# MiniCPM-V 4.7 1B vs Ornith-1.5-9B > MiniCPM-V 4.7 1B and Ornith-1.5-9B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-9b - Markdown: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-9b.md (~9,400 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 MiniCPM-V 4.7 1B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on agent interface. Both do open-weight models you run yourself. - MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json - Ornith-1.5-9B: grade C, 61/100, rank #51 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### MiniCPM-V 4.7 1B (C) Good for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model Ahead on: - Risk, 55 against 50 - Documentation and upkeep, 48 against 31 Watch for: No limitations section, safety results, agent-use guidance or security contact ### Ornith-1.5-9B (C) Good for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required Ahead on: - Agent interface, 85 against 70 Watch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on ## Why the edge ### Documentation and upkeep: MiniCPM-V 4.7 1B +17 - MiniCPM-V 4.7 1B (48): Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48. - Ornith-1.5-9B (31): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 15, the newest change to the model's files was Ornith's NVFP4 build on 26 August 2026, 69 days ago, and no card of the model or its builds has changed since. Responsiveness 0, Ornith answered none of the 15 questions and reports. Version policy 0, the fourth safetensors shard and every GGUF file were replaced in place on 23 and 24 August with no note. File health 0, the card links a missing LICENSE and an unpublished Mobile build, and the two chat templates differ. 11 + 5 + 15 + 0 + 0 + 0 = 31. ### Agent interface: Ornith-1.5-9B +15 - MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70. - Ornith-1.5-9B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native. ### Risk: MiniCPM-V 4.7 1B +5 - MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55. - Ornith-1.5-9B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact. ## Score by category | Category | Weight | MiniCPM-V 4.7 1B | Ornith-1.5-9B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 95 | even | | Risk | 20% (20 this run) | 55 | 50 | MiniCPM-V 4.7 1B +5 | | Agent interface | 10% (10 this run) | 70 | 85 | Ornith-1.5-9B +15 | | Documentation and upkeep | 10% (10 this run) | 48 | 31 | MiniCPM-V 4.7 1B +17 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | MiniCPM-V 4.7 1B | Ornith-1.5-9B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | Ornith | | 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 | 2026-10-30 | 2026-08-23 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.3/5 (4) | ## Verdicts **MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back. **Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies. ## Before you call either ### MiniCPM-V 4.7 1B 1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b` 2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector 3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace 4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends 5. Check tool calls in your runtime first, since the card names no parser 6. Validate JSON output yourself, since structured output isn't documented ### Ornith-1.5-9B 1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file 2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed 3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention 4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it 5. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card 6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp 7. Validate JSON output yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, MiniCPM-V 4.7 1B or Ornith-1.5-9B? MiniCPM-V 4.7 1B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-9b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm-v-4-7-1b", "b": "ornith-1-5-9b"}`. 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