Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Ling-3.0-tiny
Granite 4.2 30B 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 4969 open weights comparisons
Which one, for what
Best for Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card
Ahead on
- Risk, 55 against 50
- Agent interface, 85 against 60
- Documentation and upkeep, 87 against 56
Watch for
No safety results published for it
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 90
Watch for
No inference provider is listed on the Hub and no inclusionAI API serves it
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.
Documentation and upkeep Granite 4.2 30B +31
Granite 4.2 30B 87
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.
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.
Agent interface Granite 4.2 30B +25
Granite 4.2 30B 85
Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
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.
Reach Ling-3.0-tiny +5
Granite 4.2 30B 90
Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.
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.
Risk Granite 4.2 30B +5
Granite 4.2 30B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
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).
Score by category
| Category | Weight this run | Granite 4.2 30B | Ling-3.0-tiny | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 95 | Ling-3.0-tiny +5 |
| Risk | 20%20 | 55 | 50 | Granite 4.2 30B +5 |
| Agent interface | 10%10 | 85 | 60 | Granite 4.2 30B +25 |
| Documentation and upkeep | 10%10 | 87 | 56 | Granite 4.2 30B +31 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.2/100 |
Facts side by side
| Fact | Granite 4.2 30B | Ling-3.0-tiny |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | inclusionAI (Ant Group) |
| 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 | no | no |
| Last release | 2026-09-04 | 2026-08-31 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (4) |
How the scores have moved
Granite 4.2 30B
No score change since it was listed.
Verdicts
Granite 4.2 30B
IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.
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.
Before you call either
Granite 4.2 30B
- Start it with
ollama run granite4.2:30b, orllama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja - With vLLM 0.32.0 or later leave out
--reasoning-parser-plugin, since the parser is built in, and use--tool-call-parser qwen3_coder - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
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
Questions
Which is better for AI agents, Granite 4.2 30B or Ling-3.0-tiny?
Granite 4.2 30B 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.
Other comparisons with Granite 4.2 30B or Ling-3.0-tiny
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs Ling-3.0-tiny
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Ling-3.0-tiny
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs Ling-3.0-tiny
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs Ling-3.0-tiny
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs Ling-3.0-tiny
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs Ling-3.0-tiny
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Ling-3.0-tiny
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Ling-3.0-tiny
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs Ling-3.0-tiny
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs Ling-3.0-tiny
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs Ling-3.0-tiny
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Ling-3.0-tiny
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs Ling-3.0-tiny
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Ling-3.0-tiny
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs Ling-3.0-tiny
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Ling-3.0-tiny
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Ling-3.0-tiny
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs Ling-3.0-tiny
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs Ling-3.0-tiny
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs Ling-3.0-tiny
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b vs Ling-3.0-tiny
- Granite 4.2 30B vs Granite 4.2 3B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Hy-Memory-30B-A3B
- Granite 4.2 30B vs Hy3
- Granite 4.2 30B vs Inkling
- Granite 4.2 30B vs Inkling-Small
- Granite 4.2 30B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 30B vs Kimi K3
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 30B vs Kolibri-1
- Granite 4.2 30B vs Kronos
- Granite 4.2 30B vs Laguna M.1
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs Laguna XS 2.1
- Granite 4.2 30B vs LBS-27B
- Granite 4.2 30B vs LFM2.5-2.6B
- Granite 4.2 30B vs LFM3-1.7B
- Granite 4.2 30B vs LFM3-Audio-2B
- Granite 4.2 30B vs LFM3-Omni-2B
- Granite 4.2 30B vs LFM3-VL-2B
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 30B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 30B vs MiniCPM-o 4.5
- Granite 4.2 30B vs MiniCPM-V 4.7 1B
- Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 30B vs MiniMax-M2.5
- Granite 4.2 30B vs MiniMax-M2.7
- Granite 4.2 30B vs MiniMax-M3.1-Flash
- Granite 4.2 30B vs Mistral Large 4
- Granite 4.2 30B vs Muse Glimmer 30B
- Granite 4.2 30B vs Nanbeige4.3-33B
- Granite 4.2 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 30B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 4B
- Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 30B vs Nemotron 3 Super 120B A12B
- Granite 4.2 30B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 30B vs occamy-1.0
- Granite 4.2 30B vs Olmo 3 7B Think
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 30B vs Ornith-1.5-35B-A3B
- Granite 4.2 30B vs Ornith-1.5-397B
- Granite 4.2 30B vs Ornith-1.5-9B
- Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 30B vs Qwen3-4B-Instruct-2507
- Granite 4.2 30B vs Qwen3.5-0.8B
- Granite 4.2 30B vs Qwen3.5-122B-A10B
- Granite 4.2 30B vs Qwen3.5-2B
- Granite 4.2 30B vs Qwen3.5-397B-A17B
- Granite 4.2 30B vs Qwen3.5-4B
- Granite 4.2 30B vs Qwen3.5-9B
- Granite 4.2 30B vs Qwen3.6-27B
- Granite 4.2 30B vs Qwen3.6-35B-A3B
- Granite 4.2 30B vs Qwen3.8-27B
- Granite 4.2 30B vs Qwen3.8-Flash-Next
- Granite 4.2 30B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-Coder-Next
- Granite 4.2 30B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-2B-Instruct
- Granite 4.2 30B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-32B-Instruct
- Granite 4.2 30B vs Qwen3-VL-4B-Instruct
- Granite 4.2 30B vs Qwen3-VL-8B-Instruct
- Granite 4.2 30B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 30B vs Recursive Flint
- Granite 4.2 30B vs Ripple-65B Preview
- Granite 4.2 30B vs SmolLM3-3B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 30B vs Baidu Unlimited-OCR-2
- Granite 4.2 3B vs Ling-3.0-tiny
- Granite 4.2 8B vs Ling-3.0-tiny
- Hy-Memory-30B-A3B vs Ling-3.0-tiny
- Hy3 vs Ling-3.0-tiny
- Inkling-Small vs Ling-3.0-tiny
- Inkling vs Ling-3.0-tiny
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny
- Kimi K3.1 Pro vs Ling-3.0-tiny
- Kimi K3 vs Ling-3.0-tiny
- Kolibri-1 vs Ling-3.0-tiny
- Kronos vs Ling-3.0-tiny
- Laguna M.1 vs Ling-3.0-tiny
- Laguna S 2.1 vs Ling-3.0-tiny
- Laguna XS 2.1 vs Ling-3.0-tiny
- LBS-27B vs Ling-3.0-tiny
- LFM2.5-2.6B vs Ling-3.0-tiny
- LFM3-1.7B vs Ling-3.0-tiny
- LFM3-Audio-2B vs Ling-3.0-tiny
- LFM3-Omni-2B vs Ling-3.0-tiny
- LFM3-VL-2B vs Ling-3.0-tiny
- Ling-3.0-tiny vs Ling-3.1-flash-1019
- Ling-3.0-tiny vs MiMo-V2.6 Distill Qwen 9B
- Ling-3.0-tiny vs MiniCPM-o 4.5
- Ling-3.0-tiny vs MiniCPM-V 4.7 1B
- Ling-3.0-tiny vs MiniCPM-V 4.7 35B-A3B
- Ling-3.0-tiny vs MiniCPM5-2B
- Ling-3.0-tiny vs MiniMax-M2.5
- Ling-3.0-tiny vs MiniMax-M2.7
- Ling-3.0-tiny vs MiniMax-M3.1-Flash
- Ling-3.0-tiny vs Mistral Large 4
- Ling-3.0-tiny vs Muse Glimmer 30B
- Ling-3.0-tiny vs Nanbeige4.3-33B
- Ling-3.0-tiny vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Ling-3.0-tiny vs Nemotron 3.5 Lightning 30B A3B
- Ling-3.0-tiny vs Nemotron 3 Nano 30B A3B
- Ling-3.0-tiny vs Nemotron 3 Nano 4B
- Ling-3.0-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Ling-3.0-tiny vs Nemotron 3 Super 120B A12B
- Ling-3.0-tiny vs Nemotron 3 Ultra 550B A55B
- Ling-3.0-tiny vs occamy-1.0
- Ling-3.0-tiny vs Olmo 3 7B Think
- Ling-3.0-tiny vs Ornith-1.5-122B
- Ling-3.0-tiny vs Ornith-1.5-35B-A3B
- Ling-3.0-tiny vs Ornith-1.5-397B
- Ling-3.0-tiny vs Ornith-1.5-9B
- Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B
- Ling-3.0-tiny vs Qwen3-30B-A3B-Instruct-2507
- Ling-3.0-tiny vs Qwen3-4B-Instruct-2507
- Ling-3.0-tiny vs Qwen3.5-0.8B
- Ling-3.0-tiny vs Qwen3.5-122B-A10B
- Ling-3.0-tiny vs Qwen3.5-2B
- Ling-3.0-tiny vs Qwen3.5-397B-A17B
- Ling-3.0-tiny vs Qwen3.5-4B
- Ling-3.0-tiny vs Qwen3.5-9B
- Ling-3.0-tiny vs Qwen3.6-27B
- Ling-3.0-tiny vs Qwen3.6-35B-A3B
- Ling-3.0-tiny vs Qwen3.8-27B
- Ling-3.0-tiny vs Qwen3.8-Flash-Next
- Ling-3.0-tiny vs Qwen3-Coder-30B-A3B-Instruct
- Ling-3.0-tiny vs Qwen3-Coder-Next
- Ling-3.0-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-2B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-30B-A3B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-32B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-4B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-8B-Instruct
- Ling-3.0-tiny vs Qwopus3.8-27B-Flash-V2
- Ling-3.0-tiny vs Recursive Flint
- Ling-3.0-tiny vs Ripple-65B Preview
- Ling-3.0-tiny vs SmolLM3-3B
- Ling-3.0-tiny vs Spark-X2.6-4B
- Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B
- Ling-3.0-tiny vs Swift 1.5 Qwen3.8-Flash-Next
- Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)
- Ling-3.0-tiny vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-ling-3-0-tiny.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-30b.json·/api/v1/tools/ling-3-0-tiny.json - From a terminal
anchor compare granite-4-2-30b ling-3-0-tiny(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "ling-3-0-tiny"}at/mcp, no key