Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM5-2B vs North Mini Code 1.0
North Mini Code 1.0 scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 2 of 4 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep. 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 On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data
Ahead on
- Reach, 90 against 85
- Documentation and upkeep, 92 against 36
Watch for
OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning
Best for Developers who want a coding agent on their own GPU with a permissive licence
Ahead on
- Risk, 60 against 55
- Agent interface, 85 against 75
Watch for
No limitations, safety results, languages or data description on the card
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 MiniCPM5-2B +56
MiniCPM5-2B 92
Model card 17, everything but a knowledge cut-off. Training data 20, all eight training datasets are published on Hugging Face and named per stage with sizes. Last change 15, a template fix on 29 September 2026, 36 days ago, with nothing newer in OpenBMB's MiniCPM5 repositories on Hugging Face (builds last changed 12 September) or its GitHub repository (21 September). Responsiveness 20, OpenBMB members answered 16 of the 23 questions in the discussions, the newest reply on 29 September. Version policy 10, earlier models and checkpoints sit in their own repositories and the GitHub changelog dates each release. File health 10, config, tokenizer, template and card agree and no broken-file report waited on 21 October, though OpenBMB's GGUF, MLX and GPTQ repositories still carry the template from before the 29 September fix.
North Mini Code 1.0 36
Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.
Agent interface North Mini Code 1.0 +10
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.
North Mini Code 1.0 85
Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Reach MiniCPM5-2B +5
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.
North Mini Code 1.0 85
Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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. 25 + 20 + 15 + 15 + 10 + 0 = 85.
Risk North Mini Code 1.0 +5
MiniCPM5-2B 55
File safety 25, safetensors and GGUF only, and the documented way to load it runs no code from the repository. Provenance 15, the openbmb organisation linked from OpenBMB's GitHub, with a SHA-256 for every weight file. Safety evaluation 0, nothing published. Agent guidance 5, the card only cautions that prompts can manipulate the model and that users should add their own safeguards. Licence risk 10, 5 off for the card's use limit beyond the law. Security programme 0, no SECURITY.md, no security.txt and no advisories.
North Mini Code 1.0 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.
Score by category
| Category | Weight this run | MiniCPM5-2B | North Mini Code 1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | MiniCPM5-2B +5 |
| Risk | 20%20 | 55 | 60 | North Mini Code 1.0 +5 |
| Agent interface | 10%10 | 75 | 85 | North Mini Code 1.0 +10 |
| Documentation and upkeep | 10%10 | 92 | 36 | MiniCPM5-2B +56 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.7/100 | C 61/100 |
Facts side by side
| Fact | MiniCPM5-2B | North Mini Code 1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | Cohere |
| 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 | 2026-09-29 | 2026-07-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3/5 (4) | none |
How the scores have moved
North Mini Code 1.0
No score change since it was listed.
Verdicts
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.
North Mini Code 1.0
A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.
Before you call either
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
North Mini Code 1.0
- Serve it with vLLM 0.33.0 or later and
--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice, withcohere_melodyinstalled - Pass the model's thinking back with its tool calls on every turn. The card says results drop without it
- Describe tools in JSON schema and append tool results as a dictionary with the tool role
- Use temperature 1.0 and top_p 0.95
- For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's
- It's text only. Don't send images
Questions
Which is better for AI agents, MiniCPM5-2B or North Mini Code 1.0?
North Mini Code 1.0 scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 2 of 4 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep.
Other comparisons with MiniCPM5-2B or North Mini Code 1.0
- Agents-A1-4B vs MiniCPM5-2B
- Agents-A1-4B vs North Mini Code 1.0
- Apertus v1.5 8B vs MiniCPM5-2B
- Apertus v1.5 8B vs North Mini Code 1.0
- Apodex-1.1-mini vs MiniCPM5-2B
- Apodex-1.1-mini vs North Mini Code 1.0
- Arete-27B vs MiniCPM5-2B
- Arete-27B vs North Mini Code 1.0
- BigBang-v1 vs MiniCPM5-2B
- BigBang-v1 vs North Mini Code 1.0
- Carbon-X vs MiniCPM5-2B
- Carbon-X vs North Mini Code 1.0
- Cola2 vs MiniCPM5-2B
- Cola2 vs North Mini Code 1.0
- Cosmos-Reason2-2B vs MiniCPM5-2B
- Cosmos-Reason2-2B vs North Mini Code 1.0
- Darwin-36B-Opus vs MiniCPM5-2B
- Darwin-36B-Opus vs North Mini Code 1.0
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs North Mini Code 1.0
- DeepSeek V4.1 Flash vs MiniCPM5-2B
- DeepSeek V4.1 Flash vs North Mini Code 1.0
- DeepSeek-V4-Flash-0731 vs MiniCPM5-2B
- DeepSeek-V4-Flash-0731 vs North Mini Code 1.0
- Devstral Small 2 vs MiniCPM5-2B
- Devstral Small 2 vs North Mini Code 1.0
- DiffusionGemma 26B A4B IT vs MiniCPM5-2B
- DiffusionGemma 26B A4B IT vs North Mini Code 1.0
- EXAONE 4.5 33B vs MiniCPM5-2B
- EXAONE 4.5 33B vs North Mini Code 1.0
- Gemma 4 12B IT vs MiniCPM5-2B
- Gemma 4 12B IT vs North Mini Code 1.0
- Gemma 4 26B A4B IT vs MiniCPM5-2B
- Gemma 4 26B A4B IT vs North Mini Code 1.0
- Gemma 4 31B IT vs MiniCPM5-2B
- Gemma 4 31B IT vs North Mini Code 1.0
- Gemma 4 E2B IT vs MiniCPM5-2B
- Gemma 4 E2B IT vs North Mini Code 1.0
- Gemma 4 E4B IT vs MiniCPM5-2B
- Gemma 4 E4B IT vs North Mini Code 1.0
- GLM-4.5-Air vs MiniCPM5-2B
- GLM-4.5-Air vs North Mini Code 1.0
- GLM-4.7-Flash vs MiniCPM5-2B
- GLM-4.7-Flash vs North Mini Code 1.0
- GLM-5.3-Flash vs MiniCPM5-2B
- GLM-5.3-Flash vs North Mini Code 1.0
- GLM-5.3 vs MiniCPM5-2B
- GLM-5.3 vs North Mini Code 1.0
- gpt-oss-120b vs MiniCPM5-2B
- gpt-oss-120b vs North Mini Code 1.0
- gpt-oss-20b vs MiniCPM5-2B
- gpt-oss-20b vs North Mini Code 1.0
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 30B vs North Mini Code 1.0
- Granite 4.2 3B vs MiniCPM5-2B
- Granite 4.2 3B vs North Mini Code 1.0
- Granite 4.2 8B vs MiniCPM5-2B
- Granite 4.2 8B vs North Mini Code 1.0
- Hy-Memory-30B-A3B vs MiniCPM5-2B
- Hy-Memory-30B-A3B vs North Mini Code 1.0
- Hy3 vs MiniCPM5-2B
- Hy3 vs North Mini Code 1.0
- Inkling-Small vs MiniCPM5-2B
- Inkling-Small vs North Mini Code 1.0
- Inkling vs MiniCPM5-2B
- Inkling vs North Mini Code 1.0
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM5-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs North Mini Code 1.0
- Kimi K3.1 Pro vs MiniCPM5-2B
- Kimi K3.1 Pro vs North Mini Code 1.0
- Kimi K3 vs MiniCPM5-2B
- Kimi K3 vs North Mini Code 1.0
- Kolibri-1 vs MiniCPM5-2B
- Kolibri-1 vs North Mini Code 1.0
- Kronos vs MiniCPM5-2B
- Kronos vs North Mini Code 1.0
- Laguna M.1 vs MiniCPM5-2B
- Laguna M.1 vs North Mini Code 1.0
- Laguna S 2.1 vs MiniCPM5-2B
- Laguna S 2.1 vs North Mini Code 1.0
- Laguna XS 2.1 vs MiniCPM5-2B
- Laguna XS 2.1 vs North Mini Code 1.0
- LBS-27B vs MiniCPM5-2B
- LBS-27B vs North Mini Code 1.0
- LFM2.5-2.6B vs MiniCPM5-2B
- LFM2.5-2.6B vs North Mini Code 1.0
- LFM3-1.7B vs MiniCPM5-2B
- LFM3-1.7B vs North Mini Code 1.0
- LFM3-Audio-2B vs MiniCPM5-2B
- LFM3-Audio-2B vs North Mini Code 1.0
- LFM3-Omni-2B vs MiniCPM5-2B
- LFM3-Omni-2B vs North Mini Code 1.0
- LFM3-VL-2B vs MiniCPM5-2B
- LFM3-VL-2B vs North Mini Code 1.0
- Ling-3.0-tiny vs MiniCPM5-2B
- Ling-3.0-tiny vs North Mini Code 1.0
- Ling-3.1-flash-1019 vs MiniCPM5-2B
- Ling-3.1-flash-1019 vs North Mini Code 1.0
- LongCat-2.5 vs MiniCPM5-2B
- LongCat-2.5 vs North Mini Code 1.0
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM5-2B
- MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0
- MiMo-V2.6-Flash-RL vs MiniCPM5-2B
- MiMo-V2.6-Flash-RL vs North Mini Code 1.0
- MiniCPM-o 4.5 vs MiniCPM5-2B
- MiniCPM-o 4.5 vs North Mini Code 1.0
- MiniCPM-V 4.7 1B vs MiniCPM5-2B
- MiniCPM-V 4.7 1B vs North Mini Code 1.0
- MiniCPM-V 4.7 35B-A3B vs MiniCPM5-2B
- MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0
- 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 Nanbeige4.2-3B
- 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 OTel-2.0-LLM-31B-IT
- 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 SenseNova-Loop-9B-Preview
- MiniCPM5-2B vs SmolLM3-3B
- MiniCPM5-2B vs Spark-X2.6-4B
- MiniCPM5-2B vs Step 3.7 Flash
- 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
- MiniCPM5-2B vs WeVisOne-VL-2B
- MiniCPM5-2B vs WeVisOne-VL-4B
- MiniCPM5-2B vs ZAYA1-8B
- MiniMax-M2.5 vs North Mini Code 1.0
- MiniMax-M2.7 vs North Mini Code 1.0
- MiniMax-M3.1-Flash vs North Mini Code 1.0
- Mistral Large 4 vs North Mini Code 1.0
- Muse Glimmer 30B vs North Mini Code 1.0
- Nanbeige4.2-3B vs North Mini Code 1.0
- Nanbeige4.3-33B vs North Mini Code 1.0
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs North Mini Code 1.0
- Nemotron 3.5 Lightning 30B A3B vs North Mini Code 1.0
- Nemotron 3 Nano 30B A3B vs North Mini Code 1.0
- Nemotron 3 Nano 4B vs North Mini Code 1.0
- Nemotron 3 Nano Omni 30B A3B Reasoning vs North Mini Code 1.0
- Nemotron 3 Super 120B A12B vs North Mini Code 1.0
- Nemotron 3 Ultra 550B A55B vs North Mini Code 1.0
- North Mini Code 1.0 vs occamy-1.0
- North Mini Code 1.0 vs Olmo 3 7B Think
- North Mini Code 1.0 vs Ornith-1.5-122B
- North Mini Code 1.0 vs Ornith-1.5-35B-A3B
- North Mini Code 1.0 vs Ornith-1.5-397B
- North Mini Code 1.0 vs Ornith-1.5-9B
- North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT
- North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B
- North Mini Code 1.0 vs Qwen3-30B-A3B-Instruct-2507
- North Mini Code 1.0 vs Qwen3-4B-Instruct-2507
- North Mini Code 1.0 vs Qwen3.5-0.8B
- North Mini Code 1.0 vs Qwen3.5-122B-A10B
- North Mini Code 1.0 vs Qwen3.5-2B
- North Mini Code 1.0 vs Qwen3.5-397B-A17B
- North Mini Code 1.0 vs Qwen3.5-4B
- North Mini Code 1.0 vs Qwen3.5-9B
- North Mini Code 1.0 vs Qwen3.6-27B
- North Mini Code 1.0 vs Qwen3.6-35B-A3B
- North Mini Code 1.0 vs Qwen3.8-27B
- North Mini Code 1.0 vs Qwen3.8-Flash-Next
- North Mini Code 1.0 vs Qwen3-Coder-30B-A3B-Instruct
- North Mini Code 1.0 vs Qwen3-Coder-Next
- North Mini Code 1.0 vs Qwen3-Omni-30B-A3B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-2B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-30B-A3B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-32B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-4B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-8B-Instruct
- North Mini Code 1.0 vs Qwopus3.8-27B-Flash-V2
- North Mini Code 1.0 vs Recursive Flint
- North Mini Code 1.0 vs Ripple-65B Preview
- North Mini Code 1.0 vs SenseNova-Loop-9B-Preview
- North Mini Code 1.0 vs SmolLM3-3B
- North Mini Code 1.0 vs Spark-X2.6-4B
- North Mini Code 1.0 vs Step 3.7 Flash
- North Mini Code 1.0 vs Swift 1.5 Qwen3.8-27B
- North Mini Code 1.0 vs Swift 1.5 Qwen3.8-Flash-Next
- North Mini Code 1.0 vs ThinkingCap (Qwen3.8 27B)
- North Mini Code 1.0 vs Baidu Unlimited-OCR-2
- North Mini Code 1.0 vs WeVisOne-VL-2B
- North Mini Code 1.0 vs WeVisOne-VL-4B
- North Mini Code 1.0 vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/minicpm5-2b-vs-north-mini-code-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm5-2b.json·/api/v1/tools/north-mini-code-1-0.json - From a terminal
anchor compare minicpm5-2b north-mini-code-1-0(the CLI) - Over MCP
compare_tools {"a": "minicpm5-2b", "b": "north-mini-code-1-0"}at/mcp, no key