Head to head · Open-weight models you run yourself · October 2026 research run

Hy3 vs LongCat-2.5

Hy3 scores 62.6 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

Hy3 B

Best for Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0

Ahead on

  • Reach, 70 against 35
  • Documentation and upkeep, 56 against 46

Watch for

597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build

LongCat-2.5 D

Best for Operators with NPU clusters, and anyone tracking what the open flagships can do

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

3,562 GB in BF16 and 1,336 GB at 4 bits

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reach Hy3 +35

Hy3 70

Memory earns 5, because the smallest build of 4-bit precision or better is AngelSlim's GPTQ-Int4 of 168.5 GB, about 202 GB with the fifth added, which is within 256 GB but beyond any single workstation. The 1-bit IQ1_M GGUF fits our reference machine but sits below the 4-bit line. Runtimes earn 20, for model code in released Transformers, vLLM, SGLang and llama.cpp; Ollama and LM Studio have no entry for it. Builds earn the full 15, with Tencent's FP8 repository and AngelSlim's GPTQ and GGUF builds. Terms earn 15 for plain Apache 2.0. Download earns 10, with no gate and no account. Hosted earns 5, since Featherless AI and DeepInfra serve it live through Hugging Face.

LongCat-2.5 35

Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.

Documentation and upkeep Hy3 +10

Hy3 56

The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general statement with no cut-off. The last change earns 10, a config.json edit on 20 July and AngelSlim uploads on 21 July, within 180 days on 2 November 2026; the line drops to 5 after 17 January 2027 unless the files change. Responsiveness earns 10, with Tencent replies on 7 of 19 discussions and none since 22 July. Version policy earns the full 10, since the preview stays in its own repository and the card dates it and says what changed. File health earns 10, with files that agree and no broken-file report waiting.

LongCat-2.5 46

Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.

Score by category

CategoryWeight this runHy3LongCat-2.5Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257035Hy3 +35
Risk20%206060even
Agent interface10%107575even
Documentation and upkeep10%105646Hy3 +10
Negative events≤1500
TotalB 62.6/100D 50.5/100

Facts side by side

FactHy3LongCat-2.5
KindOpen-weight modelOpen-weight model
VendorTencent (Hy team)Meituan (LongCat)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0MIT
Read-only variant documentednono
llms.txtnono
Last release2026-07-202026-11-10
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

Verdicts

Hy3

Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.

LongCat-2.5

Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.

Before you call either

Hy3

  1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3
  2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows
  3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error
  4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content
  5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe
  6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling
  7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers
  8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers

LongCat-2.5

  1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
  2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
  3. Pass chat_template_kwargs with enable_thinking true or false
  4. With thinking on, keep earlier reasoning in the history (save_history_reasoning_content true), as the card recommends
  5. The card's results use temperature 1.0 and top_p 0.95
  6. Its benchmark table is Meituan's own. The unmarked scores are in-house

Questions

Which is better for AI agents, Hy3 or LongCat-2.5?

Hy3 scores 62.6 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories.

Other comparisons with Hy3 or LongCat-2.5

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