# Gemma 4 E4B IT vs LongCat-2.5 > Gemma 4 E4B IT scores 63.2 (B) to LongCat-2.5's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-longcat-2-5 - Markdown: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-longcat-2-5.md (~9,550 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Gemma 4 E4B IT scores 63.2 (B) 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. - Gemma 4 E4B IT: grade B, 63.2/100, rank #24 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json - LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Gemma 4 E4B IT (B) Good for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning Ahead on: - Reach, 95 against 35 - Risk, 70 against 60 - Agent interface, 85 against 75 - Documentation and upkeep, 75 against 46 Watch for: No safety evaluation figures published ### LongCat-2.5 (D) Good for: Operators with NPU clusters, and anyone tracking what the open flagships can do Watch for: 3,562 GB in BF16 and 1,336 GB at 4 bits ## Why the edge ### Reach: Gemma 4 E4B IT +60 - Gemma 4 E4B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 6.15 GB, which with a fifth on top is 7.4 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one. - 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 E4B IT +29 - Gemma 4 E4B IT (75): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google's JAX library fixed the Gemma 4 audio embedding that this size runs on 5 October 2026, 28 days before this check, while the Hugging Face files last changed on 20 July. The line drops to 15 after 4 November unless the files or reference code change. Responsiveness 10, Google replied to 12 of 26 questions and reports, the last on 2026-07-17, with the five opened from 21 September to 1 November unanswered. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but reports about them wait without a reply. 20 + 10 + 20 + 10 + 10 + 5 = 75. - 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 E4B IT +10 - Gemma 4 E4B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma 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 E4B IT +10 - Gemma 4 E4B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line. - 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 | Gemma 4 E4B IT | LongCat-2.5 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 35 | Gemma 4 E4B IT +60 | | Risk | 20% (20 this run) | 70 | 60 | Gemma 4 E4B IT +10 | | Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 E4B IT +10 | | Documentation and upkeep | 10% (10 this run) | 75 | 46 | Gemma 4 E4B IT +29 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **63.2 · B** | **50.5 · D** | | ## Facts side by side | Fact | Gemma 4 E4B 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 | 3.5/5 (4) | none | ## Verdicts **Gemma 4 E4B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published 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 ### Gemma 4 E4B IT 1. On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio 2. With Ollama run `ollama run gemma4:e4b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K 3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there 4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns 5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them ### 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, Gemma 4 E4B IT or LongCat-2.5? Gemma 4 E4B IT scores 63.2 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-longcat-2-5.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e4b-it", "b": "longcat-2-5"}`. 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[LongCat-2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.md) - [LongCat-2.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.md) - [LongCat-2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.md) - [LongCat-2.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.md) - [LongCat-2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.md) - [LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.md) - [LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.md) - [LongCat-2.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.md) - [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md) - [LongCat-2.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.md) - [LongCat-2.5 vs ZAYA1-8B](https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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