Kimi K2.6 vs LongCat-2.5
Moonshot AI · China | Meituan · China · Updated June 2026
Quick verdict
Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents). Pick LongCat-2.5 for native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token or limited-time api price of $0.30/$1.20 per million tokens (cached input $0.006). Choose Kimi K2.6 if you need self-hosting or data privacy; LongCat-2.5 if you want a managed API.
Kimi K2.6 (Moonshot AI) and LongCat-2.5 (Meituan) are two of the models people most often weigh against each other in 2026. Kimi K2.6 is moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. LongCat-2.5 is meituan's November 2, 2026 natively multimodal successor to LongCat-2.0 - 1.6T parameters and 1M context at $0.30/$1.20, with open weights planned but undated. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: LongCat-2.5 is about 3.2× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.95/$4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: LongCat-2.5 holds 3.8× more — 1M tokens (~1,500 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: LongCat-2.5 is the newer model by about 7 months (released November 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Kimi K2.6 | LongCat-2.5 |
|---|---|---|
| Provider | Moonshot AI (China) | Meituan (China) |
| Released | April 20, 2026 | November 2, 2026 |
| Context window | 256K (~393 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $0.95/$4 per 1M tokens | $0.3/$1.2 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, video, code | text, image, code |
| SWE-Bench Verified | 80.2% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open-weight agentic coding and long-horizon tasks
Kimi K2.6
Open weights make this possible at all — LongCat-2.5 is API-only, so it cannot leave the vendor's servers.
Multi-agent swarms (scales to ~300 sub-agents)
Kimi K2.6
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host — and its weights are open while LongCat-2.5 is API-only.
Self-hosting and data-residency control
Kimi K2.6
Kimi K2.6 lists self-hosting and data-residency control among its strengths; LongCat-2.5 does not.
Native multimodal upgrade to LongCat-2.0 - 1.6T total parameters, about 48B active per token
LongCat-2.5
Its 1M tokens window holds about 3.8× more than Kimi K2.6's 256K in a single prompt.
Limited-time API price of $0.30/$1.20 per million tokens (cached input $0.006)
LongCat-2.5
At $0.3/$1.2 per 1M tokens it undercuts Kimi K2.6 ($0.95/$4 per 1M tokens), and that gap compounds at volume.
Meituan-reported Terminal-Bench v2.1 of 88.4, up from 70.8 for LongCat-2.0
LongCat-2.5
Meituan's November 2, 2026 natively multimodal successor to LongCat-2.0 - 1.6T parameters and 1M context at $0.30/$1.20, with open weights planned but undated — and it runs cheaper at $0.3/$1.2 per 1M tokens.
Lowest cost at scale
LongCat-2.5
At $0.3/$1.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
LongCat-2.5
Its 1M tokens window is about 3.8× larger than Kimi K2.6's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ LongCat-2.5
At $0.3/$1.2 per 1M tokens it undercuts Kimi K2.6, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ LongCat-2.5
Larger 1M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Kimi K2.6
Open weights let you run it on your own hardware; LongCat-2.5 is API-only.
Anyone whose priority is open-weight agentic coding and long-horizon tasks
→ Kimi K2.6
It is specifically built for that.
Anyone whose priority is native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token
→ LongCat-2.5
That is its strongest area.
Kimi K2.6: where it fits
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. Released April 20, 2026 by Moonshot AI, it is built for open-weight agentic coding and long-horizon tasks, multi-agent swarms (scales to ~300 sub-agents), self-hosting and data-residency control, and strong price-to-performance across many API providers.
Its trade-offs are real: 256K context trails the 1M Claude and Gemini flagships, weaker on single-turn vision and grounded multimodal tasks, and chinese-jurisdiction data and newer vendor track record. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
LongCat-2.5: where it fits
Meituan's November 2, 2026 natively multimodal successor to LongCat-2.0 - 1.6T parameters and 1M context at $0.30/$1.20, with open weights planned but undated. Released November 2, 2026 by Meituan, it is built for native multimodal upgrade to LongCat-2.0 - 1.6T total parameters, about 48B active per token, limited-time API price of $0.30/$1.20 per million tokens (cached input $0.006), meituan-reported Terminal-Bench v2.1 of 88.4, up from 70.8 for LongCat-2.0, and openAI- and Anthropic-compatible API endpoints with a 1M-token context window.
Its trade-offs: aPI-only for now - GitHub and Hugging Face releases are planned but undated, and the license is unconfirmed (LongCat-2.0 is MIT), the $0.30/$1.20 rate is labeled limited-time, and Meituan says prices are subject to change, and benchmarks are Meituan's own, and sources found no independent score at launch. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. Kimi K2.6 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. LongCat-2.5 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Kimi K2.6 and LongCat-2.5 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Kimi K2.6 or LongCat-2.5 better for coding?
Public SWE-Bench figures are not available for LongCat-2.5, so the honest test is your own repository — run an identical real bug through both. By design, Kimi K2.6 leans toward open-weight agentic coding and long-horizon tasks while LongCat-2.5 leans toward native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.6 or LongCat-2.5?
Kimi K2.6 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while LongCat-2.5 is API-metered at $0.3/$1.2 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
LongCat-2.5 — 1M tokens vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K2.6 and LongCat-2.5 together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.6, LongCat-2.5 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Kimi K2.6 or LongCat-2.5?
LongCat-2.5 — released November 2, 2026, about 7 months after Kimi K2.6.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.