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Head to head · Open-weight models you run yourself · October 2026 research run

Agents-A1-4B vs MiniCPM-V 4.7 1B

Agents-A1-4B and MiniCPM-V 4.7 1B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-V 4.7 1B 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 4969 open weights comparisons

Agents-A1-4BInternScience (Shanghai AI Laboratory) · Open-weight model C61/100#39 of 135 open-weight models MiniCPM-V 4.7 1BOpenBMB · Open-weight model C61/100#68 of 135 open-weight models

Which one, for what

Agents-A1-4B C

Best for Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

The base isn't declared, and the technical report doesn't cover the 4B

MiniCPM-V 4.7 1B C

Best 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

  • Reach, 95 against 90
  • Documentation and upkeep, 48 against 43

Watch for

No limitations section, safety results, agent-use guidance or security contact

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.

Reach MiniCPM-V 4.7 1B +5

Agents-A1-4B 90

Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two.

MiniCPM-V 4.7 1B 95

Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.

Documentation and upkeep MiniCPM-V 4.7 1B +5

Agents-A1-4B 43

Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.

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.

Score by category

CategoryWeight this runAgents-A1-4BMiniCPM-V 4.7 1BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259095MiniCPM-V 4.7 1B +5
Risk20%205555even
Agent interface10%107070even
Documentation and upkeep10%104348MiniCPM-V 4.7 1B +5
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactAgents-A1-4BMiniCPM-V 4.7 1B
KindOpen-weight modelOpen-weight model
VendorInternScience (Shanghai AI Laboratory)OpenBMB
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-07-142026-10-30
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3/5 (4)none

Verdicts

Agents-A1-4B

A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.

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.

Before you call either

Agents-A1-4B

  1. Quickest start is llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja, about 3.4 GB with the vision projector
  2. With vLLM use --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, as on the card, and add --language-model-only for text-only work
  3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14
  4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them
  5. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding
  7. Validate JSON output yourself, since structured output isn't documented

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

Questions

Which is better for AI agents, Agents-A1-4B or MiniCPM-V 4.7 1B?

Agents-A1-4B and MiniCPM-V 4.7 1B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-V 4.7 1B leads on reach and documentation and upkeep.

Other comparisons with Agents-A1-4B or MiniCPM-V 4.7 1B

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