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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 and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models. The model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.
Price/1M
$0.18
287th cheapest
42% below median
Top 43%
Context Window
131K
145th largest
Top 63%
Input
$0.09
per 1M tokens
Output
$0.45
per 1M tokens
Blended
$0.18
per 1M tokens
Cheaper than 57% of models. Median price is $0.31/1M tokens.
Daily
$0.18
Monthly
$5.40
Context Window
131K
tokens
Larger than 37% of models
Max Output
131K
tokens
100% of context
Context Window Comparison