Tongyi DeepResearch 30B A3B
Tongyi DeepResearch 30B A3B • LLM
Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks...
Specifications
Context Window
131K tokens
Input Price/1M
$0.09
Output Price/1M
$0.45
Parameters
—
Max Output
131K tokens
Information
- Tool Calling
- ✅ Supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: Tongyi DeepResearch 30B A3B
What is Tongyi DeepResearch 30B A3B?
Tongyi DeepResearch 30B A3B is an AI model developed by Tongyi DeepResearch 30B A3B, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 131K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
Tongyi DeepResearch 30B A3B is usage-based, priced at $0.09/1M input tokens and $0.45/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Benchmarks & Performance
We don't have detailed benchmark results for Tongyi DeepResearch 30B A3B yet. Benchmarks are updated weekly as new data becomes available from sources like Artificial Analysis, LM Arena, and LiveBench.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
Tongyi DeepResearch 30B A3B is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, complex reasoning, math problem solving, and logical analysis, text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, Tongyi DeepResearch 30B A3B competes directly with similarly capable models. Tongyi DeepResearch 30B A3B competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is Tongyi DeepResearch 30B A3B?
Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks...
How much does Tongyi DeepResearch 30B A3B cost?
Tongyi DeepResearch 30B A3B costs $0.09/1M input tokens and $0.45/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does Tongyi DeepResearch 30B A3B compare with other models?
We don't have detailed benchmarks for Tongyi DeepResearch 30B A3B yet. Check the main benchmark page to compare available models.
Is Tongyi DeepResearch 30B A3B open source?
Yes, Tongyi DeepResearch 30B A3B is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
What is Tongyi DeepResearch 30B A3B best for?
Tongyi DeepResearch 30B A3B excels at complex reasoning and problem solving. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: May 24, 2026 • View methodology →