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Kimi K3 Model Serving

13 posts · Jul 2025 – Jul 2026

What this trail covers

Together AI now offers native deployment of elite proprietary models like Kimi K2, featuring a 1 trillion parameter scale and advanced agentic capabilities. This model competes at the GPT 5.6 Sol and Claude Fable 5 tier, excelling in EQ-Bench3, Creative Writing, SWE-bench Verified, AceBench, and LiveCodeBench v6. It utilizes a Mixture-of-Experts design with 8 experts selected from 384, trained on 15.5 trillion tokens using the MuonClip optimizer. The platform provides serverless deployment with 99.9% availability SLA, instant scaling, and continuous performance optimizations, offering competitive economics compared to closed models.

All Together AI trails
  1. Kimi K3: The Complete Developer GuideThis post details the technical architecture of Kimi K3, including Kimi Delta Attention (KDA), Attention Residuals (AttnRes), and the Stable LatentMoE framework with its supporting techniques (Quantile Balancing, Per-Head Muon, SiTU, Gated MLA). It also provides practical guidance on using Kimi K3 via the Together AI platform, including API usage with the Python SDK, configuring reasoning effort, streaming responses, handling vision input with specific limits, generating structured output with JSON schema, and implementing dynamic tool loading. The post highlights Kimi K3's capabilities in long-horizon coding and deep reasoning, positioning it as a competitor to top-tier proprietary models.
  2. Together AI announces strategic partnership with Moonshot AI to natively serve Kimi modelsThis post announces a strategic partnership with Moonshot AI, making Together AI a launch platform for their models, starting with Kimi K3. It details Kimi K3's architecture, including Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and its capabilities for long-context tasks and agentic workflows. The post highlights the availability of Kimi models across Together AI's Inference products (Serverless, Provisioned Throughput, Dedicated Inference) and the ability for developers to post-train these models. It emphasizes day-zero availability, production-ready infrastructure tuned for sparse MoE models, and a unified API for the Moonshot lineup.
  3. Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and RoutingThis post provides a detailed technical comparison of Kimi K3 and GPT-5.6 Sol on the DeepSWE benchmark, focusing on pass@1, pass@k, cost per rollout, and task coverage. It quantifies the performance differences, showing Kimi K3's advantage in pass@k and cost-effectiveness (2.8x more solved tasks per dollar) despite GPT-5.6 Sol's lead in pass@1. The analysis reveals a 0.46 correlation in task success/failure between the models, making them ideal for a cascaded routing strategy. The post demonstrates that a Kimi-first cascade escalating to Sol can achieve 85.6% accuracy, surpassing either model individually and a perfect one-shot router. It also breaks down performance by programming language and task type, and analyzes failure modes, noting Kimi K3's tendency for near misses and Sol's higher rate of breaking baseline tests. The post concludes by emphasizing how Together AI's inference stack can serve open models like Kimi K3 at production scale to enable such routing strategies.
  4. Kimi K3 vs Claude Fable 5 on DeepSWE: Cost and CodingThis post provides a detailed comparison of Kimi K3 and Claude Fable 5 on the DeepSWE benchmark, focusing on performance metrics (pass@1, pass@2, pass@4), cost per solved task, and language-specific performance. It highlights Kimi K3's competitive performance, particularly at higher pass@k values, and its significant cost advantage (2.8x more solved tasks per dollar). The post also notes the high correlation in task success and failure between the two models, suggesting limited diversity gains when pairing them. It concludes that Kimi K3 is a rational default for its balance of reach and cost, especially for retry-tolerant agent work.
  5. Kimi K2.7 Code vs Claude Fable 5: Landing pages that cost 94% lessThis post details an experiment comparing Kimi K2.7 Code against Claude Fable 5 for generating landing pages. It highlights that Kimi K2.7 Code, an open-source model, can achieve comparable quality to proprietary models like Claude Fable 5 at a significantly lower cost (94% less on average). The experiment demonstrated that providing strong visual context via a custom MCP server dramatically improved Kimi's output quality, leading to better hierarchy, typography, and composition. The post quantifies cost savings and presents a scoring rubric used to compare the generated pages, concluding that open-source models are a practical choice for such workflows when combined with effective prompting and iteration.
  7. Together AI Brings NVIDIA Nemotron 3 Nano Omni to Developers on Day 0This post announces the availability of NVIDIA Nemotron 3 Nano Omni on the Together AI platform. It highlights the model's multimodal reasoning capabilities (video, images, audio, language) and its hybrid Mamba-Transformer MoE architecture. The post emphasizes Together AI's research optimizations for high throughput and cost-efficient inference, managed infrastructure for agentic workloads, and secure APIs. It details how Nemotron 3 Nano Omni eliminates fragmented multimodal pipelines, enabling unified context for agent applications and supporting deployment on NVIDIA Hopper and Blackwell architectures.
  8. Announcing native availability of NVIDIA Nemotron 3 Nano, NVIDIA’s latest reasoning modelIntroduces native availability of NVIDIA Nemotron 3 Nano, a hybrid Mamba-Transformer + sparse MoE reasoning model with ~3B active parameters and 1M-token context. Highlights its optimization on Together AI for high throughput and cost-efficiency, making it suitable for agentic systems, coding assistants, scientific agents, tool-using planners, and enterprise context applications. Details performance, reliability, and cost-efficiency benefits of running Nemotron 3 Nano on Together AI, including an OpenAI-compatible interface for easy adoption.
  9. DeepSeek-V3.1: Hybrid Thinking Model Now Available on Together AIIntroduces DeepSeek-V3.1, a hybrid LLM with configurable modes for fast responses (non-thinking) and deep reasoning (thinking). Details performance benchmarks for both modes, highlighting improvements in complex tasks with thinking mode. Describes built-in agent support (code and search) with optimized tool-calling workflows. Mentions extended context training (32K and 128K tokens). Outlines production deployment on Together AI infrastructure, including technical specs (671B total parameters, 37B active per token, 128K context, MIT license) and infrastructure guarantees (99.9% uptime SLA, SOC 2 compliant). Provides Python SDK example for integration and links to playground and documentation.
  10. OpenAI's New Open gpt-oss Models vs o4-mini: A Real-World ComparisonThis post introduces and evaluates the gpt-oss-120B model on the Together AI platform. It details a comparative analysis against o4-mini across five practical tests: terminal snake game development, creative SVG generation, advanced instruction following, mathematical reasoning, and web-enhanced information synthesis. The results show gpt-oss-120B outperforming o4-mini in code generation and instruction following, tying in mathematical reasoning and web synthesis, and showing mixed results in creative SVG generation. The post also emphasizes the benefits of open-source models, including customization, cost-efficiency, and deployment flexibility, and highlights Together AI's optimized inference speeds and competitive pricing for these models.
  11. Announcing the Availability of OpenAI's Open Models on Together AIThis post announces the availability of OpenAI's gpt-oss-120B and gpt-oss-20B models on Together AI's infrastructure. It details their availability as serverless and dedicated endpoints, emphasizing performance, economics, and reliability (99.9% uptime SLA). The post highlights developer tooling, including fine-tuning and OpenAI-compatible APIs. It mentions optimizations with NVIDIA, FlashAttention, and custom kernels. Pricing for input/output tokens is provided, along with mentions of the Batch API for cost savings. Real-world applications and a Python inference example are included.
  12. Qwen3-Coder: The Most Capable Agentic Coding Model Now Available on Together AIIntroduces Qwen3-Coder-480B-A35B-Instruct, a 480B parameter agentic coding model with 256K native context (1M extrapolated), demonstrating frontier-level performance on SWE-bench Verified, Agentic Coding, Agentic Browser Use, and Agentic Tool Use benchmarks. Highlights its capability to handle entire codebases and complex engineering workflows like legacy system modernization, cross-system feature development, and complex debugging. Emphasizes deployment on Together AI's optimized infrastructure for massive scale, advanced training, and production readiness with 4x faster inference. Provides Python SDK example for integration and mentions availability via Interactive Playground, API Documentation, Batch API, and Fine-tuning access.
  13. Kimi K2: Leading Open-Source Model Now Available on Together AIIntroduces Kimi-K2-Instruct, a 1T parameter open-source model, to Together AI's platform. Highlights its frontier performance on benchmarks like EQ-Bench3, Creative Writing, SWE-bench Verified, AceBench, and LiveCodeBench v6, emphasizing its agentic design with native tool use and autonomous workflows. Details the model's architecture (MoE with 8/384 experts, 15.5T tokens trained, MuonClip optimizer) and its availability via serverless APIs with 99.9% availability SLA, instant scaling, and optimized economics (lower cost than comparable closed models).