# DiffusionGemma 26B A4B IT vs Inkling-Small > Inkling-Small scores 66.5 (B) 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-inkling-small - Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.md (~6,650 tokens) - Slim: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.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 Inkling-Small scores 66.5 (B) 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 - Inkling-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.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 ### Inkling-Small (B) Good for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking. Ahead on: - Capability, 63 against 30 - Reach, 55 against 50 - Agent interface, 85 against 70 - Documentation and upkeep, 82 against 60 Also in its favour: - Rated higher by the reviewer agents, 3.5 against 2.5 out of 5 Watch for: The smallest build Thinking Machines publishes needs about 205 GB ## Score by category | Category | Weight | DiffusionGemma 26B A4B IT | Inkling-Small | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 30 | 63 | Inkling-Small +33 | | Reach | 25% (25 this run) | 50 | 55 | Inkling-Small +5 | | Risk | 20% (20 this run) | 70 | 70 | even | | Agent interface | 10% (10 this run) | 70 | 85 | Inkling-Small +15 | | Documentation and upkeep | 10% (10 this run) | 60 | 82 | Inkling-Small +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **50 · D** | **66.5 · B** | | ## Facts side by side | Fact | DiffusionGemma 26B A4B IT | Inkling-Small | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Google DeepMind | Thinking Machines Lab | | 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 | 2026-07-31 | | 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. **Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside 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 ``, 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 ### Inkling-Small 1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4 2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself 3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh 4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens 5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error 6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card 7. Use guided decoding or validate JSON yourself, since structured output isn't documented 8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal ## Questions ### Which is better for AI agents, DiffusionGemma 26B A4B IT or Inkling-Small? Inkling-Small scores 66.5 (B) 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-inkling-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "inkling-small"}`. 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