Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Nanbeige4.3-33B
Granite 4.2 30B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on risk. 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
- Reach, 90 against 80
- Documentation and upkeep, 87 against 56
Watch for
No safety results published for it
Best for Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.
Ahead on
- Risk, 60 against 55
Watch for
Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs
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.
Nanbeige4.3-33B 56
The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off. Training data earns 0, as nothing describes it. Last change earns 20 for the quantised builds of 31 October 2026. Responsiveness earns 10, a Nanbeige member answering nine of 30 discussions. Version policy earns 5 for a first release, with a config change made and reverted under bare commit titles. File health earns 10, the files and the card agreeing.
Reach Granite 4.2 30B +10
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.
Nanbeige4.3-33B 80
Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all run the Llama architecture, and Ollama and LM Studio have no build. Builds earn 15 for Nanbeige's INT4, INT8 and FP8 builds of 31 October 2026. Terms earn 15 for Apache 2.0 with nothing added, though only the metadata names it. Download earns 10, with no gate. Hosted earns 0, as no provider or Nanbeige API serves it.
Risk Nanbeige4.3-33B +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.
Nanbeige4.3-33B 60
File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model.
Score by category
| Category | Weight this run | Granite 4.2 30B | Nanbeige4.3-33B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 80 | Granite 4.2 30B +10 |
| Risk | 20%20 | 55 | 60 | Nanbeige4.3-33B +5 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 87 | 56 | Granite 4.2 30B +31 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 60.8/100 |
Facts side by side
| Fact | Granite 4.2 30B | Nanbeige4.3-33B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Nanbeige LLM Lab (BOSS Zhipin) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-04 | 2026-10-31 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
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.
Nanbeige4.3-33B
A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.
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
Nanbeige4.3-33B
- Serve with
vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder, as the card says, or SGLang with the same two parsers - On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB
- Keep
tool_call_formatat its defaultxml, since qwen3_coder parses that format; thejsonoption writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes - Pass
chat_template_kwargs{"preserve_thinking": true}for multi-turn tool use and coding, and{"enable_thinking": false}to skip reasoning, as the card recommends - Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card
- Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed
- Send one system message at the start only, as the template raises an error for a later one
- In plain Transformers pass
use_cache=Trueto generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed
Questions
Which is better for AI agents, Granite 4.2 30B or Nanbeige4.3-33B?
Granite 4.2 30B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on risk.
Other comparisons with Granite 4.2 30B or Nanbeige4.3-33B
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs Nanbeige4.3-33B
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Nanbeige4.3-33B
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs Nanbeige4.3-33B
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs Nanbeige4.3-33B
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs Nanbeige4.3-33B
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs Nanbeige4.3-33B
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Nanbeige4.3-33B
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Nanbeige4.3-33B
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs Nanbeige4.3-33B
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs Nanbeige4.3-33B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs Nanbeige4.3-33B
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs Nanbeige4.3-33B
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs Nanbeige4.3-33B
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Nanbeige4.3-33B
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs Nanbeige4.3-33B
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Nanbeige4.3-33B
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs Nanbeige4.3-33B
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Nanbeige4.3-33B
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Nanbeige4.3-33B
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs Nanbeige4.3-33B
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs Nanbeige4.3-33B
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs Nanbeige4.3-33B
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b vs Nanbeige4.3-33B
- 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-3.0-tiny
- 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 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 Nanbeige4.3-33B
- Granite 4.2 8B vs Nanbeige4.3-33B
- Hy-Memory-30B-A3B vs Nanbeige4.3-33B
- Hy3 vs Nanbeige4.3-33B
- Inkling-Small vs Nanbeige4.3-33B
- Inkling vs Nanbeige4.3-33B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.3-33B
- Kimi K3.1 Pro vs Nanbeige4.3-33B
- Kimi K3 vs Nanbeige4.3-33B
- Kolibri-1 vs Nanbeige4.3-33B
- Kronos vs Nanbeige4.3-33B
- Laguna M.1 vs Nanbeige4.3-33B
- Laguna S 2.1 vs Nanbeige4.3-33B
- Laguna XS 2.1 vs Nanbeige4.3-33B
- LBS-27B vs Nanbeige4.3-33B
- LFM2.5-2.6B vs Nanbeige4.3-33B
- LFM3-1.7B vs Nanbeige4.3-33B
- LFM3-Audio-2B vs Nanbeige4.3-33B
- LFM3-Omni-2B vs Nanbeige4.3-33B
- LFM3-VL-2B vs Nanbeige4.3-33B
- Ling-3.0-tiny vs Nanbeige4.3-33B
- Ling-3.1-flash-1019 vs Nanbeige4.3-33B
- MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.3-33B
- MiniCPM-o 4.5 vs Nanbeige4.3-33B
- MiniCPM-V 4.7 1B vs Nanbeige4.3-33B
- MiniCPM-V 4.7 35B-A3B vs Nanbeige4.3-33B
- MiniCPM5-2B vs Nanbeige4.3-33B
- MiniMax-M2.5 vs Nanbeige4.3-33B
- MiniMax-M2.7 vs Nanbeige4.3-33B
- MiniMax-M3.1-Flash vs Nanbeige4.3-33B
- Mistral Large 4 vs Nanbeige4.3-33B
- Muse Glimmer 30B vs Nanbeige4.3-33B
- Nanbeige4.3-33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Nanbeige4.3-33B vs Nemotron 3.5 Lightning 30B A3B
- Nanbeige4.3-33B vs Nemotron 3 Nano 30B A3B
- Nanbeige4.3-33B vs Nemotron 3 Nano 4B
- Nanbeige4.3-33B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nanbeige4.3-33B vs Nemotron 3 Super 120B A12B
- Nanbeige4.3-33B vs Nemotron 3 Ultra 550B A55B
- Nanbeige4.3-33B vs occamy-1.0
- Nanbeige4.3-33B vs Olmo 3 7B Think
- Nanbeige4.3-33B vs Ornith-1.5-122B
- Nanbeige4.3-33B vs Ornith-1.5-35B-A3B
- Nanbeige4.3-33B vs Ornith-1.5-397B
- Nanbeige4.3-33B vs Ornith-1.5-9B
- Nanbeige4.3-33B vs Qwen-AgentWorld-35B-A3B
- Nanbeige4.3-33B vs Qwen3-30B-A3B-Instruct-2507
- Nanbeige4.3-33B vs Qwen3-4B-Instruct-2507
- Nanbeige4.3-33B vs Qwen3.5-0.8B
- Nanbeige4.3-33B vs Qwen3.5-122B-A10B
- Nanbeige4.3-33B vs Qwen3.5-2B
- Nanbeige4.3-33B vs Qwen3.5-397B-A17B
- Nanbeige4.3-33B vs Qwen3.5-4B
- Nanbeige4.3-33B vs Qwen3.5-9B
- Nanbeige4.3-33B vs Qwen3.6-27B
- Nanbeige4.3-33B vs Qwen3.6-35B-A3B
- Nanbeige4.3-33B vs Qwen3.8-27B
- Nanbeige4.3-33B vs Qwen3.8-Flash-Next
- Nanbeige4.3-33B vs Qwen3-Coder-30B-A3B-Instruct
- Nanbeige4.3-33B vs Qwen3-Coder-Next
- Nanbeige4.3-33B vs Qwen3-Omni-30B-A3B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-2B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-30B-A3B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-32B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-4B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct
- Nanbeige4.3-33B vs Qwopus3.8-27B-Flash-V2
- Nanbeige4.3-33B vs Recursive Flint
- Nanbeige4.3-33B vs Ripple-65B Preview
- Nanbeige4.3-33B vs SmolLM3-3B
- Nanbeige4.3-33B vs Spark-X2.6-4B
- Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B
- Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next
- Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)
- Nanbeige4.3-33B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-nanbeige-4-3-33b.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/nanbeige-4-3-33b.json - From a terminal
anchor compare granite-4-2-30b nanbeige-4-3-33b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "nanbeige-4-3-33b"}at/mcp, no key