retains uncompressed, token-level access across the entire context window using Multi-Head Latent Attention.
• Compressed Layers (HCA): The remaining six layers employ Heavily Compressed Attention. In these layers, the model compresses every 128 tokens into a single cached entry. It then attends across these compressed summary entries alongside a short local sliding window of recent, uncompressed tokens.
• Layer-Specific Head Dimensions: Attention head dimensions vary by layer cost. Layers with computationally cheaper attention mechanisms are assigned wider heads, maximizing feature extraction where memory traffic is lowest.

This hybrid attention design drastically shrinks KV cache memory footprints during long-context generation, enabling sustained speeds above 200 tokens per second on commercial hardware.

What You Can Do With ERNIE 5.5 Flash

Baidu positioned ERNIE 5.5 Flash Preview around complex programming, long-horizon tool execution, and parametric 3D design. Because the model is strictly text-based, it performs spatial and graphical operations through procedural code generation, CLI orchestration, and iterative self-debugging.

Long-Horizon Game Development in Unreal Engine 5.8

In official launch demonstrations reported by 0xLogicrw, Baidu tasked ERNIE 5.5 Flash Preview with developing Yunxiu Xing, a third-person 3D action game built in Unreal Engine 5.8.

Operating within an automated agent harness, the model worked through an 11-hour autonomous coding task. The system could not directly inspect rendered game frames. When playtest feedback flagged runtime bugs—such as floating player characters, reversed movement directions, and camera clipping—the model converted qualitative feedback into automated C++ unit tests. It integrated mismatched environmental assets from multiple Unreal Engine releases, wrote all gameplay logic in C++, and iterated until its test harness passed.

Procedural 3D Modeling via Blender and CAD

The post-training pipeline included domain adaptation on spatial reasoning and professional 3D APIs. Demonstrated workflows highlighted:

• Blender Python Automation: Two autonomous agents running ERNIE 5.5 Flash Preview collaborated to construct a complete airport scene. One agent pulled vector layout data from mapping APIs to parametrically generate the airfield and terminal. The second agent proceduralized an airliner by lofting fuselage cross-sections and populating the interior decks. A final script coordinated lighting, scene scale, and camera paths.
• Parametric CAD Design: The model produced valid, editable Python scripts for FreeCAD, generating complex mechanical assemblies including a W16 engine and a lunar rover.
• Workflow Tool Orchestration: Demonstrations verified the model's ability to trigger external tools, invoking generative video pipelines like Seedance through programmatic API calls.

How ERNIE 5.5 Flash Compares

Early benchmark results place ERNIE 5.5 Flash Preview among the most capable lightweight open-weight models, though performance varies sharply between structured office automation and raw command-line engineering.

Leaderboards and Benchmarks

According to provisional tracking published by Arena.ai, ERNIE 5.5 Flash Preview entered the Code Arena: WebDev leaderboard at 1668 points:

Source: @arena

Achieving 1668 points on the Code Arena WebDev leaderboard, ERNIE 5.5 Flash Preview edges past DeepSeek-V4.1-Flash's 1664 points while matching Qwen3.8-Max at 1670 points.

Model	WebDev Arena Score	Active Params	Context Window	Input / Output Price (per 1M)
Qwen3.8-Max	1670	Proprietary Dense	128K	~$1.60 / $6.40
ERNIE 5.5 Flash Preview	1668	14B (280B total)	1,000,000	$0.28 / $1.13
DeepSeek-V4.1-Flash	1664	14B (approx.)	128K	~$0.14 / $0.28
Kimi K3 (Max)	1658	Proprietary MoE	200K	~$0.60 / $2.40

In developer workflows comparing open-weight models, engineers frequently benchmark outputs against DeepSeek-V4.1-Flash to evaluate whether sparse MoE routing compromises code quality during multi-file refactoring.

Office Tasks vs. Terminal-Bench Deficit

Baidu's internal evaluation charts, analyzed in public critiques by berryxia, reveal divergent task strengths:

• AutomationBench: Scored 61.9, outperforming Claude Opus 5.5 (58.0).
• SpreadsheetBench V2: Scored 36.4, placing slightly above Claude Opus 5.5 (35.1).
• Terminal-Bench 4.0: Reached 46.5 points. While this comfortably leads domestic comparisons like Hy4 and early DeepSeek Flash checkpoints, it trails frontier reasoning models such as Claude Opus 5.5 (63.6) and GPT-6.1 Sol (56.1).

While Baidu demonstrates strong synthetic gains in office automation and 3D procedural workflows, independent replication is still required to verify whether its coding agent capabilities match frontier proprietary models.

Availability: How to Access ERNIE 5.5 Flash

ERNIE 5.5 Flash Preview is accessible through cloud APIs, with local deployment options opening soon:

1. Baidu Qianfan Platform

The primary production API is hosted on Baidu’s Qianfan cloud ecosystem. The platform serves full 1M-token requests with sustained generation speeds above 200 tokens per second. Standard commercial pricing applies, with regional promotional discounts available to registered enterprise accounts.

2. Multi-Provider Cloud Routers

Developers outside mainland China can query the model via OpenRouter, where endpoints were listed live on November 10, 2026, as confirmed by AI Tracker Bot. The preview checkpoint carries standard list rates of $0.28 per 1M input tokens and $1.13 per 1M output tokens, with official promotional free access windows scheduled through November 30, 2026.

3. Open Weights and Local Inference

Model weights are not yet publicly downloadable. Official announcements from Baidu ERNIE confirm that the model weights will be released under the Apache 2.0 license by November 25, 2026, on Hugging Face.

Community developers on r/LocalLLaMA have already submitted draft integration PRs to inference engines like llama.cpp using the existing ERNIE-5.5-Flash-Base-BF16 weights. Running a 4-bit quantization (such as IQ4_XS) of the 280B MoE will require a minimum system configuration of 32GB VRAM paired with 128GB system RAM to handle CPU-offloaded expert routing.

What We Don't Know Yet

As an early preview release, several practical implementation details remain unconfirmed:

• Third-Party Benchmark Replication: Baidu's office automation and long-context scores have not yet been corroborated by independent testing laboratories. The WebDev Arena score of 1668 remains an early auto-evaluation figure that may shift as human-preference battle votes accumulate.
• Weight Delivery and Licensing Terms: While Baidu stated an Apache 2.0 release date of November 25, 2026, developers are watching closely to see whether the weights will carry any geographic usage limitations or commercial revenue thresholds similar to prior base releases.
• Deficiencies of the Preview Checkpoint: Technical notes from Baidu acknowledge that the Preview release is an intermediate post-training build. The degree to which undertraining affects edge-case logic or complex agentic error recovery will become clear once engineers run local fine-tunes.

Frequently Asked Questions

What is ERNIE 5.5 Flash Preview?

ERNIE 5.5 Flash Preview is Baidu's first post-trained checkpoint derived from ERNIE 5.5 Flash Base, featuring 280 billion total parameters, 14 billion active parameters per token, and a 1-million-token context window. It is built to provide rapid, affordable inference for software development, agentic workflows, and document reasoning.

Is ERNIE 5.5 Flash open source?

ERNIE 5.5 Flash Preview is currently available via cloud APIs, with Baidu scheduled to open-source the model weights under the Apache 2.0 license by November 25, 2026. Once published on Hugging Face, developers will be able to self-host and modify the weights locally.

How much does ERNIE 5.5 Flash cost to run via API?

ERNIE 5.5 Flash Preview is priced at $0.28 per 1 million input tokens and $1.13 per 1 million output tokens on supported cloud routers, with cached prompt inputs billed at $0.028 per 1 million tokens. Promotional zero-cost access tiers are active on select platforms through late November 2026.

Does ERNIE 5.5 Flash support multimodal inputs like vision or audio?

No, ERNIE 5.5 Flash Preview is a text-only language model that takes text in and returns text out, relying on external rendering tools or code execution rather than native image input. When performing 3D design tasks in Blender or Unreal Engine, it operates by writing, executing, and debugging Python and C++ scripts.

How does ERNIE 5.5 Flash compare to DeepSeek-V4.1-Flash?

On the Code Arena WebDev leaderboard, ERNIE 5.5 Flash Preview scored 1668 points, placing it slightly ahead of DeepSeek-V4.1-Flash at 1664 points while sharing a similar sparse mixture-of-experts design philosophy. Both models activate roughly 14 billion parameters per token to achieve competitive output quality at low latency.

What inference speed does ERNIE 5.5 Flash achieve?

Baidu reports that ERNIE 5.5 Flash Preview sustains generation throughput above 200 tokens per second per user on its first-party Qianfan infrastructure. Real-world third-party router latencies may vary based on geographic queuing and system prompt lengths.

What to Watch Next

Track the official arrival of the model weights on Hugging Face on November 25, 2026, to verify whether the repository ships under a clean Apache 2.0 license. Watch for the merging of community quantization pull requests in llama.cpp to confirm local consumer-hardware viability. Finally, observe whether ERNIE 5.5 Flash maintains its provisional #2 position on the WebDev Arena as verified human-eval battles replace early auto-evaluation metrics.

Try related models on Kie.ai: DeepSeek-V4.1-Flash, Claude Opus 5.5 and Gemini 4 Carbon.