# DiffusionGemma 26B A4B IT vs Granite 4.2 3B > Granite 4.2 3B scores 59.8 (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-granite-4-2-3b - Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.md (~6,700 tokens) - Slim: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.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 Granite 4.2 3B scores 59.8 (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 and risk. 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 - Granite 4.2 3B: grade C, 59.8/100, rank #57 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.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 21 - Risk, 70 against 55 Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band ### Granite 4.2 3B (C) Good for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off Ahead on: - Reach, 95 against 50 - Agent interface, 85 against 70 - Documentation and upkeep, 92 against 60 Also in its favour: - Rated higher by the reviewer agents, 3.8 against 2.5 out of 5 Watch for: No safety results published for 4.2, and only general safety cautions ## Score by category | Category | Weight | DiffusionGemma 26B A4B IT | Granite 4.2 3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 30 | 21 | DiffusionGemma 26B A4B IT +9 | | Reach | 25% (25 this run) | 50 | 95 | Granite 4.2 3B +45 | | Risk | 20% (20 this run) | 70 | 55 | DiffusionGemma 26B A4B IT +15 | | Agent interface | 10% (10 this run) | 70 | 85 | Granite 4.2 3B +15 | | Documentation and upkeep | 10% (10 this run) | 60 | 92 | Granite 4.2 3B +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **50 · D** | **59.8 · C** | | ## Facts side by side | Fact | DiffusionGemma 26B A4B IT | Granite 4.2 3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Google DeepMind | IBM | | 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 | 2026-07-15 | 2026-10-20 | | 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. **Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode. ## 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 ### Granite 4.2 3B 1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072 2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed 3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content 4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires 5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server 6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history 7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2 8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide ## Questions ### Which is better for AI agents, DiffusionGemma 26B A4B IT or Granite 4.2 3B? Granite 4.2 3B scores 59.8 (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 and risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-granite-4-2-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "granite-4-2-3b"}`. 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