AI Coding Live News

On November 4 Together AI launched Together Compiler (TKCC), which pairs a swarm of AI agents with a persistent inference engine to keep rewriting inference kernels for real traffic, models and hardware, as part of what it calls Polymorphic Inference. The company says that within 24 hours it reached up to 2.6x higher TPS on models such as Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, up to 50% faster on customer workloads, and it already serves trillions of tokens a day in production.

Together AI (@togethercompute) says you can try GLM-5.3 and GLM-5.3 Flash free on Together Chat (chat.together.ai)—no API setup, just pick a model and prompt—served on secure North American infrastructure for quick hands-on access to Zhipu's latest coding/agent models.

Together AI (@togethercompute) announces Equinix Inference Exchange with Equinix and NVIDIA: its open-model platform is live across Equinix’s global data centers, productizing both which model enterprises run and where it runs — arguing model choice and performance are not a trade-off.

Together ran about 900 DeepSWE rollouts of GLM-5.3 vs GLM-5.3 Flash: 69.0% pass@1 at ~$3.99 per task versus 63.4% at ~$0.24, a 17x cost gap. Distillation mostly removed first-try reliability, not coverage—the pass@4 gap shrinks to 2.6 points. A Flash-then-escalate cascade reached 80.9% at about $1.70 per task, beating the full model alone at less than half the price. Coding agents should default to Flash and promote on test failure, but Flash broke an already-passing baseline in 6.9% of rollouts versus 4.4%, so diffs still need a regression gate.

Together AI is cutting Dedicated Inference on H100s from $5.49/hr to $3.99/hr for September, with the lower rate applied automatically to new and existing deployments. Supported options include Gemma 4, Qwen3/3.5, gpt-oss, Llama, Nemotron 3.5 Lightning, or a bring-your-own LoRA. For teams that pin coding agents or fine-tunes to dedicated GPUs, the change is a straight cut in 24/7 serving cost without a migration.

Together AI said GLM-5.3 now beats GPT-5.6 Sol and Claude Fable 5 on agentic benchmarks, with GLM-5.3 Flash close behind. Zhipu kept the GLM-5.2 base and scaled post-training—more long-horizon environments, more diverse tasks, and more RL compute—rather than training a new foundation model. For coding agents, that is a claim that post-training stack height can overtake closed flagships, and that the cheaper open-weight default is already on Together’s API.

Together AI said it signed one of the largest AI infrastructure deals for open source: a 250MW data center with HUMAIN in Saudi Arabia, carrying more than $5 billion in annualized revenue. The company framed the pact as open-source compute catching up with closed models that have long been bankrolled by hyperscalers, and argued power—not chips alone—is the binding constraint. For teams that fine-tune and serve open weights, the deal points to a step-change in dedicated capacity without each lab raising its own multi-billion-dollar balance sheet.

Together AI added GLM-5.3 Flash to its cloud inference catalog, offering native multimodal understanding, 1M context with hybrid attention, and double the DeepSWE throughput per dollar compared to proprietary peers.

Together AI launched hosted Day-1 inference for GLM-5.3 Flash (320B-A18B MoE, 1M context), nearly matching Luna on DeepSWE benchmarks while completing more than twice the work per dollar.

Together AI launched hosted serverless API serving for GLM-5.3 Flash, highlighting its 320B-A18B multimodal MoE architecture, 1M context, and high DeepSWE efficiency compared to proprietary frontier models.