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

LongCat-2.5 vs Qwen3-4B-Instruct-2507

Qwen3-4B-Instruct-2507 scores 54.5 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep. 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

LongCat-2.5 D

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

Ahead on

  • Risk, 60 against 55
  • Documentation and upkeep, 46 against 26

Watch for

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

Qwen3-4B-Instruct-2507 C

Best for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on

  • Reach, 90 against 35

Watch for

No safety results, prompt-injection guidance or security contact

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 Qwen3-4B-Instruct-2507 +55

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.

Qwen3-4B-Instruct-2507 90

Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3 architecture, Ollama's released build pins a llama.cpp that has it, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 0, on 4 November 2026 the Hub's provider mapping showed only Featherless AI with status error, Nscale was gone and Alibaba's Model Studio doesn't list it. 30 + 25 + 10 + 15 + 10 + 0 = 90.

Documentation and upkeep LongCat-2.5 +20

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.

Qwen3-4B-Instruct-2507 26

Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26.

Risk LongCat-2.5 +5

LongCat-2.5 60

File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.

Qwen3-4B-Instruct-2507 55

File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Score by category

CategoryWeight this runLongCat-2.5Qwen3-4B-Instruct-2507Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%253590Qwen3-4B-Instruct-2507 +55
Risk20%206055LongCat-2.5 +5
Agent interface10%107575even
Documentation and upkeep10%104626LongCat-2.5 +20
Negative events≤1500
TotalD 50.5/100C 54.5/100

Facts side by side

FactLongCat-2.5Qwen3-4B-Instruct-2507
KindOpen-weight modelOpen-weight model
VendorMeituan (LongCat)Alibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-102025-09-17
Terms last updatednot read yetno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

Verdicts

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.

Qwen3-4B-Instruct-2507

A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published.

Before you call either

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

Qwen3-4B-Instruct-2507

  1. Quickest start is ollama run qwen3:4b-instruct, 2.5 GB. Plain qwen3:4b is the Thinking-2507 edition, not this one
  2. On our reference machine use BF16 with vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes
  3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
  4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
  5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
  6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
  7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

Questions

Which is better for AI agents, LongCat-2.5 or Qwen3-4B-Instruct-2507?

Qwen3-4B-Instruct-2507 scores 54.5 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep.

Other comparisons with LongCat-2.5 or Qwen3-4B-Instruct-2507

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