A Food-Delivery Giant's 1.6T-Parameter Bet: Meituan Ships LongCat-2.5-Preview

On September 25–26, 2026, one of the more improbable frontier-model stories of the year got its next chapter. Meituan — the Chinese company best known outside China for food delivery and local-services superapps — quietly shipped LongCat-2.5-Preview, an updated version of its 1.6-trillion-parameter mixture-of-experts model. There was no livestreamed keynote, no benchmark montage, no glossy landing page with cherry-picked scores. Just a changelog entry, a launch post on X, and — the part that got developers talking — two weeks of completely free access through the OpenCode platform with a zero-retention data policy.

For anyone tracking where frontier model capability actually lives now, a launch like this matters precisely because of who shipped it and what they chose not to ship with it.

What LongCat-2.5-Preview actually is

The headline specifications carry over from LongCat-2.0, the model Meituan open-sourced at the end of June 2026:

• 1.6 trillion total parameters in a mixture-of-experts design, with roughly 48 billion parameters active per token — about 3% of the model firing at any given moment, which is what keeps a model this size servable at all
• A 1 million-token context window, aimed squarely at long-horizon agent work
• Up to 131,072 output tokens (128K) in a single generation
• Native tool calling and multi-step reasoning inherited from the 2.0 lineage

What’s new in 2.5-Preview is native multimodal understanding. The model can now parse image content directly — supporting cross-modal question answering, content summarization over images, and what Meituan describes as complex visual reasoning. The framing in their changelog is deliberate: this is multimodality built into the model, “not patched together afterwards.” Meituan also emphasizes coding improvements — code generation, code understanding, and automated programming tasks — and deep compatibility with mainstream agent development environments: Claude Code, OpenCode, OpenClaw, Kilo Code, and Hermes Agent are all named explicitly.

The target workload is unmistakable: long-process agents. Meituan’s launch messaging describes tasks that span terminals, browsers, desktop software, spreadsheets, and design tools — the kind of multi-hour, multi-tool workflows that eat context for breakfast and need a model that can watch a screen, read a screenshot, and write the next tool call without losing the plot.

The OpenCode free window

The detail that turned a routine preview release into a trending topic on r/opencodeCLI was distribution. OpenCode announced on September 26 that LongCat-2.5-Preview would be free on its platform for two weeks — no request cap, no token meter running — under a stated zero-retention policy that excludes user data from training.

For developers, the math is straightforward. The paid-tier API pricing gives a sense of what this model costs to run: $0.30 per million uncached input tokens, $0.006 per million cached, and $1.20 per million output tokens, with reasoning mode available as a toggle that interleaves reasoning output with the answer. Two weeks of unmetered access to a 1M-token-context, natively multimodal model is, practically speaking, an open invitation to stress-test it on the workloads it was designed for: giant repository refactors, marathon browsing-and-extraction agents, whole-codebase comprehension passes.

Meituan sweetened the pot on its own platform too, granting 5 million free tokens to existing users to try the new model, with previously purchased token packs remaining usable. The official API is compatible with both OpenAI-style and Anthropic-style interfaces, and the company published configuration guides for Codex, OpenCode, OpenClaw, and its own CatPaw agent tool — meaning most existing agent setups can switch models with a one-line config change.

What Meituan did not publish

Here’s the genuinely unusual part, and the reason this launch deserves scrutiny rather than hype: no benchmarks accompanied the release. No SWE-bench numbers, no agentic eval suites, no comparisons against Claude, Gemini, or GPT-6 variants. For a 1.6T-parameter frontier-class model in late 2026, that’s close to unheard of — and it cuts both ways.

The skeptical read: LongCat-2.0 launched in June with bold claims of parity with the best models of its day, trained end-to-end on a 50,000-card cluster of Chinese Ascend 910B chips — a genuinely significant engineering feat and a geopolitical statement in itself. But independent testing was less kind. One well-known tester’s small-scale trials found the model, in his words, completely hopeless — small tests, to be fair, but a reminder that vendor claims and real-world agent performance often diverge sharply at this layer of the stack.

The charitable read: a “Preview” label with no benchmark theater is more honest than the alternative. In a year where Epoch AI reviewed 15 prominent AI benchmarks and flagged 9 of them as flawed — and where the same model can score 91 or 99 depending on which harness runs the test — publishing another leaderboard-topping chart arguably tells you less than it used to. Shipping the weights of your claim and letting two weeks of free, unmetered developer access render the verdict is a different, and arguably more credible, kind of evidence.

One correction worth pinning down, because it circulated widely in the first hours after launch: the model has 1.6 trillion parameters, not 16 trillion. Several early headlines and aggregation posts inflated the figure by a factor of ten. Both Meituan’s own materials and the Chinese-language source summaries agree on 1.6T total, ~48B active.

Why a delivery company keeps doing this

The deeper story is Meituan itself. The company operates one of the most brutally complex local-services marketplaces on earth — millions of riders, real-time dispatch, dynamic pricing, hallucination-intolerant logistics. An agentic model that can operate software, read screens, and sustain coherent behavior across thousand-step workflows isn’t a demo for a company like this; it’s headcount and margin. LongCat-2.0’s specialization in agentic coding was widely read as Meituan building for its own operations first and offering the surplus to the world second.

And LongCat-2.5-Preview continues a strategy that has become familiar across the Chinese AI ecosystem in 2026: iterate fast, price aggressively (or free), optimize for agent workloads over chatbench aesthetics, and treat developer adoption as the benchmark that matters. DeepSeek’s roadmap, Qwen’s voice-stack price cuts of up to 95% this same week, and now Meituan’s free multimodal preview all rhyme.

What to watch

Two weeks is a short window. The questions that will decide whether LongCat-2.5 graduates from “interesting free experiment” to “serious option” are:

1. Does native multimodality actually hold up in agent loops? Reading a screenshot and acting on it correctly, thousands of steps into a run, is the actual test — not single-image Q&A.
2. What happens when the benchmark data finally lands? Meituan says official numbers are not yet released. When they arrive, they’ll be checked against two weeks of grassroots developer experience, and any gap will be noticed.
3. Does the model stay accessible — and on what terms? Nothing in the launch material commits to post-window pricing or continued availability, and the weights have not been published for this version, so “open-weight” would be an overstatement today.

For now, the pragmatic move is the obvious one: if you build agents, you have a rare chance to put a 1.6T-parameter, million-token-context, natively multimodal model through your real workload at zero cost and zero data retention. Whatever you find in those two weeks is worth more than any benchmark chart Meituan could have published — and Meituan, one suspects, knows exactly that.