Together AI expands fine-tuning service with more models, live metrics, and finer controls

Support for the latest open-weight models, live experiment tracking, finer controls over training and data processing, and lower prices on selected models.

Artem Chumachenko, Egor Timofeev, Jasmine Li, Ruslan Khaidurov, Nikita Smetanin, Sergei Vorobyov, Arseniy Belorukov, Denis Fedorenko, Alex Moldovan, Shadi Mokhtar, Gleb Vazhenin, Sonny Khan, Adee Feiner, Jen Wu, Max Ryabinin

40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production...

Turning an open-weight model into a high-performing model for your task takes a sequence of well-measured experiments. Teams need to understand what the model will train on, follow how each run is progressing, adjust the training recipe, and identify which checkpoint performs best.

Today, we're expanding Together Fine-Tuning across that workflow. This release adds support for the latest open-weight models, live experiment tracking, finer controls over training and data processing, and lower prices on selected models.

You can also inspect and validate a dataset before training, compare runs while they are in flight, and stop when validation loss plateaus.

New open-weight models are arriving at an unprecedented pace, with nearly every release raising the bar for quality, efficiency, or both. Teams want to bring these advances into production quickly, without spending weeks on the technical challenges of each new architecture.

For tasks that need frontier performance, you can turn to the largest and strongest models such as GLM-5.3 and Kimi K2.7. For example, GLM-5.3 scores 88.2 on Terminal-Bench 2.1, within one point of the leading proprietary models. Other teams prioritize performance and costs, choosing models such as Qwen 3.8-27B or Gemma 4. For local and on-device applications, the Qwen 3.5 family offers options ranging from 0.8B to 9B parameters.