# Ling-3.0-tiny vs LongCat-2.5 > Ling-3.0-tiny scores 56.2 (C) 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/ling-3-0-tiny-vs-longcat-2-5 - Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5.md (~9,500 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 Ling-3.0-tiny scores 56.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. Both do open-weight models you run yourself. - Ling-3.0-tiny: grade C, 56.2/100, rank #117 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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 ### Ling-3.0-tiny (C) Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on: - Reach, 95 against 35 - Documentation and upkeep, 56 against 46 Watch for: No inference provider is listed on the Hub and no inclusionAI API serves it ### LongCat-2.5 (D) Good for: Operators with NPU clusters, and anyone tracking what the open flagships can do Ahead on: - Risk, 60 against 50 - Agent interface, 75 against 60 Watch for: 3,562 GB in BF16 and 1,336 GB at 4 bits ## Why the edge ### Reach: Ling-3.0-tiny +60 - Ling-3.0-tiny (95): Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it was found. - 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 +15 - Ling-3.0-tiny (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format is only in the template and the card names the ling3 parser in commands without describing it, while SGLang uses another parser. Trained tool calling 20, several calls a turn and agentic results on the card. Structured output 0, not documented. Reasoning control 0, thinking turns off per request but the card doesn't say where the reasoning comes back. Context 10, 131,072 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. ### Risk: LongCat-2.5 +10 - Ling-3.0-tiny (50): File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code. Provenance gets 15 for files in inclusionAI's own organisation with a checksum on each. Safety evaluation is 0, since SingProbe's results grade the probe, not the model. Agent guidance gets 5 for the SingProbe guardrail inclusionAI publishes for this model, with no statement of trained refusals. MIT carries no licence deductions (15). No security contact or disclosure policy was found (0). - 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. ### Documentation and upkeep: Ling-3.0-tiny +10 - Ling-3.0-tiny (56): Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 15, a data card with sources, size, cut-off and filtering. Last change 15, the newest commit is still the card change of 24 September 2026, 41 days before 4 November. Responsiveness 10, six of 16 discussions answered by an inclusionAI member, some. Version policy 5, a first release at this size. File health 0, the card's MTP recipe contradicts the files and a broken index size waits 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. ## Score by category | Category | Weight | Ling-3.0-tiny | LongCat-2.5 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 35 | Ling-3.0-tiny +60 | | Risk | 20% (20 this run) | 50 | 60 | LongCat-2.5 +10 | | Agent interface | 10% (10 this run) | 60 | 75 | LongCat-2.5 +15 | | Documentation and upkeep | 10% (10 this run) | 56 | 46 | Ling-3.0-tiny +10 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **56.2 · C** | **50.5 · D** | | ## Facts side by side | Fact | Ling-3.0-tiny | LongCat-2.5 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | Meituan (LongCat) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-31 | 2026-11-10 | | Terms last updated | no document linked | not read yet | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.8/5 (4) | none | ## How the scores have moved ### Ling-3.0-tiny - +1.2 on 4 November 2026: correction ### LongCat-2.5 No score change since it was listed. ## Verdicts **Ling-3.0-tiny.** A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and 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 ### Ling-3.0-tiny 1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3 2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code 3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer 4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks 5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20 6. The template keeps earlier reasoning in the prompt, which grows long agent sessions 7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented 8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches ### 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, Ling-3.0-tiny or LongCat-2.5? Ling-3.0-tiny scores 56.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "longcat-2-5"}`. From a terminal: `anchor compare ling-3-0-tiny longcat-2-5` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json ## Other comparisons with Ling-3.0-tiny or LongCat-2.5 - [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.md) - [Agents-A1-4B vs LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.md) - [Apertus v1.5 8B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.md) - [Apertus v1.5 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md) - [Apodex-1.1-mini vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.md) - [Apodex-1.1-mini vs LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md) - [Arete-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.md) - 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[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). How: https://www.anchorterminal.com/agents/#mcp