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

InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5

InternVL3.5-GPT-OSS-20B-A4B-Preview scores 53.2 (D) 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

InternVL3.5-GPT-OSS-20B-A4B-Preview D

Best for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.

Ahead on

  • Reach, 65 against 35

Watch for

No quantised build from OpenGVLab, so about 51 GB at BF16

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 45
  • Documentation and upkeep, 46 against 28

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 InternVL3.5-GPT-OSS-20B-A4B-Preview +30

InternVL3.5-GPT-OSS-20B-A4B-Preview 65

Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.

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.

Agent interface LongCat-2.5 +30

InternVL3.5-GPT-OSS-20B-A4B-Preview 45

Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.

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.

Documentation and upkeep LongCat-2.5 +18

InternVL3.5-GPT-OSS-20B-A4B-Preview 28

Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.

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.

Risk LongCat-2.5 +5

InternVL3.5-GPT-OSS-20B-A4B-Preview 55

File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.

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.

Score by category

CategoryWeight this runInternVL3.5-GPT-OSS-20B-A4B-PreviewLongCat-2.5Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256535InternVL3.5-GPT-OSS-20B-A4B-Preview +30
Risk20%205560LongCat-2.5 +5
Agent interface10%104575LongCat-2.5 +30
Documentation and upkeep10%102846LongCat-2.5 +18
Negative events≤1500
TotalD 53.2/100D 50.5/100

Facts side by side

FactInternVL3.5-GPT-OSS-20B-A4B-PreviewLongCat-2.5
KindOpen-weight modelOpen-weight model
VendorOpenGVLab (Shanghai AI Laboratory)Meituan (LongCat)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)MIT
Read-only variant documentednono
llms.txtnono
Last release2025-09-082026-11-10
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews1.8/5 (4)none

How the scores have moved

Verdicts

InternVL3.5-GPT-OSS-20B-A4B-Preview

An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.

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

InternVL3.5-GPT-OSS-20B-A4B-Preview

  1. Serve with vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768, which is our command; the card gives none for vLLM
  2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
  3. Use Transformers 4.55.0 or later, as the card requires for this size
  4. For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
  5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says

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, InternVL3.5-GPT-OSS-20B-A4B-Preview or LongCat-2.5?

InternVL3.5-GPT-OSS-20B-A4B-Preview scores 53.2 (D) 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.

Other comparisons with InternVL3.5-GPT-OSS-20B-A4B-Preview or LongCat-2.5

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.