LongCat-2.5 vs MAI-1-preview

Meituan · China  |  Microsoft · US · Updated June 2026

Quick verdict

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). Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. On a tight budget at scale, MAI-1-preview is the value pick.

LongCat-2.5 (Meituan, China) and MAI-1-preview (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecLongCat-2.5MAI-1-preview
ProviderMeituan (China) Microsoft (US)
ReleasedNovember 2, 2026 August 28, 2025
Context window1M tokens (~1,500 pages) 128K (~192 pages)
Price (in/out)$0.3/$1.2 per 1M tokens Not published
Open weight?No — API only No — API only
Modalitiestext, image, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

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 7.8× more than MAI-1-preview's 128K in a single prompt.

Limited-time API price of $0.30/$1.20 per million tokens (cached input $0.006)

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 carries the larger 1M tokens context.

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 is the newer of the two.

Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI

MAI-1-preview

MAI-1-preview lists microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI among its strengths; LongCat-2.5 does not.

Ranked in the top 15 on LM Arena at launch

MAI-1-preview

LongCat-2.5 is comparatively weak here — benchmarks are Meituan's own, and sources found no independent score at launch

Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment

MAI-1-preview

MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; LongCat-2.5 does not.

Lowest cost at scale

MAI-1-preview

Its weights are open, so at volume you pay for your own hardware instead of LongCat-2.5's $0.3/$1.2 per 1M tokens.

Largest single-prompt input

LongCat-2.5

Its 1M tokens window is about 7.8× larger than MAI-1-preview's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ MAI-1-preview

At Not published it undercuts LongCat-2.5, 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.

Anyone whose priority is native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token

→ LongCat-2.5

It is specifically built for that.

Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai

→ MAI-1-preview

That is its strongest area.

An enterprise with regional data-residency rules

→ MAI-1-preview or LongCat-2.5

Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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 are real: 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.

MAI-1-preview: where it fits

Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.

Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." LongCat-2.5 (China) and MAI-1-preview (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. MAI-1-preview is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both LongCat-2.5 and MAI-1-preview 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 pricing

Frequently asked questions

Is LongCat-2.5 or MAI-1-preview better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, LongCat-2.5 leans toward native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, LongCat-2.5 or MAI-1-preview?

MAI-1-preview is cheaper — $0.3/$1.2 per 1M tokens vs Not published.

Which has the bigger context window?

LongCat-2.5 — 1M tokens vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both LongCat-2.5 and MAI-1-preview together?

Yes — a multi-model platform like LumiChats gives you LongCat-2.5, MAI-1-preview 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, LongCat-2.5 or MAI-1-preview?

LongCat-2.5 — released November 2, 2026, about 14 months after MAI-1-preview.

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.