# DiffusionGemma 26B A4B IT vs Gemma 4 31B IT > Gemma 4 31B IT scores 74.1 (BB) 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-4-31b-it - Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.md (~6,600 tokens) - Slim: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-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-4-31b-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 31B IT scores 74.1 (BB) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories. 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 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-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 Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band ### Gemma 4 31B IT (BB) Good for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence Ahead on: - Capability, 61 against 30 - Reach, 95 against 50 - Agent interface, 85 against 70 - Documentation and upkeep, 65 against 60 Also in its favour: - Agent-ready, a grade of BB or better - Rated higher by the reviewer agents, 3.8 against 2.5 out of 5 Watch for: No safety evaluation results published ## Score by category | Category | Weight | DiffusionGemma 26B A4B IT | Gemma 4 31B IT | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 30 | 61 | Gemma 4 31B IT +31 | | Reach | 25% (25 this run) | 50 | 95 | Gemma 4 31B IT +45 | | Risk | 20% (20 this run) | 70 | 70 | even | | Agent interface | 10% (10 this run) | 70 | 85 | Gemma 4 31B IT +15 | | Documentation and upkeep | 10% (10 this run) | 60 | 65 | Gemma 4 31B IT +5 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **50 · D** | **74.1 · BB** | | ## Facts side by side | Fact | DiffusionGemma 26B A4B IT | Gemma 4 31B 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) | 3.8/5 (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 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July. ## 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 ``, 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 31B IT 1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images 2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build 3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does 4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block 5. Drop earlier thoughts from the history except on tool-call turns, as the card asks 6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64 7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding 8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine ## Questions ### Which is better for AI agents, DiffusionGemma 26B A4B IT or Gemma 4 31B IT? Gemma 4 31B IT scores 74.1 (BB) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "gemma-4-31b-it"}`. 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