# DiffusionGemma 26B A4B IT vs GLM-4.7-Flash > GLM-4.7-Flash scores 63 (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-glm-4-7-flash - Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.md (~6,600 tokens) - Slim: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.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 GLM-4.7-Flash scores 63 (B) 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 risk and documentation and upkeep. 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 - GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.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: - Risk, 70 against 55 - Documentation and upkeep, 60 against 28 Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band ### GLM-4.7-Flash (B) Good for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime Ahead on: - Capability, 59 against 30 - Reach, 80 against 50 - Agent interface, 85 against 70 Watch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January ## Score by category | Category | Weight | DiffusionGemma 26B A4B IT | GLM-4.7-Flash | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 30 | 59 | GLM-4.7-Flash +29 | | Reach | 25% (25 this run) | 50 | 80 | GLM-4.7-Flash +30 | | Risk | 20% (20 this run) | 70 | 55 | DiffusionGemma 26B A4B IT +15 | | Agent interface | 10% (10 this run) | 70 | 85 | GLM-4.7-Flash +15 | | Documentation and upkeep | 10% (10 this run) | 60 | 28 | DiffusionGemma 26B A4B IT +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **50 · D** | **63 · B** | | ## Facts side by side | Fact | DiffusionGemma 26B A4B IT | GLM-4.7-Flash | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Google DeepMind | Z.ai (Zhipu AI) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | MIT | | Read-only variant documented | no | no | | llms.txt | yes | no | | Last release | 2026-07-15 | 2026-01-29 | | 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) | 2.8/5 (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. **GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no 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 ### GLM-4.7-Flash 1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash` 2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build 3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text 4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026 5. Pass `chat_template_kwargs: {"enable_thinking": false}` for short tasks, since thinking is on by default 6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench 7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000 ## Questions ### Which is better for AI agents, DiffusionGemma 26B A4B IT or GLM-4.7-Flash? GLM-4.7-Flash scores 63 (B) 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 risk and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "glm-4-7-flash"}`. 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