# Gemma 4 E2B IT vs MiniCPM-V 4.7 1B

> MiniCPM-V 4.7 1B and Gemma 4 E2B IT score within a point of each other for open-weight models you run yourself, 61 and 60.4 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b.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 <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. 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 Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Gemma 4 E2B IT: grade C, 60.4/100, rank #88 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e2b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json
- 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
- 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

### Gemma 4 E2B IT (C)

Good for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning

Ahead on:
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 48

Watch for: No safety evaluation figures published

### 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

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


## Why the edge

### Documentation and upkeep: Gemma 4 E2B IT +27

- Gemma 4 E2B IT (75): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.
- 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.

### Risk: Gemma 4 E2B IT +15

- Gemma 4 E2B 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 programme give a route to report a flaw, but no public Gemma advisories were found.
- 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.

### Agent interface: Gemma 4 E2B IT +15

- Gemma 4 E2B 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, and ships a response schema for Transformers' parser. 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, 131,072 tokens native, which meets the 128K line.
- 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.

## Score by category

| Category | Weight | Gemma 4 E2B IT | MiniCPM-V 4.7 1B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 95 | even |
| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 E2B IT +15 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E2B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 75 | 48 | Gemma 4 E2B IT +27 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.4 · C** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 E2B IT | MiniCPM-V 4.7 1B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | 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 | yes | no |
| Last release | 2026-11-03 | 2026-10-30 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.5/5 (4) | none |

## Verdicts

**Gemma 4 E2B IT.** A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.

**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

### Gemma 4 E2B IT

1. On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio
2. With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small 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; Google's w4a16 build is the 4-bit route there
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. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them

### 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, Gemma 4 E2B IT or MiniCPM-V 4.7 1B?

MiniCPM-V 4.7 1B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e2b-it", "b": "minicpm-v-4-7-1b"}`. From a terminal: `anchor compare gemma-4-e2b-it minicpm-v-4-7-1b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json

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- [MiniCPM-V 4.7 1B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct.md)
- [MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.md)
- [MiniCPM-V 4.7 1B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2.md)
- [MiniCPM-V 4.7 1B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.md)
- [MiniCPM-V 4.7 1B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview.md)
- [MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md)
- [MiniCPM-V 4.7 1B vs SmolLM3-3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b.md)
- [MiniCPM-V 4.7 1B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.md)
- [MiniCPM-V 4.7 1B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash.md)
- [MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b.md)
- [MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next.md)
- [MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b.md)
- [MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.md)
- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)
- [MiniCPM-V 4.7 1B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.md)
- [MiniCPM-V 4.7 1B vs ZAYA1-8B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.md)

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