Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs MiniCPM5-2B
Granite 4.2 8B scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 2 of 4 scored categories. 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 16 GB machine
Ahead on
- Reach, 95 against 90
- Agent interface, 85 against 75
Watch for
No safety results published for it
Best for On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning
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 Granite 4.2 8B +10
Granite 4.2 8B 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.
MiniCPM5-2B 75
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card and OpenBMB's cookbooks document the XML call format, the tools request and the minicpm5 parser in SGLang and vLLM. Trained tool calling 20, agent SFT on 500K samples and a template that renders several calls in one turn. Structured output 0, nothing documents asking for JSON. Reasoning control 0, a judgement call, because OpenBMB's own cookbooks call Think the only supported mode for the 2B even though the template accepts enable_thinking false. Context 10, 131,072 tokens native with no rope scaling.
Reach Granite 4.2 8B +5
Granite 4.2 8B 95
Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. 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, 8b 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. 30 + 25 + 15 + 15 + 10 + 0 = 95.
MiniCPM5-2B 90
Memory 30, OpenBMB's own MLX 4-bit build is 1.42 GB and its Q4_K_M GGUF 1.56 GB, about 2 GB with the fifth added. Runtimes 25, the cap, since the model is a stock Llama and vLLM, SGLang, released Transformers, a released llama.cpp and Ollama all run it. Builds 15, OpenBMB ships GGUF at three precisions plus MLX and GPTQ 4-bit. Terms 10, the LICENSE is plain Apache 2.0 but the card prohibits jailbreaking and injection testing and limits use to ethical purposes, which reads as a use policy on top (dated 21 October 2026). Download 10, nothing is gated. Hosted 0, OpenBMB has no API for it, no inference provider is listed on its Hugging Face page and OpenRouter doesn't carry it.
Score by category
| Category | Weight this run | Granite 4.2 8B | MiniCPM5-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 90 | Granite 4.2 8B +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 75 | Granite 4.2 8B +10 |
| Documentation and upkeep | 10%10 | 92 | 92 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 59.7/100 |
Facts side by side
| Fact | Granite 4.2 8B | MiniCPM5-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | OpenBMB |
| 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-09-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Granite 4.2 8B
No score change since it was listed.
Verdicts
Granite 4.2 8B
IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, 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.
MiniCPM5-2B
A 2B dense model for local tool calling and long context, with published training data. OpenBMB's own 4-bit builds are under 1.6 GB, and the model loads in vLLM, SGLang, llama.cpp, Ollama and Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.
Before you call either
Granite 4.2 8B
- Start it with
ollama run granite4.2:8b, orllama-server -hf ibm-granite/granite-4.2-8b-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
MiniCPM5-2B
- Smallest start is
ollama run openbmb/minicpm5-2b(1.6 GB, q4_k_m) - On vLLM 0.29.0 or later run
vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5, or tool calls come back as XML text - On SGLang 0.5.16 or later add
--tool-call-parser minicpm5 --reasoning-parser qwen3; SGLang is OpenBMB's recommended backend for tool calls - Tool calls are XML,
<function name=...><param name=...>, not Hermes JSON - Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does
- Expect a
<think>block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B - Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
- Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix
Questions
Which is better for AI agents, Granite 4.2 8B or MiniCPM5-2B?
Granite 4.2 8B scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 2 of 4 scored categories.
Other comparisons with Granite 4.2 8B or MiniCPM5-2B
- Agents-A1-4B vs Granite 4.2 8B
- Agents-A1-4B vs MiniCPM5-2B
- Apertus v1.5 8B vs Granite 4.2 8B
- Apertus v1.5 8B vs MiniCPM5-2B
- Apodex-1.1-mini vs Granite 4.2 8B
- Apodex-1.1-mini vs MiniCPM5-2B
- Arete-27B vs Granite 4.2 8B
- Arete-27B vs MiniCPM5-2B
- BigBang-v1 vs Granite 4.2 8B
- BigBang-v1 vs MiniCPM5-2B
- Carbon-X vs Granite 4.2 8B
- Carbon-X vs MiniCPM5-2B
- Cosmos-Reason2-2B vs Granite 4.2 8B
- Cosmos-Reason2-2B vs MiniCPM5-2B
- Darwin-36B-Opus vs Granite 4.2 8B
- Darwin-36B-Opus vs MiniCPM5-2B
- DeepSeek-V3.2 vs Granite 4.2 8B
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek V4.1 Flash vs Granite 4.2 8B
- DeepSeek V4.1 Flash vs MiniCPM5-2B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 8B
- DeepSeek-V4-Flash-0731 vs MiniCPM5-2B
- DiffusionGemma 26B A4B IT vs Granite 4.2 8B
- DiffusionGemma 26B A4B IT vs MiniCPM5-2B
- Gemma 4 12B IT vs Granite 4.2 8B
- Gemma 4 12B IT vs MiniCPM5-2B
- Gemma 4 26B A4B IT vs Granite 4.2 8B
- Gemma 4 26B A4B IT vs MiniCPM5-2B
- Gemma 4 31B IT vs Granite 4.2 8B
- Gemma 4 31B IT vs MiniCPM5-2B
- Gemma 4 E2B IT vs Granite 4.2 8B
- Gemma 4 E2B IT vs MiniCPM5-2B
- Gemma 4 E4B IT vs Granite 4.2 8B
- Gemma 4 E4B IT vs MiniCPM5-2B
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs MiniCPM5-2B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs MiniCPM5-2B
- GLM-5.3-Flash vs Granite 4.2 8B
- GLM-5.3-Flash vs MiniCPM5-2B
- GLM-5.3 vs Granite 4.2 8B
- GLM-5.3 vs MiniCPM5-2B
- gpt-oss-120b vs Granite 4.2 8B
- gpt-oss-120b vs MiniCPM5-2B
- gpt-oss-20b vs Granite 4.2 8B
- gpt-oss-20b vs MiniCPM5-2B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 3B vs Granite 4.2 8B
- Granite 4.2 3B vs MiniCPM5-2B
- Granite 4.2 8B vs Hy-Memory-30B-A3B
- Granite 4.2 8B vs Hy3
- Granite 4.2 8B vs Inkling
- Granite 4.2 8B vs Inkling-Small
- Granite 4.2 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 8B vs Kimi K3
- Granite 4.2 8B vs Kimi K3.1 Pro
- Granite 4.2 8B vs Kolibri-1
- Granite 4.2 8B vs Kronos
- Granite 4.2 8B vs Laguna M.1
- Granite 4.2 8B vs Laguna S 2.1
- Granite 4.2 8B vs Laguna XS 2.1
- Granite 4.2 8B vs LBS-27B
- Granite 4.2 8B vs LFM2.5-2.6B
- Granite 4.2 8B vs LFM3-1.7B
- Granite 4.2 8B vs LFM3-Audio-2B
- Granite 4.2 8B vs LFM3-Omni-2B
- Granite 4.2 8B vs LFM3-VL-2B
- Granite 4.2 8B vs Ling-3.0-tiny
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 8B vs MiniCPM-o 4.5
- Granite 4.2 8B vs MiniCPM-V 4.7 1B
- Granite 4.2 8B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 8B vs MiniMax-M2.5
- Granite 4.2 8B vs MiniMax-M2.7
- Granite 4.2 8B vs MiniMax-M3.1-Flash
- Granite 4.2 8B vs Mistral Large 4
- Granite 4.2 8B vs Muse Glimmer 30B
- Granite 4.2 8B vs Nanbeige4.3-33B
- Granite 4.2 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 8B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 4B
- Granite 4.2 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 8B vs Nemotron 3 Super 120B A12B
- Granite 4.2 8B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 8B vs occamy-1.0
- Granite 4.2 8B vs Olmo 3 7B Think
- Granite 4.2 8B vs Ornith-1.5-122B
- Granite 4.2 8B vs Ornith-1.5-35B-A3B
- Granite 4.2 8B vs Ornith-1.5-397B
- Granite 4.2 8B vs Ornith-1.5-9B
- Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 8B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 8B vs Qwen3-4B-Instruct-2507
- Granite 4.2 8B vs Qwen3.5-0.8B
- Granite 4.2 8B vs Qwen3.5-122B-A10B
- Granite 4.2 8B vs Qwen3.5-2B
- Granite 4.2 8B vs Qwen3.5-397B-A17B
- Granite 4.2 8B vs Qwen3.5-4B
- Granite 4.2 8B vs Qwen3.5-9B
- Granite 4.2 8B vs Qwen3.6-27B
- Granite 4.2 8B vs Qwen3.6-35B-A3B
- Granite 4.2 8B vs Qwen3.8-27B
- Granite 4.2 8B vs Qwen3.8-Flash-Next
- Granite 4.2 8B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-Coder-Next
- Granite 4.2 8B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-VL-2B-Instruct
- Granite 4.2 8B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-VL-32B-Instruct
- Granite 4.2 8B vs Qwen3-VL-4B-Instruct
- Granite 4.2 8B vs Qwen3-VL-8B-Instruct
- Granite 4.2 8B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 8B vs Recursive Flint
- Granite 4.2 8B vs Ripple-65B Preview
- Granite 4.2 8B vs SmolLM3-3B
- Granite 4.2 8B vs Spark-X2.6-4B
- Granite 4.2 8B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 8B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 8B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 8B vs Baidu Unlimited-OCR-2
- Hy-Memory-30B-A3B vs MiniCPM5-2B
- Hy3 vs MiniCPM5-2B
- Inkling-Small vs MiniCPM5-2B
- Inkling vs MiniCPM5-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM5-2B
- Kimi K3.1 Pro vs MiniCPM5-2B
- Kimi K3 vs MiniCPM5-2B
- Kolibri-1 vs MiniCPM5-2B
- Kronos vs MiniCPM5-2B
- Laguna M.1 vs MiniCPM5-2B
- Laguna S 2.1 vs MiniCPM5-2B
- Laguna XS 2.1 vs MiniCPM5-2B
- LBS-27B vs MiniCPM5-2B
- LFM2.5-2.6B vs MiniCPM5-2B
- LFM3-1.7B vs MiniCPM5-2B
- LFM3-Audio-2B vs MiniCPM5-2B
- LFM3-Omni-2B vs MiniCPM5-2B
- LFM3-VL-2B vs MiniCPM5-2B
- Ling-3.0-tiny vs MiniCPM5-2B
- Ling-3.1-flash-1019 vs MiniCPM5-2B
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM5-2B
- MiniCPM-o 4.5 vs MiniCPM5-2B
- MiniCPM-V 4.7 1B vs MiniCPM5-2B
- MiniCPM-V 4.7 35B-A3B vs MiniCPM5-2B
- MiniCPM5-2B vs MiniMax-M2.5
- MiniCPM5-2B vs MiniMax-M2.7
- MiniCPM5-2B vs MiniMax-M3.1-Flash
- MiniCPM5-2B vs Mistral Large 4
- MiniCPM5-2B vs Muse Glimmer 30B
- MiniCPM5-2B vs Nanbeige4.3-33B
- MiniCPM5-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiniCPM5-2B vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM5-2B vs Nemotron 3 Nano 30B A3B
- MiniCPM5-2B vs Nemotron 3 Nano 4B
- MiniCPM5-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiniCPM5-2B vs Nemotron 3 Super 120B A12B
- MiniCPM5-2B vs Nemotron 3 Ultra 550B A55B
- MiniCPM5-2B vs occamy-1.0
- MiniCPM5-2B vs Olmo 3 7B Think
- MiniCPM5-2B vs Ornith-1.5-122B
- MiniCPM5-2B vs Ornith-1.5-35B-A3B
- MiniCPM5-2B vs Ornith-1.5-397B
- MiniCPM5-2B vs Ornith-1.5-9B
- MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B
- MiniCPM5-2B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM5-2B vs Qwen3-4B-Instruct-2507
- MiniCPM5-2B vs Qwen3.5-0.8B
- MiniCPM5-2B vs Qwen3.5-122B-A10B
- MiniCPM5-2B vs Qwen3.5-2B
- MiniCPM5-2B vs Qwen3.5-397B-A17B
- MiniCPM5-2B vs Qwen3.5-4B
- MiniCPM5-2B vs Qwen3.5-9B
- MiniCPM5-2B vs Qwen3.6-27B
- MiniCPM5-2B vs Qwen3.6-35B-A3B
- MiniCPM5-2B vs Qwen3.8-27B
- MiniCPM5-2B vs Qwen3.8-Flash-Next
- MiniCPM5-2B vs Qwen3-Coder-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-Coder-Next
- MiniCPM5-2B vs Qwen3-Omni-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-VL-2B-Instruct
- MiniCPM5-2B vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-VL-32B-Instruct
- MiniCPM5-2B vs Qwen3-VL-4B-Instruct
- MiniCPM5-2B vs Qwen3-VL-8B-Instruct
- MiniCPM5-2B vs Qwopus3.8-27B-Flash-V2
- MiniCPM5-2B vs Recursive Flint
- MiniCPM5-2B vs Ripple-65B Preview
- MiniCPM5-2B vs SmolLM3-3B
- MiniCPM5-2B vs Spark-X2.6-4B
- MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B
- MiniCPM5-2B vs Swift 1.5 Qwen3.8-Flash-Next
- MiniCPM5-2B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM5-2B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-minicpm5-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-8b.json·/api/v1/tools/minicpm5-2b.json - From a terminal
anchor compare granite-4-2-8b minicpm5-2b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "minicpm5-2b"}at/mcp, no key