Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 31B IT vs LongCat-2.5
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category. 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
Best for An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence
Ahead on
- Reach, 95 against 35
- Risk, 70 against 60
- Agent interface, 85 against 75
- Documentation and upkeep, 65 against 46
Also in its favour
- Agent-ready, a grade of BB or better
Watch for
No safety evaluation results published
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 Gemma 4 31B IT +60
Gemma 4 31B IT 95
Memory 25, Google's own QAT Q4_0 GGUF is 17.7 GB, which with a fifth added comes to about 21 GB and fits a 24 GB card even with the vision projector. Runtimes 25, released code in llama.cpp, Ollama's library, vLLM, SGLang and Transformers fills the cap without LM Studio, which only Google's guides name. Builds 15, Google ships a 4-bit GGUF and a 4-bit compressed-tensors build, read as two of the kinds the checklist names. Terms 15, plain Apache 2.0 with nothing added. Download 10, no gate and no account. Hosted 5, Google's Gemini API serves it today.
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 Gemma 4 31B IT +19
Gemma 4 31B IT 65
Model card 20, all seven items are there, with languages given as counts. Training data 10, a general description with a January 2025 cut-off. Last change 10, the files last changed on 20 July, 104 days before 1 November, and Google's reference code for Gemma 4 last changed in a way that runs this model on 6 July, both inside 180 days. Responsiveness 10, Google answered 14 of the last 40 discussions, none since 30 July. Version policy 10, earlier generations are kept in their own repositories with dated releases. File health 5, two template reports wait without a reply.
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 Gemma 4 31B IT +10
Gemma 4 31B IT 70
File safety 25, safetensors only and no code from the repository runs to load it or build prompts. Provenance 15, the files come from the google organisation that gemma.ai links to, with a SHA-256 for every weight file and a git hash for every file. Safety evaluation 0, Google says violations were minimal but publishes no results. Agent guidance 10, the card says what its safety training covers and asks for content safeguards, and the tool guide says to validate calls, but there is no prompt-injection guidance. Licence risk 15, Apache 2.0 carries none of the deductions and its link works. Security programme 5, Google's company-wide security.txt and reward programme exist but nothing ties them to Gemma, and no public advisories were found.
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.
Agent interface Gemma 4 31B IT +10
Gemma 4 31B IT 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents the declaration and call syntax and ships a response schema that Transformers parses. Trained tool calling 20, several calls and responses in one turn. Structured output 0, Google documents none and the card doesn't claim it. Reasoning control 10, thinking turns on or off per request and comes back in its own block. Context 10, 262,144 tokens native.
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.
Score by category
| Category | Weight this run | Gemma 4 31B IT | LongCat-2.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 35 | Gemma 4 31B IT +60 |
| Risk | 20%20 | 70 | 60 | Gemma 4 31B IT +10 |
| Agent interface | 10%10 | 85 | 75 | Gemma 4 31B IT +10 |
| Documentation and upkeep | 10%10 | 65 | 46 | Gemma 4 31B IT +19 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 74.1/100 | D 50.5/100 |
Facts side by side
| Fact | Gemma 4 31B IT | LongCat-2.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Meituan (LongCat) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-11-10 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |
Verdicts
Gemma 4 31B IT
A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository 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
Gemma 4 31B IT
- On the reference machine run
llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768, adding gemma-4-31B-it-mmproj.gguf for images - Or
ollama run gemma4:31bfor the 20 GB Q4_K_M build - In vLLM serve with
--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4, as vLLM's recipe does - Thinking is off by default, so pass
enable_thinking: truein chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block - Drop earlier thoughts from the history except on tool-call turns, as the card asks
- Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64
- Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding
- The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine
LongCat-2.5
- You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
- If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
- Pass
chat_template_kwargswithenable_thinkingtrue or false - With thinking on, keep earlier reasoning in the history (
save_history_reasoning_contenttrue), as the card recommends - The card's results use temperature 1.0 and top_p 0.95
- Its benchmark table is Meituan's own. The unmarked scores are in-house
Questions
Which is better for AI agents, Gemma 4 31B IT or LongCat-2.5?
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.
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anchor compare gemma-4-31b-it longcat-2-5(the CLI) - Over MCP
compare_tools {"a": "gemma-4-31b-it", "b": "longcat-2-5"}at/mcp, no key