Knowledge catalogue
Highlights: 👉 80.2% SWE-Bench Verified and 89.6% LiveCodeBench v6 👉 Agent Swarm executes up to 4,000 coordinated steps 👉 Native text, image, and video input with 79.4% MMMU-Pro 👉 Production-ready on t
Introducing Kimi K2.6 from @Kimi_Moonshot, a multimodal agentic model with Agent Swarm scaling to 300 sub-agents and long-horizon coding stability. AI natives can now use Kimi K2.6 on Together AI and
Kimi K2.6 is now available for use on Together AI's cloud platform, which offers AI model deployment and inference services. This announcement indicates the model has been added to Together AI's roste
Our researchers are heading to ICLR with new work: model efficiency, long-context reasoning, next-gen attention and decoding, and more. Check out what we've been building 👇🏼 #TogetherResearch #AINativ
We’re back 🔥. Thrilled to be named once again to the @Forbes AI 50. The AI Native Cloud, built for the full AI lifecycle: fast inference, open models, fine-tuning at scale. https://www.forbes.com/list
@lmsysorg @sgl_project @vllm_project @CoreWeave @nebiusai @nscale @togethercompute @Togethercompute enables AI labs and enterprises to run frontier models and agents on the NVIDIA Blackwell platform—g
The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, not by adding parameters. Bigger-model quality at smaller-mode
Training code and models are live on Hugging Face. Dan Fu (Together AI's VP of Kernels) led the work. Together AI provided compute. Blog: https://www.together.ai/blog/parcae Paper: https://arxiv.org/a
What if you could get 1.3B Transformer quality from a 770M model? That's not a compression result. It's a different architecture. Parcae, from @realDanFu (Together AI's VP of Kernels) and his lab at U
EinsteinArena is a platform where AI agents collaborate on open science problems — submitting solutions, posting in discussion threads, building on each other's constructions in real time. Agents just
EinsteinArena is an open-source benchmarking platform developed by Together AI designed to evaluate and rank AI models, with a live public leaderboard tracking model performance. The project welcomes
This isn't the only result. As of April 11, agents have set 11 new SOTA results on open problems including: → Erdős minimum overlap problem → Second autocorrelation inequality → Tammes problem (n=50)
MiniMax M2.7 is now available on Together AI, offering a powerful open-source model trained using a self-directed reinforcement learning loop. This training methodology enabled the model to achieve th
On software engineering: → 56.22% SWE-Pro, matching GPT-5.3-Codex → 55.6% VIBE-Pro — full-stack project delivery → SWE Multilingual: 76.5 For agentic work: → Multi-agent collaboration built into the m
MiniMax-M2 is a large-scale mixture-of-experts (MoE) language model available for inference on Together AI's platform, accessible via both serverless and dedicated infrastructure options. The model ca
Together AI maintains a public events page at [together.ai/events](https://www.together.ai/events) listing its past and upcoming appearances, spanning major industry conferences such as NVIDIA GTC,...
Together Computer (Together AI) participated in HumanX 2026, a major AI conference held April 6–9 in San Francisco, where they hosted activations including custom comics, branded hats, and a happy ...
Google's Gemma 4 31B is a dense multimodal model from Google DeepMind now available on Together AI's serverless infrastructure via the endpoint `google/gemma-4-31B-it`. It features a 256K context ...
Highlights: 👉 Configurable thinking mode for step-by-step reasoning 👉 Multimodal understanding with text and image input, including document parsing and OCR 👉 Native function calling with structured t
Introducing Gemma 4 31B from @GoogleDeepMind on Together AI. AI natives can now use Gemma 4 31B on Together and benefit from reliable inference for multimodal reasoning, tool use, and agentic workflow
GLM-5.1 is Z.ai's post-training upgrade to GLM-5, now available on Together AI, delivering a 28% coding performance improvement through a refined reinforcement learning pipeline while retaining the...
Highlights: 👉 28% coding improvement over GLM-5 with refined RL post-training 👉 Better long-horizon execution across hundreds of rounds and thousands of tool calls 👉 Thinking mode, tool calling, and s
Introducing GLM-5.1 from @Zai_org on Together AI. AI natives can now use GLM-5.1 on Together and benefit from reliable inference for production-scale agentic engineering and long-horizon coding workfl