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2026-09-02
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GLM-5.3 vs. GLM-5.3 Flash on DeepSWE: Cost, Coding, and Routing
We ran 900 DeepSWE rollouts on GLM-5.3 and GLM-5.3 Flash. Flash gives up 5.6 points of pass@1 at 17x lower cost, and only 2.6 points at pass@4.
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GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing
We ran 904 DeepSWE rollouts on GLM-5.3 and Claude Fable 5. A tie on pass@1, but GLM-5.3 wins pass@4 and costs 5.4x less: \$3.99 per rollout vs. \$21.63.
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GLM-5.3 vs. GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
We ran 904 DeepSWE rollouts on GLM-5.3 and GPT-5.6 Sol. Sol leads pass@1 by 3.7 points; GLM-5.3 wins pass@4 at half the cost, and a GLM-first cascade hits 85.9%.
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DeepSeek V4 Pro 0813 vs Claude Fable 5 on DeepSWE: Cost, Coding, and Routing
We ran 904 DeepSWE rollouts on DeepSeek V4 Pro 0813 and Claude Fable 5. Fable leads pass@1 at 90x the cost; Pro wins pass@4, and a Pro-first cascade hits 82.7%.
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DeepSeek V4 Pro 0813 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
We ran 904 DeepSWE rollouts on DeepSeek V4 Pro 0813 and GPT-5.6 Sol. Sol leads pass@1 by 10 points at 35x the cost; Pro wins pass@4, and a Pro-first cascade hits 83.0%.
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A/B test models in production
Shadow traffic proves a candidate is operationally sound. It can't tell you if users like it better. Run the split at the endpoint instead of in your app code.
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Capacity without conflict: A guide to multi-tenant GPU cluster design for AI-native teams
Learn how AI-native companies design multi-tenant GPU clusters that pool capacity without sacrificing team isolation — and how Together AI makes it work in practice.
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Accelerate RL rollouts by up to 50% with distribution-aware speculative decoding
Rollout is the silent bottleneck in RL post-training. DAS fixes it with adaptive speculative decoding — up to 50% faster, zero degradation in reward quality.
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Together AI Brings NVIDIA Nemotron 3 Nano Omni to Developers on Day 0
NVIDIA Nemotron 3 Nano Omni is now on Together AI: a single open model that reasons across video, images, audio, and text, built for agentic workloads at scale.
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DeepSeek-V4 Pro now available on Together AI
DeepSeek-V4 Pro is now available on Together AI with 512K context, controllable reasoning modes, and cached-input pricing for long-context reasoning workloads like code agents, document intelligence, and research synthesis.
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Announcing Together AI and Adaption Partnership
Together AI and Adaption partner to bring Together Fine-Tuning natively into Adaptive Data, helping teams optimize datasets, run fine-tuning, evaluate results, and deploy stronger open models.
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From 732 bytes to nowhere: shutting down Copy Fail in production
How Together AI responded to the Copy Fail Linux kernel bug (CVE-2026-31431): disabling the affected crypto interface fleet wide and patching safely.
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Foundational research powering efficient inference at scale
As AI moves from research to production, the challenge for AI-native teams shifts from building models to running them — efficiently, reliably, and at scale.
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Deploy and inference any model from HuggingFace
Learn how to deploy any Hugging Face model in one session using Goose and Together's Dedicated Container Inference. Skip the setup complexity — one prompt gets your model running in a production-grade GPU environment on release day.
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Serving DeepSeek-V4: why million-token context is an inference systems problem
DeepSeek-V4 makes million-token context a serving-systems problem. Together AI explores the inference work behind V4 on NVIDIA HGX B200, including compressed KV layouts, prefix caching, kernel maturity, and endpoint profiles for long-context workloads.
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Introducing voice finder — a new tool to quickly find the right voice for your app from over 600+ voices
Voice finder helps developers search, match, filter, and audition 600+ voices across Together AI TTS models using natural-language prompts or uploaded audio samples.
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Violin: An open-source video translation skill that breaks language barriers
Violin is an open-source AI video translation tool that combines speech recognition, LLM translation, and text-to-speech to make video content accessible across languages.
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Together AI and Pearl Research Labs Team Up to Reduce the Cost of AI Inference
Together AI partners with Pearl Research Labs to launch a discounted Pearl-powered inference endpoint for Gemma-4-31B-it-pearl, using Proof of Useful Work to turn AI workloads into crypto emissions.
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Benchmarking inference at scale: coding agents
Real-world inference benchmarks for coding agents: 31% more TPS than TensorRT-LLM, 2× better TTFT at saturation, and 76% lower cost than Claude Opus 4.6.
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How Together AI built the world’s fastest speech-to-text stack
Together AI built the fastest speech-to-text stack on Artificial Analysis by treating ASR as a full-path systems problem, not just a GPU inference problem.
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