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

LongCat-2.5 vs Qwen3-VL-8B-Instruct

Qwen3-VL-8B-Instruct scores 54.9 (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, agent interface 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
  • Agent interface, 75 against 60
  • Documentation and upkeep, 46 against 31

Watch for

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

Qwen3-VL-8B-Instruct C

Best for An agent with 8 GB or more to spare that needs a local model to read images, documents and video

Ahead on

  • Reach, 100 against 35

Watch for

No safety evaluation, agent-use 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-VL-8B-Instruct +65

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-VL-8B-Instruct 100

Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.

Agent interface LongCat-2.5 +15

LongCat-2.5 75

Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.

Qwen3-VL-8B-Instruct 60

A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.

Documentation and upkeep LongCat-2.5 +15

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-VL-8B-Instruct 31

The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.

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-VL-8B-Instruct 55

Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55.

Score by category

CategoryWeight this runLongCat-2.5Qwen3-VL-8B-InstructEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%2535100Qwen3-VL-8B-Instruct +65
Risk20%206055LongCat-2.5 +5
Agent interface10%107560LongCat-2.5 +15
Documentation and upkeep10%104631LongCat-2.5 +15
Negative events≤1500
TotalD 50.5/100C 54.9/100

Facts side by side

FactLongCat-2.5Qwen3-VL-8B-Instruct
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 release2026-11-102025-11-01
Terms last updatednot read yetno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.8/5 (4)

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-VL-8B-Instruct

Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.

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-VL-8B-Instruct

  1. Start it with llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:8b-instruct
  2. In Ollama the plain qwen3-vl:8b tag is the Thinking edition, so name 8b-instruct
  3. With the GGUF, load the mmproj vision file too, or images won't be read
  4. Parse tool calls as JSON inside <tool_call> tags, or use SGLang's --tool-call-parser qwen
  5. Use the card's sampling, temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5 for vision and temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0 for text
  6. Validate JSON output yourself, since structured output isn't documented
  7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

Questions

Which is better for AI agents, LongCat-2.5 or Qwen3-VL-8B-Instruct?

Qwen3-VL-8B-Instruct scores 54.9 (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, agent interface and documentation and upkeep.

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