LongCat 2.5: Meituan's 1.6T Model Built for Autonomous Agents

Meituan is back with a sequel. LongCat 2.5 lands roughly three months after LongCat 2.0 — the 1.6-trillion-parameter mixture-of-experts model that made its name training on AI ASIC superpods instead of Nvidia GPUs — and this time the pitch has shifted. LongCat 2.0 sold itself on coding-agent benchmarks. LongCat 2.5 sells itself on autonomy: long-horizon task execution, tool use, and multi-step planning, framed explicitly for the agent workloads that Claude Code, OpenClaw, and similar harnesses run in production.

The headline number carries over almost unchanged: ~1.6 trillion parameters, still almost certainly a mixture-of-experts (MoE) design given Meituan's LongCat lineage and the fact that a dense 1.6T model would be functionally unservable at any reasonable cost. What moved is the framing, and — per Meituan's own release messaging — the post-training emphasis. This post covers what's actually new, where LongCat 2.5 sits in the crowded field of open-weight Chinese agent models, and when it's worth reaching for over GPT, Claude, or Gemini for agentic work.

TL;DR — LongCat 2.5 at a glance

What actually changed from LongCat 2.0

LongCat 2.0 was, at its core, a coding-agent model that happened to also do general chat and reasoning. Meituan's June 30 launch leaned hard on Terminal-Bench, SWE-bench Pro, and SWE-bench Multilingual scores, measured through Claude Code sandboxes. The "agent" story was there — Claude Code, OpenClaw, and Hermes integration were called out at launch — but it read as a secondary bullet point under a coding-benchmark headline.

LongCat 2.5 inverts that emphasis. The framing this time is autonomous agents first: sustaining a task across many steps, invoking tools correctly across a long session, replanning when a sub-task fails, and finishing a job without a human re-prompting at every turn. That is a meaningfully different design target than "score well on a single-turn SWE-bench task," even when the same base architecture underlies both.

Parameter count and likely architecture

The 1.6T figure puts LongCat 2.5 in the same weight class as LongCat 2.0's 1.6T total / ~48B active MoE design. Given Meituan's LongCat 2.0 precedent — LongCat Sparse Attention (LSA), N-gram Embedding at 135B parameters, and a MOPD (multi-expert post-training) fusion of Agent, Reasoning, and Interaction expert groups — the reasonable assumption is that LongCat 2.5 iterates on the same architectural family rather than starting from scratch. That is an inference from Meituan's track record, not a confirmed spec: verify the exact active-parameter count, attention mechanism, and expert-routing details against Meituan's own technical report or model card before citing them as fact. Vendors regularly tweak active-parameter counts and routing between "point" releases even when the headline total stays flat.

The agent framing, concretely

"Targets autonomous agents" typically shows up in three places for a model like this:

1. Long-horizon task execution — staying coherent and on-task across dozens or hundreds of tool calls in a single session, rather than degrading after a few turns.
2. Tool use reliability — correctly formatted function calls, sane retries on tool errors, and resistance to hallucinating tool outputs instead of actually invoking them.
3. Multi-step planning — decomposing a vague instruction ("migrate this service to the new SDK") into an ordered plan and executing it, revising the plan when an early step fails.

LongCat 2.0's MOPD post-training already had a dedicated Agent Experts group covering "code, work, search — tool invocation, parameter parsing, self-correction vs infinite loops." If LongCat 2.5 doubles down on that expert group specifically — more agent-oriented training data, more emphasis in the RL/post-training mix on multi-step task completion over single-turn scoring — that would explain the framing shift without requiring a from-scratch architecture change. Again: this is the most plausible mechanism given Meituan's own prior release notes, not a confirmed detail of LongCat 2.5's training recipe.

Benchmarks and pricing — what to actually trust

Meituan has a track record of publishing detailed, mostly in-house benchmark tables at launch (Terminal-Bench, SWE-bench Pro, FORTE, BrowseComp, GPQA-diamond for LongCat 2.0), with a smaller set of externally sourced comparison numbers for competing models. That table read as directionally credible but was measured on Meituan's own harness, and LongCat 2.0 trailed Opus-class closed models on several coding columns even in Meituan's own comparison.

Do not assume LongCat 2.5's benchmark specifics without checking Meituan's release notes directly — this post deliberately avoids inventing numbers it can't source. What's reasonable to expect, based on the pattern:

• An agent-specific benchmark suite (something in the shape of FORTE, or a new long-horizon task benchmark) getting top billing over pure coding scores, matching the "built for agents" framing.
• In-house numbers presented alongside starred external comparisons to GPT, Claude, and Gemini-class models — the same disclosure pattern LongCat 2.0 used.
• Pricing for the LongCat API Platform likely staying competitive with the rest of the open-weight Chinese field — Kimi K3, DeepSeek V4, and GLM-5.3 have all raced toward aggressive per-token pricing to win developer mindshare during the Fable 5 export-control disruption.

If you're evaluating LongCat 2.5 for production, run your own long-horizon agent tasks against it rather than trusting a launch-day leaderboard position — the same advice that applied to LongCat 2.0, and to every open-weight release in this cycle.

Where LongCat 2.5 sits in the open-weight landscape

2026 has been a race among Chinese labs to own the open-weight agent story while Fable 5 access stayed unstable for many international developers. LongCat 2.5 enters a field that already includes:

The pattern across all five: every major open-weight Chinese lab is now explicitly chasing agentic workloads, not just chat quality or raw benchmark scores. That's a meaningful shift from the "bigger number, better score" framing that dominated open-weight releases earlier in 2026. See explainx.ai's broader top-10 open-weight models roundup and open-weight vs closed model guide for how to think about the trade-offs beyond any single release.

LongCat 2.5 vs the field, practically

• Vs Kimi K3 — Kimi K3 is bigger by raw parameter count (2.8T vs ~1.6T) and leans on its 1M-token context window as the headline differentiator. LongCat 2.5 leans on agent-execution framing instead of context length. If your workload is "read an enormous codebase in one prompt," Kimi K3's context advantage matters more; if it's "execute a long chain of tool calls reliably," LongCat 2.5's positioning is the more direct fit — assuming its benchmarks hold up under independent testing.
• Vs DeepSeek V4 — DeepSeek has consistently undercut the field on price, making it the default choice for high-volume, cost-sensitive API traffic. LongCat models have historically required datacenter-class self-hosting (Meituan recommended 16x H20 GPUs for LongCat 2.0) rather than being the cheapest hosted option, so DeepSeek V4 likely remains the better fit when API cost per token is the deciding factor.
• Vs GLM-5.3 — GLM-5.3 pitches itself at coding plus cyber-defense benchmark performance. LongCat 2.5's agent-first framing is a narrower, more workflow-specific pitch. Choose GLM-5.3 if security-adjacent evaluation matters to your use case; choose LongCat 2.5 if the deciding factor is sustained multi-step task completion.

LongCat 2.5 vs GPT, Claude, and Gemini for agentic workloads

The practical question for most developers isn't "LongCat 2.5 vs the other open-weight Chinese models" — it's "when do I reach for LongCat 2.5 instead of a closed frontier model I already trust for agents."

Reach for LongCat 2.5 (or any open-weight agent model in this class) when:

• You need to self-host for data residency, cost-at-scale, or export-control reasons and can afford the datacenter-class hardware LongCat 2.0 required.
• You want to fine-tune or modify the model for a narrow agentic task — something closed frontier APIs don't permit.
• You're building redundancy into a multi-model routing setup and want an open-weight fallback that isn't dependent on a single vendor's API availability, the way many teams built around Fable 5 disruptions earlier this year.

Stick with GPT, Claude, or Gemini when:

• Your agent harness (Claude Code, Codex, or similar) is already tuned against a specific closed model's tool-calling format and you don't want to re-validate an entire pipeline.
• You need the highest floor on reliability today — LongCat 2.0's own launch numbers trailed Opus-class models on several coding columns, and LongCat 2.5's improvements are unverified until independent benchmarking lands.
• Your task involves sensitive data you can't route through a self-hosted model your team hasn't security-reviewed.

The honest takeaway: LongCat 2.5 is worth evaluating specifically because of its agent-first framing, not because it's guaranteed to beat GPT, Claude, or Gemini on your workload. Run it against your own long-horizon agent tasks before committing production traffic.

Licensing and where to run it

LongCat 2.0 shipped its weights and inference code MIT licensed with no restrictions starting July 5, 2026 — a fully open release with real safetensors (BF16/F32, FP8 variant, community quantizations) on Hugging Face. Meituan has kept this open-licensing pattern consistent across the LongCat family, and early signals point to LongCat 2.5 following the same MIT path, but this is worth confirming directly against the model card rather than assuming it carries over automatically — license terms have shifted release to release across other labs in this same field (Kimi K2.7-Code shipped under a Modified MIT, for instance).

For distribution, expect LongCat 2.5 to follow LongCat 2.0's established channels:

If LongCat 2.5 shows up on OpenRouter or similar model aggregators, that will likely happen a few days to weeks after the direct Hugging Face release, following the pattern set by Kimi K3 and DeepSeek V4's day-0-to-day-N hosting rollouts. Watch Meituan's own longcat.chat/blog for the primary announcement and confirm any third-party hosting claims against it.

Evaluation checklist before routing production agents to LongCat 2.5

1. Confirm the license on the actual Hugging Face model card — don't assume MIT carries over from LongCat 2.0.
2. Verify the parameter count and architecture details (active parameters, attention mechanism, expert routing) against Meituan's technical report rather than headline marketing.
3. Run your own long-horizon agent tasks — multi-step tool use, replanning after failure — since that's the specific capability this release claims to improve.
4. Compare against Kimi K3, DeepSeek V4, and GLM-5.3 on the same harness before picking one as your open-weight default.
5. Check hardware requirements — if LongCat 2.0's precedent holds, this is a datacenter-class model requiring multi-GPU or NPU deployment, not a laptop-friendly download.
6. Wait for independent benchmark reproduction before trusting any launch-day leaderboard position, in either direction.

Bottom line

LongCat 2.5 doesn't change the scale story Meituan told with LongCat 2.0 — it's still roughly 1.6 trillion parameters, still almost certainly MoE, still requiring datacenter-class hardware to self-host. What changed is the pitch: from "strong coding-agent benchmarks" to "built for autonomous agents," an emphasis on long-horizon execution, tool use, and multi-step planning that puts it in direct competition with Kimi K3, DeepSeek V4, and GLM-5.3 for the same agentic-coding audience.

The specifics that matter most — exact active-parameter count, confirmed license terms, and independently verified benchmark numbers — are not yet fully public as of this writing. Treat this as a directional read of what Meituan is building toward, verify against Meituan's own technical report before production use, and run your own agent evaluation before routing real workloads to it.

Related reading

• LongCat 2.0 — 1.6T MoE coding agent, full guide
• Kimi K3 open weights — 2.8T parameters, day-0 hosting
• DeepSeek V4-Pro benchmarks, pricing, and agent coding
• GLM-5.3 launch — coding and cyber-defense benchmarks
• Top 10 open-weight models you can run on a laptop
• Choosing open-weight vs closed AI models
• Fable 5 open-source enterprise alternatives
• What is an agent harness? Complete guide

LongCat 2.5 details in this post reflect publicly available information as of September 26, 2026. Meituan has not yet published a full technical report with independently verifiable benchmark numbers at the time of writing — verify architecture specifics, license terms, and performance claims against Meituan's own release notes and model card before production use.