# DiffusionGemma 26B A4B IT vs Gemma 4 E2B IT

> Gemma 4 E2B IT scores 60.4 (C) to DiffusionGemma 26B A4B IT's 50 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it
- Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Gemma 4 E2B IT scores 60.4 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 3 of 5 scored categories. DiffusionGemma 26B A4B IT leads on capability. Both do open-weight models you run yourself.

- DiffusionGemma 26B A4B IT: grade D, 50/100, rank #94 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json
- Gemma 4 E2B IT: grade C, 60.4/100, rank #54 of 100 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
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### DiffusionGemma 26B A4B IT (D)

Good for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4

Ahead on:
- Capability, 30 against 19

Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band

### 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:
- Reach, 95 against 50
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 60

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5

Watch for: No safety evaluation figures published


## Score by category

| Category | Weight | DiffusionGemma 26B A4B IT | Gemma 4 E2B IT | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 30 | 19 | DiffusionGemma 26B A4B IT +11 |
| Reach | 25% (25 this run) | 50 | 95 | Gemma 4 E2B IT +45 |
| Risk | 20% (20 this run) | 70 | 70 | even |
| Agent interface | 10% (10 this run) | 70 | 85 | Gemma 4 E2B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 60 | 75 | Gemma 4 E2B IT +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50 · D** | **60.4 · C** | |

## Facts side by side

| Fact | DiffusionGemma 26B A4B IT | Gemma 4 E2B IT |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Google DeepMind |
| 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 | yes |
| Last release | 2026-07-15 | 2026-07-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.5/5 (4) | 3.5/5 (4) |

## Verdicts

**DiffusionGemma 26B A4B IT.** A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.

**Gemma 4 E2B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading 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, with no published safety results.

## Before you call either

### DiffusionGemma 26B A4B IT

1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions
2. In Transformers, install `transformers>=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype="auto"`; generation_config.json already holds the recommended sampler
3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `<|think|>` at the start of the system prompt, read the reasoning between `<|channel>thought` and `<channel|>`, and drop it from history before the next turn
4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself
5. Streaming arrives one 256-token canvas at a time, not token by token
6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs

### 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

## Questions

### Which is better for AI agents, DiffusionGemma 26B A4B IT or Gemma 4 E2B IT?

Gemma 4 E2B IT scores 60.4 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 3 of 5 scored categories. DiffusionGemma 26B A4B IT leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "gemma-4-e2b-it"}`. From a terminal: `anchor compare diffusiongemma-26b-a4b-it gemma-4-e2b-it`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json

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