Infrastructure→Official→Together AI Blog
Together AI introduced Cache-aware Prefill–Decode Disaggregation (CPD), a new inference architecture that separates warm and cold workloads. The approach delivers up to 40% higher throughput and significantly reduces time-to-first-token for long-context LLM serving.
Why it matters: This technique addresses a key bottleneck in serving long prompts, enabling faster responses for applications like document analysis and code generation.
Jul 11, 2026
Open Source→Official→Together AI Blog
Together AI has announced CoderForge-Preview, an open dataset intended for training efficient coding agents. The dataset is designed to support open-source AI development in code generation and understanding.
Why it matters: This release offers an open resource that could support research and development of coding AI agents and foster collaboration in the field.
Jul 11, 2026
Research→Official→Together AI Blog
Together AI's research finds that leading speech models such as Whisper and Deepgram, despite near-human benchmark scores, fail to correctly transcribe street names 39% of the time. The company outlines a proposed solution to address this significant shortcoming.
Why it matters: This research exposes a major limitation in speech AI that could affect critical real-world uses like navigation and emergency response.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI has introduced Consistency Diffusion Language Models (CDLM), a post-training method that enables exact block-wise KV caching and reduces the number of refinement steps required. This approach achieves up to 14.5x latency improvements over standard diffusion language models without sacrificing output quality.
Why it matters: This development makes diffusion language models more practical for real-time applications by significantly reducing inference time while maintaining quality.
Jul 11, 2026
Products Agents→Official→Together AI Blog
Together AI has introduced Dedicated Container Inference, a production-grade orchestration service for custom AI models. The service delivers 1.4x to 2.6x faster inference compared to standard approaches.
Why it matters: This enables enterprises to deploy custom models with significantly improved performance, reducing latency and cost for AI inference at scale.
Jul 11, 2026
Research→Official→Together AI Blog
New research from Together AI finds that different large language model (LLM) families exhibit distinct default behaviors when given no specific prompt. According to the study, GPT models tend to generate code and math, Llama models favor narratives, DeepSeek often produces religious content, and Qwen outputs exam questions.
Why it matters: Understanding these inherent biases is crucial for deploying LLMs in applications where neutrality is important.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI has announced that Rime Arcana V3 Turbo and Rime Arcana V3 are now available on its platform. Users can now access these models through Together AI.
Why it matters: This expands Together AI's model offerings with new versions of the Rime Arcana series.
Jul 11, 2026
Companies Funding→Official→Together AI Blog
Together AI has appointed Alon Gavrielov as Vice President of Infrastructure Strategy. The company says this hire deepens its commitment to building reliable, efficient, and scalable infrastructure for AI-native teams.
Why it matters: This hire highlights Together AI's focus on strengthening its infrastructure to support AI-native teams.
Jul 11, 2026
Products Agents→Official→Together AI Blog
Together AI has updated its Evaluations platform to support benchmarking models from OpenAI, Anthropic, and Google alongside open-source and fine-tuned models. Users can now compare quality, cost, and performance across providers within a single platform.
Why it matters: This enables data-driven model selection by allowing direct comparison of proprietary and open-source models on the same evaluation platform.
Jul 11, 2026
Models→Official→Together AI Blog
Together AI fine-tuned the open-source GPT-OSS 120B model using Direct Preference Optimization on 5,400 preference pairs. The resulting model outperformed GPT-5.2 in human preference alignment for evaluating model outputs, while offering 15x lower cost and 14x faster inference speeds.
Why it matters: This shows that open-source models can surpass proprietary models in specific evaluation tasks with significantly reduced cost and latency.
Jul 11, 2026
Open Source→Official→Together AI Blog
Together AI has introduced DSGym, a holistic framework for evaluating and training large language model (LLM)-based data science agents. DSGym features over 90 bioinformatics tasks, 92 Kaggle competitions, and synthetic trajectory generation. Together AI reports that their 4B model achieves state-of-the-art performance among open-source models.
Why it matters: DSGym offers a comprehensive benchmark and training environment for data science agents, which could accelerate advancements in automated data analysis.
Jul 11, 2026