An honest LongCat-2.5 review (2026): what Meituan's 1.6T native-multimodal agentic coding model does well, its real limits, pricing, and who it's for.
LongCat-2.5 is a serious agentic coding model: a 1.6T native-multimodal mixture-of-experts system with strong reported benchmarks on long-horizon tasks, a 1M-token context, and an API priced aggressively low. The honest catches are that it's a day-old release with mostly self-reported numbers, open weights aren't out yet, and — most important for creators — it is a reasoning and coding model, not a tool that makes or publishes social content. Promising for builders and agents; the wrong aisle if you need finished posts.
This review scores LongCat-2.5 for what it is: Meituan's natively multimodal, agentic mixture-of-experts model, introduced on November 2, 2026. It is not a content-creation tool, and the single most useful thing a review can do here is say that plainly before anyone spends a trial expecting it to cut shorts or draw carousels. If you build software, automate agents, or need cheap multimodal reasoning, read on. If you need finished video, images, and posts, this is the wrong category — skip to the Kompozy positioning note.
For the job it claims, LongCat-2.5 looks strong. The headline specs — 1.6 trillion total parameters, about 48 billion active per token, a 1M-token context window, and a native image-as-tokens design — are a credible frontier-agentic package, and the reported benchmarks on long-horizon coding and browsing are high. It is also cheap: reported limited-time API pricing lands well under a dollar per million input tokens, which is where a lot of agent workloads become economical.
I run a content engine, not a model lab, so I'll be careful about what I can and can't verify. The scores below weight capability as reported alongside the things that should temper enthusiasm on a brand-new model: benchmark numbers that are largely self-published, open weights that are still "coming soon," a short public track record, and training-and-infrastructure details (domestic accelerators, W4A4 inference) that matter more for some buyers than others.
Facts and figures here are reconciled against LongCat's announcement and November 2026 third-party coverage on 2026-11-02. A day-old release moves fast and much of it is secondary reporting, so verify current specs, pricing, and benchmarks on LongCat's official channels before you build around any single number.
LongCat-2.5 is a natively multimodal mixture-of-experts model from Meituan's LongCat team. It has 1.6 trillion total parameters with roughly 48 billion activated per token, builds on LongCat-2.0, and is aimed at long-horizon agentic tasks, agentic coding, and visual understanding. Rather than attaching a separate vision encoder, it handles images as discrete tokens via Residual Vector Quantization on native-resolution features from a Semantic-and-Aligned Encoder, processing text and images in one token format, with a visual de-tokenizer reconstructing images. That design underpins its visual-coding claims — generating front-end code from a screenshot or design. Access is through a live chat interface and a hosted API; GitHub and Hugging Face releases are listed as "coming soon," so open weights aren't available yet. Reported limited-time API pricing is about $0.30 per million input tokens and $1.20 per million output, with cached input far cheaper, a 1M-token context window, and up to 128K output tokens. Meituan says it trained on domestically produced AI accelerators and uses W4A4 (4-bit weight and activation) inference, and reports compatibility with agentic coding harnesses including Claude Code, Codex, and Hermes. What it is not is a generator of social video, images, or carousels — it reasons, codes, and runs agents.
LongCat-2.5 fits developers, agent builders, and technical teams who need a capable, inexpensive model for multi-step coding and automation, especially anyone doing visual coding (code from designs or screenshots) or wiring a model into harnesses like Claude Code, Codex, or Hermes. The cheap API and 1M-token context make it attractive for high-volume agent workloads where frontier closed models get costly. It's a weak fit for teams that require open weights today, for buyers who need independent, third-party-verified benchmarks before committing, or for anyone with strict data-governance or jurisdictional constraints to clear. And it is simply not for content creators who want to produce or publish finished video, images, carousels, blogs, or newsletters — that's a different class of tool.
| Dimension | Score | Why |
|---|---|---|
| Agentic coding capability | 4.4 / 5 | Reported scores on long-horizon coding and SWE-style benchmarks are high, and it targets full-project builds, not just snippets — though the numbers are largely self-published. |
| Multimodal / visual understanding | 4.2 / 5 | Native image-as-tokens design and visual-coding results (web-from-image, text-to-3D) are a genuine differentiator versus text-only peers. |
| Long-horizon task handling | 4.1 / 5 | Built and RL-tuned specifically for multi-turn agent trajectories, which is where many models fall apart; promising but newly released. |
| API pricing / value | 4.6 / 5 | Reported limited-time pricing well under $1 per million input tokens makes high-volume agent use economical — a real advantage if it holds. |
| Context window | 4.3 / 5 | A 1M-token context with up to 128K output covers large codebases and long agent runs; competitive at the frontier. |
| Availability & openness | 3.2 / 5 | Chat and API are live, but open weights are still "coming soon," so you cannot self-host or fully audit it yet. |
| Independent verification & track record | 3.0 / 5 | A day-old release with mostly self-reported benchmarks and a short public history — treat the claims as unverified until third parties test it. |
| Ecosystem / harness compatibility | 4.0 / 5 | Reported compatibility with Claude Code, Codex, and Hermes lets developers adopt it with a key swap into existing agent workflows. |
| Fit for content creators | 2.4 / 5 | Not a content tool — it generates no social video, images, or carousels and publishes nowhere. Low here is a category mismatch, not a flaw. |
On price, LongCat-2.5 is positioned to win on cost. Reported limited-time API pricing of roughly $0.30 per million input tokens and $1.20 per million output — with cached input far cheaper — sits well below frontier closed models, and for agent workloads that burn tokens across many turns, that difference compounds fast. If the goal is a cheap, capable brain for a high-volume automation or coding pipeline, the headline economics are genuinely attractive.
Two caveats keep this from a top score. First, the pricing is described as limited-time, so budgeting a long-running system on launch-promo rates is risky until steady-state pricing is published; the API platform's displayed prices are authoritative. Second, "cheap tokens" only matters if the model holds up in your real workload — and with benchmarks mostly self-reported on a day-old release, the right move is a scoped pilot measuring your tasks before you commit volume.
The honest framing for a creator reading this: the pricing conversation is a developer conversation. LongCat-2.5's cost advantage is real for building and automating, but it does not change the fact that the model doesn't produce the content you post. Cheap reasoning tokens and finished, on-brand, scheduled posts are different purchases. Confirm current rates on LongCat's official API platform before planning spend.
| Use case | Fit | Why |
|---|---|---|
| Running a multi-step coding or refactor agent across a codebase | Strong | Built and RL-tuned for long-horizon agentic coding, with reported high scores on SWE-style benchmarks. |
| Generating front-end code from a screenshot or design (visual coding) | Strong | Native image-as-tokens design and web-from-image results target exactly this task. |
| Cheap, high-volume multimodal reasoning in a custom pipeline | Strong | Low reported API pricing plus a 1M-token context make large agent workloads economical. |
| Self-hosting or fully auditing the model | Weak | Open weights are still "coming soon," so only the hosted API and chat are available today. |
| Committing production volume on verified performance | OK | Reported benchmarks are strong but largely self-published; a scoped pilot is wise before heavy reliance. |
| Generating finished social-media video, images, or carousels | Weak | LongCat-2.5 is a coding and reasoning model — it produces no social content and publishes nowhere. |
| Turning one source into a week of multi-platform posts | Weak | That is a content-engine job (clipping, carousels, avatar video, scheduling) entirely outside this model's scope. |
A fair comparison has to start by admitting LongCat-2.5 and Kompozy aren't competitors — they're different categories that search results sometimes jam together. LongCat-2.5 is a model you build with: it reasons, writes code, and runs agents. Kompozy is a content engine you create and publish with: it generates 18 content formats from one source and ships them across nine destinations. There is no overlap on the core job, and pretending otherwise would be dishonest.
Where they meet is a workflow, not a feature list. A technical creator might use LongCat-2.5 to build a product or automate a research step, then use Kompozy to produce and distribute the marketing — avatar and clipped video, brand-exact carousels, quote graphics, a blog, and a newsletter, all in one voice via the Persona Brief, scheduled and published across the eight social platforms plus blog and email. For the record, Kompozy's own generation runs on Claude and OpenAI, not LongCat. If your question is "which coding model should I pick," compare LongCat-2.5 against DeepSeek, Kimi, Claude, and GPT — not against Kompozy. If your question is "how do I turn what I build into content that reaches people," that's where Kompozy fits.
LongCat-2.5 is a natively multimodal mixture-of-experts AI model from Meituan's LongCat team, introduced on November 2, 2026. It has 1.6 trillion total parameters with about 48 billion active per token, builds on LongCat-2.0, and targets long-horizon agentic tasks, agentic coding, and visual understanding. A chat interface and API are live; open weights are listed as coming soon.
Per Meituan's announcement and launch coverage, yes — it's built specifically for agentic coding and long-horizon tasks, with high reported scores on SWE-style and browsing benchmarks and native visual-coding ability (code from screenshots). Those numbers are largely self-reported on a day-old release, so a scoped pilot on your own tasks is the sensible way to confirm it before relying on it.
Secondary coverage of the launch puts the limited-time API price at roughly $0.30 per million input tokens and $1.20 per million output, with cached input much cheaper and a 1M-token context window. Because it is described as limited-time and the API platform's displayed prices are authoritative, verify current rates before budgeting a long-running workload.
Not yet, as of the November 2026 launch. The announcement lists GitHub and Hugging Face releases as 'coming soon,' so for now LongCat-2.5 is reachable only through its chat interface and a hosted API. If self-hosting matters to you, a model like Qwen's open-weight family is available today; check LongCat's channels for status changes.
No. LongCat-2.5 is a coding and reasoning model, not a content generator — it does not produce social video, images, or carousels, and it publishes nowhere. To create and distribute that content you would use a dedicated engine like Kompozy, which generates 18 formats from one source and publishes across nine destinations.
No. They are different categories. LongCat-2.5 is a model you build and automate with; Kompozy is a content generation and multi-platform publishing engine you create posts with. They can complement each other — build with LongCat, then generate and publish the marketing with Kompozy — but one does not replace the other.
Meituan's LongCat team. It is part of Meituan's push on domestic models and chips — the company says LongCat-2.5 was trained on domestically produced AI accelerators and uses W4A4 inference — and it is reported compatible with agentic coding harnesses including Claude Code, Codex, and Hermes.