Baidu’s open-source (Apache 2.0) multimodal foundation model: heterogeneous MoE, variants up to 424B-A47B (VL), 128K context; powers the Ernie Bot assistant.
Context window
128K
tokens
Parameters
do 424B (47B aktywne) — VL-424B-A47B; warianty 300B-A47B, 21B-A3B, 28B-A3B, 0.3B
parameters
Release date
16 March 2025
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud
Overview
Applications
Access & deployment
DownloadAPIHosted
LocalCloud
Weights: Open source
Key parameters
📏 Context: 128K
🧩 Parameters: do 424B (47B aktywne) — VL-424B-A47B; warianty 300B-A47B, 21B-A3B, 28B-A3B, 0.3B
✓ Tools · ✓ Fine-tuning
📥 Input: text, image, video
Technical specification
Context window
128K
tokens
Parameters
do 424B (47B aktywne) — VL-424B-A47B; warianty 300B-A47B, 21B-A3B, 28B-A3B, 0.3B
parameters
License
Apache 2.0
Hardware requirements
An open-weights family (Apache 2.0) on Hugging Face and AI Studio. The largest MoE variants (up to 424B total parameters) require a large-VRAM GPU cluster; smaller variants (21B-A3B, 0.3B) and quantized versions (FP8, W4A8C8) enable lighter deployments. Also available via Baidu Cloud (Qianfan).
Features:✓ Tool use✓ Fine-tuning
Modalities
⬇ Input
textimagevideo
⬆ Output
text
Capabilities and applications
Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Image understanding
Analysing and interpreting the content of images.
Category: vision
Video understanding
The model's ability to analyse and interpret video content — recognising actions, motion, events and relationships between objects over time.
Category: video
Multimodal understanding
Category: multimodal
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Natural conversation
Conducting a conversation with a tone close to human: a warmer voice, empathy in emotional responses, humour, and avoiding the stiff 'AI assistant jargon'. Introduced as a deliberate improvement in GPT-5.1 Instant.
Category: language
Technical architecture
Core Architecture
Training Techniques
