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

Gemma 4 26B A4B IT vs MiniCPM-V 4.7 35B-A3B

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (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 6359 open weights comparisons

Gemma 4 26B A4B ITGoogle DeepMind · Open-weight model BB72.2/100#5 of 160 open-weight models MiniCPM-V 4.7 35B-A3BOpenBMB · Open-weight model C61/100#77 of 160 open-weight models

Which one, for what

Gemma 4 26B A4B IT BB

Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context

Ahead on

  • Risk, 70 against 55
  • Agent interface, 85 against 70

Also in its favour

  • Agent-ready, a grade of BB or better

Watch for

No safety evaluation figures published

MiniCPM-V 4.7 35B-A3B C

Best for Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence

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

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.

Risk Gemma 4 26B A4B IT +15

Gemma 4 26B A4B IT 70

File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.

MiniCPM-V 4.7 35B-A3B 55

File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.

Agent interface Gemma 4 26B A4B IT +15

Gemma 4 26B A4B IT 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.

MiniCPM-V 4.7 35B-A3B 70

Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.

Score by category

CategoryWeight this runGemma 4 26B A4B ITMiniCPM-V 4.7 35B-A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259090even
Risk20%207055Gemma 4 26B A4B IT +15
Agent interface10%108570Gemma 4 26B A4B IT +15
Documentation and upkeep10%106568MiniCPM-V 4.7 35B-A3B +3
Negative events≤1500
TotalBB 72.2/100C 61/100

Facts side by side

FactGemma 4 26B A4B ITMiniCPM-V 4.7 35B-A3B
KindOpen-weight modelOpen-weight model
VendorGoogle DeepMindOpenBMB
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-07-202026-11-03
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3.8/5 (4)none

Verdicts

Gemma 4 26B A4B IT

A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.

MiniCPM-V 4.7 35B-A3B

A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.

Before you call either

Gemma 4 26B A4B IT

  1. On the reference machine run llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images
  2. With Ollama run ollama run gemma4:26b and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K
  3. In vLLM or SGLang pass --tool-call-parser gemma4 --reasoning-parser gemma4 (vLLM also --enable-auto-tool-choice), or tool calls come back as text
  4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
  5. Validate tool names and arguments before running them, and check any JSON yourself

MiniCPM-V 4.7 35B-A3B

  1. Start it with llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja, or ollama run openbmb/minicpm-v4.7:35b-a3b
  2. For image input in llama.cpp add mmproj-model-f16.gguf, or the 4x projector for more visual detail at a higher cost
  3. The GGUF files at the repository root have thinking off. Use the files in thinking/ when you want the trace
  4. With Transformers pass enable_thinking to apply_chat_template, and pass downsample_mode to both apply_chat_template and generate
  5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
  6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format
  7. Validate JSON output yourself, since structured output isn't documented

Questions

Which is better for AI agents, Gemma 4 26B A4B IT or MiniCPM-V 4.7 35B-A3B?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories.

Other comparisons with Gemma 4 26B A4B IT or MiniCPM-V 4.7 35B-A3B

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