LongCat-2.0 and Kimi K3: Open-Weight MoE Flagships Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, domestic-ASIC training, $0.30/M input) and Kimi K3 (2.8T MoE, modified MIT License, native vision, $3.00/M input) — the two largest open-weight Chinese models. Covers architecture, licensing, pricing, modalities, and deployment.
Independent third-party resource. Not affiliated with or endorsed by LongCat, Meituan, DeepSeek, or any other publisher discussed on this page.
Published: 2026-08-04
This is an independent, source-based comparison of LongCat-2.0 (Meituan) and Kimi K3 (Moonshot AI) — two of the largest open-weight Chinese models as of August 2026. It compares documented architecture, licensing, pricing, modalities, and deployment options. It does not report performance as a verdict — for publisher-reported scores, see the individual model pages linked below. Every claim in this page is sourced from the linked publisher documentation.
| Dimension | LongCat-2.0 | Kimi K3 |
|---|---|---|
| Developer | Meituan (LongCat team) | Moonshot AI (Kimi) |
| Total parameters | 1.6T (MoE) | 2.8T (MoE) |
| Active parameters | ~48B per token (33B—56B dynamic) | ~104B per token (16 of 896 experts) |
| Architecture | ScMoE + LSA sparse attention + N-gram Embedding | KDA hybrid linear attention + AttnRes + Stable LatentMoE |
| Context window | 1M tokens (native) | 1,048,576 tokens |
| Modalities | Text | Text, image, video input (native vision) |
| API pricing (per 1M tokens) | $0.30 input / $1.20 output (OpenRouter) | $3.00 input / $15.00 output |
| Cache pricing | Free (cache hits not billed) | $0.30 per M cached input |
| Cost ratio (relative) | 1× | ~10× input, ~12.5× output |
| License | MIT License (standard) | Modified MIT License |
| Training hardware | 50,000 domestic AI ASICs (no NVIDIA) | Undisclosed (NVIDIA + GPGPU inference verified) |
| Self-hosted deployment | Yes — SGLang, vLLM, Transformers | Yes — open weights, non-NVIDIA GPGPU verified |
| Reasoning mode | Dynamic (MOPD distilled, adaptive) | Always-reasoning; reasoning_effort low/high/max |
| Open-weights release | Available (HuggingFace meituan-longcat) | 2026-07-27 (HuggingFace, 1,561 GB) |
Two Scaling Philosophies
LongCat-2.0 (1.6T total, ~48B active) was built to run end-to-end on domestic AI ASICs — the publisher states the model completed full training and inference without NVIDIA GPUs, on a cluster of approximately 50,000 domestic accelerators. Its architecture (Shortcut-connected MoE, Zero-Computation Experts, LSA sparse attention, N-gram Embedding) is explicitly designed for compute-constrained, non-NVIDIA hardware.
Kimi K3 (2.8T total, ~104B active) is the largest open-weight model ever released — the first open 3T-class model. Its architecture (KDA hybrid linear attention, Attention Residuals, Stable LatentMoE with 896 experts) is aimed at scaling efficiency: the publisher reports approximately 2.5× scaling efficiency versus Kimi K2. The two models represent different trade-offs: LongCat-2.0 prioritizes hardware independence and cost; Kimi K3 prioritizes raw scale and multimodal capability.
Licensing: MIT vs Modified MIT
LongCat-2.0 is released under the standard MIT License, one of the most permissive open-source licenses — commercial use, modification, redistribution, and distillation are permitted without additional conditions.
Kimi K3 is released under a modified MIT License. The publisher states commercial use is permitted with conditions; developers should review the license file in the model repository before commercial deployment. This is a meaningful difference for organizations evaluating either model for production use.
Pricing and Cost Structure
LongCat-2.0 is substantially cheaper on a per-token basis: $0.30 per million input tokens and $1.20 per million output tokens via OpenRouter — approximately 10× cheaper on input and 12.5× cheaper on output compared to Kimi K3's $3.00 and $15.00 respectively. LongCat-2.0 also provides free cache hits, while Kimi K3 charges $0.30 per million cached input tokens (a 90% discount, not free).
Moonshot reports a 90%+ cache hit rate for coding workloads on Kimi K3, meaning most effective input cost lands near $0.30/M. For agentic workloads that repeatedly process the same context, the two models' effective pricing converges more than the headline rates suggest. Self-hosting eliminates per-token cost for both, but hardware requirements differ substantially.
Modalities and Reasoning
Kimi K3 natively supports image and video input through the MoonViT-V2 vision encoder, while LongCat-2.0 is text-only. LongCat's multimodal needs are covered by sibling models in the family (LongCat-Image, LongCat-Video, LongCat-Flash-Omni).
Kimi K3 is always-reasoning and ships with a configurable reasoning_effort parameter (low/high/max, default max). This produces thorough output but, as third-party reviewers note, can consume significant reasoning tokens even for simple tasks. LongCat-2.0 integrates reasoning through MOPD multi-teacher distillation, blending agent, reasoning, and interaction capabilities into a single model with dynamic activation rather than an explicit reasoning-effort knob.
Ecosystem and Deployment
LongCat-2.0 is distributed on HuggingFace (meituan-longcat organization) and accessible via the LongCat API (OpenAI and Anthropic-compatible endpoints) and OpenRouter. Self-hosted deployment is documented for SGLang, vLLM, and HuggingFace Transformers, plus a dedicated NPU inference path for domestic accelerators.
Kimi K3 weights (approximately 1,561 GB, MXFP4/MXFP8 quantization-aware) are available on HuggingFace and ModelScope. The publisher open-sourced supporting infrastructure — MoonEP, FlashKDA, and AgentEnv — and verified inference on non-NVIDIA GPGPUs as well as NVIDIA hardware. Both models integrate with mainstream agent frameworks via OpenAI-compatible or Anthropic-compatible APIs.
This comparison is based on publicly available publisher documentation accessed on 2026-08-04. LongCat-2.0 specifications are from the publisher blog, GitHub repository, and OpenRouter listing. Kimi K3 specifications are from the official Moonshot AI blog, technical report (arXiv:2607.24653), ModelScope model card, and official API pricing page.
This is a feature-level comparison, not a performance evaluation. This site has not independently tested either model. Benchmark scores published by either vendor have not been independently verified. For publisher-reported scores, see the individual model pages and benchmark index.
Pricing is current as of the access date and may change. Kimi K3 uses a modified MIT license; review the license file before commercial use. Cache behavior, rate limits, and feature availability are vendor-specific and may differ between API tiers.
Related pages
- LongCat-2.0 model profile
Full technical brief covering architecture, training, benchmarks, and deployment options.
- LongCat-Flash-Chat model profile
The 560B-parameter sibling model with 256K context, for mid-tier workloads.
- Qwen3.8-Max release coverage
Another 2.4T-parameter open-weight flagship released August 3, 2026.
- LongCat-2.0 Publisher-Reported Benchmark Scores
The benchmark data cited in this comparison with source attribution.
Sources
- LongCat-2.0 Publisher Blog Post
Publisher documentationAccessed 2026-08-04
Publisher announcement detailing LongCat-2.0 architecture (ScMoE, LSA, N-gram Embedding, MOPD), 1.6T parameters, domestic-ASIC training, and MIT License.
- LongCat-2.0 GitHub Repository
Primary sourceAccessed 2026-08-04
Model code, weights, MIT License terms, and deployment instructions.
- Kimi K3: Open Frontier Intelligence — Official Blog
Publisher documentationPublished 2026-07-16Accessed 2026-08-04
Moonshot AI announcement: 2.8T parameters, KDA + AttnRes architecture, native vision, 1M context, open-weights release on 2026-07-27.
- Kimi K3 Technical Report (arXiv:2607.24653)
Primary sourcePublished 2026-07-27Accessed 2026-08-04
Peer-style technical report covering KDA, AttnRes, Stable LatentMoE, MoonEP infrastructure, and evaluation suite.
- Kimi K3 API Pricing
Publisher documentationAccessed 2026-08-04
Official pricing: $0.30/M cached input, $3.00/M uncached input, $15.00/M output. 1,048,576-token context window.
- Kimi-K3 Model Card (ModelScope)
Primary sourceAccessed 2026-08-04
Architecture details: 93 layers, 69 KDA + 24 Gated MLA attention layers, 896 experts with 16 active per token, MoonViT-V2 vision encoder, MXFP4 weights.
Independent third-party disclosure
This page is published by an independent third-party site. It is not affiliated with, endorsed by, sponsored by, or operated by Meituan, LongCat, or any of their affiliates. The content summarizes publicly-available primary documentation and does not represent the views of any referenced organization.
Last reviewed: 2026-08-04