Remember DeepSeek? Two New AI Models Say They’re Even Better

AI Powerhouses Emerge as OpenAI’s Reign Comes to an End

AI companies used to measure themselves against industry leader OpenAI. No more. Now that China’s DeepSeek has emerged as the frontrunner, it’s become the one to beat.

On Monday, DeepSeek turned the AI industry on its head, causing billions of dollars in losses on Wall Street while raising questions about how efficient some U.S. startups—and venture capital—actually are.

New AI Powerhouses Enter the Ring

Now, two new AI powerhouses have entered the ring: The Allen Institute for AI in Seattle and Alibaba in China; both claim their models are on a par with or better than DeepSeek V3.

The Allen Institute’s Tülu 3

The Allen Institute for AI, a U.S.-based research organization known for the release of a more modest vision model named Molmo, today unveiled a new version of Tülu 3, a free, open-source 405-billion parameter large language model.

“We are thrilled to announce the launch of Tülu 3 405B—the first application of fully open post-training recipes to the largest open-weight models,” the Paul Allen-funded non-profit said in a blog post. “With this release, we demonstrate the scalability and effectiveness of our post-training recipe applied at 405B parameter scale.”

For those who like comparing sizes, Meta’s latest LLM, Llama-3.3, has 70 billion parameters, and its largest model to date is Llama-3.1 405b—the same size as Tülu 3.

The model was so big that it demanded extraordinary computational resources, requiring 32 nodes with 256 GPUs running in parallel for training.

The Allen Institute hit several roadblocks while building its model. The sheer size of Tülu 3 meant the team had to split the workload across hundreds of specialized computer chips, with 240 chips handling the training process while 16 others managed real-time operations.

Even with this massive computing power, the system frequently crashed and required round-the-clock supervision to keep it running.

Tülu 3’s breakthrough centered on its novel Reinforcement Learning with Verifiable Rewards (RLVR) framework, which showed particular strength in mathematical reasoning tasks.

Each RLVR iteration took approximately 35 minutes, with inference requiring 550 seconds, weight transfer 25 seconds, and training 1,500 seconds, with the AI getting better at problem-solving with each round.

Conclusion

The triple release of DeepSeek, Qwen2.5-Max, and Tülu 3 just gave the open-source AI world its most significant boost in a while.

DeepSeek had already turned heads by building its R1 reasoning model using earlier Qwen technology for distillation, proving open-source AI could match billion-dollar tech giants at a fraction of the cost.

And now Qwen2.5-Max has upped the ante. If DeepSeek follows its established playbook—leveraging Qwen’s architecture—its next reasoning model could pack an even bigger punch.

Still, this could be a good opportunity for the Allen Institute. OpenAI is racing to launch its o3 reasoning model, which some industry analysts estimated could cost users up to $1,000 per query.

If so, Tülu 3’s arrival could be a great open-source alternative—especially for developers wary of building on Chinese technology due to security concerns or regulatory requirements.