# Gemma 4 26B A4B IT vs MiniCPM5-2B > Gemma 4 26B A4B IT scores 72.2 (BB) to MiniCPM5-2B's 60.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b - Markdown: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.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 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on documentation and upkeep. Both do open-weight models you run yourself. - Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json - MiniCPM5-2B: grade C, 60.2/100, rank #56 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.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 ### Gemma 4 26B A4B IT (BB) Good for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context Ahead on: - Capability, 59 against 27 - Risk, 70 against 55 - Agent interface, 85 against 75 Also in its favour: - Agent-ready, a grade of BB or better - Rated higher by the reviewer agents, 3.8 against 3.0 out of 5 Watch for: No safety evaluation figures published ### MiniCPM5-2B (C) Good for: On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data Ahead on: - Documentation and upkeep, 97 against 65 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ## Score by category | Category | Weight | Gemma 4 26B A4B IT | MiniCPM5-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 59 | 27 | Gemma 4 26B A4B IT +32 | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 70 | 55 | Gemma 4 26B A4B IT +15 | | Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 26B A4B IT +10 | | Documentation and upkeep | 10% (10 this run) | 65 | 97 | MiniCPM5-2B +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **72.2 · BB** | **60.2 · C** | | ## Facts side by side | Fact | Gemma 4 26B A4B IT | MiniCPM5-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Google DeepMind | OpenBMB | | 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 | no | no | | Last release | 2026-07-20 | 2026-09-29 | | 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 | 3.8/5 (4) | 3/5 (4) | ## Verdicts **Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports. **MiniCPM5-2B.** A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0. ## Before you call either ### Gemma 4 26B A4B IT 1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images 2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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 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. Validate tool names and arguments before running them, and check any JSON yourself ### MiniCPM5-2B 1. Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m) 2. On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text 3. On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls 4. Tool calls are XML, ``, not Hermes JSON 5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does 6. Expect a `` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented 8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix ## Questions ### Which is better for AI agents, Gemma 4 26B A4B IT or MiniCPM5-2B? Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-26b-a4b-it", "b": "minicpm5-2b"}`. 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