Head to head · Open-weight models you run yourself · October 2026 research run
Ling-3.0-tiny vs Nanbeige4.2-3B
Nanbeige4.2-3B scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach. 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 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 85
Watch for
No inference provider is listed on the Hub and no inclusionAI API serves it
Best for Local agents that need tool calling and a long context on a laptop or a small GPU
Ahead on
- Risk, 55 against 50
- Agent interface, 85 against 60
- Documentation and upkeep, 72 against 56
Watch for
Its own architecture: Transformers loads it only with the repository's code
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.
Agent interface Nanbeige4.2-3B +25
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.
Nanbeige4.2-3B 85
Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Documentation and upkeep Nanbeige4.2-3B +16
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.
Nanbeige4.2-3B 72
Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.
Reach Ling-3.0-tiny +10
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.
Nanbeige4.2-3B 85
Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit 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, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.
Risk Nanbeige4.2-3B +5
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).
Nanbeige4.2-3B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.
Score by category
| Category | Weight this run | Ling-3.0-tiny | Nanbeige4.2-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 85 | Ling-3.0-tiny +10 |
| Risk | 20%20 | 50 | 55 | Nanbeige4.2-3B +5 |
| Agent interface | 10%10 | 60 | 85 | Nanbeige4.2-3B +25 |
| Documentation and upkeep | 10%10 | 56 | 72 | Nanbeige4.2-3B +16 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.2/100 | C 61/100 |
Facts side by side
| Fact | Ling-3.0-tiny | Nanbeige4.2-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Nanbeige LLM Lab (BOSS Zhipin) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-31 | 2026-09-11 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
Nanbeige4.2-3B
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.
Nanbeige4.2-3B
A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.
Before you call either
Ling-3.0-tiny
- 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 - 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 - With SGLang 0.5.22, its cookbook uses
--reasoning-parser deepseek-r1 --tool-call-parser glm45for this model, and drop the card's NEXTN flags since tiny has no MTP layer - Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks - Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
- The template keeps earlier reasoning in the prompt, which grows long agent sessions
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
- 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
Nanbeige4.2-3B
- Serve it with SGLang 0.5.23 or later and pass
--tool-call-parser nanbeige --reasoning-parser nanbeige - Pass
toolsin the request. The card recommendstool_call_format="xml" - Set
preserve_thinkingtrue for multi-turn tool use and coding, false for plain chat - Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card
- For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's
- If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it
Questions
Which is better for AI agents, Ling-3.0-tiny or Nanbeige4.2-3B?
Nanbeige4.2-3B scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach.
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Machine-readable
- This page as Markdown
/compare/ling-3-0-tiny-vs-nanbeige-4-2-3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ling-3-0-tiny.json·/api/v1/tools/nanbeige-4-2-3b.json - From a terminal
anchor compare ling-3-0-tiny nanbeige-4-2-3b(the CLI) - Over MCP
compare_tools {"a": "ling-3-0-tiny", "b": "nanbeige-4-2-3b"}at/mcp, no key