Seed-Thinking-v1.5 is a reasoning model project developed by ByteDance's Seed team and open-sourced on GitHub. The repository address is https://github.com/ByteDance-Seed/Seed-Thinking-v1.5。
In terms of purpose: This model is positioned as a model with excellent reasoning ability, capable of thinking first and then answering, thereby improving performance across a wide range of benchmarks. It is mainly aimed at tasks requiring reasoning ability such as STEM and programming, while also demonstrating cross-domain generalization ability.
In terms of main capabilities: According to the official introduction, Seed-Thinking-v1.5 reaches 86.7 on AIME 2024, 55.0 on Codeforces, and 77.3 on GPQA, demonstrating excellent reasoning ability in STEM and programming. In addition to reasoning tasks, this method shows significant generalization ability across diverse domains, for example, its win rate on non-reasoning tasks is 8% higher than DeepSeek R1, indicating broader applicability. Compared with other state-of-the-art reasoning models, Seed-Thinking-v1.5 is a relatively small Mixture-of-Experts (MoE) model, with 20B activated parameters and 200B total parameters.
In terms of usage: The official materials do not provide specific installation, deployment, or invocation steps; they only state that complete technical details can be found in its technical report and note that contributing code requires creating a GitHub account.
In terms of applicable scenarios: It is suitable for reasoning tasks in fields such as mathematics, programming, science, and general knowledge, as well as applications requiring cross-domain generalization ability.
Limitations and supplementary notes: The official materials point out that the evaluation results presented come from an internal sandbox and may differ from the reported results due to inconsistent testing environments. To evaluate generalized reasoning ability, the team developed two internal benchmarks, BeyondAIME and Codeforces, both of which will be made public to support future research. Regarding the model's specific license, pricing, platform support, or detailed usage steps, the current materials do not yet provide them.
