# GLM-5.3 vs MiniCPM5-2B > GLM-5.3 scores 64.6 (B) 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/glm-5-3-vs-minicpm5-2b - Markdown: https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm5-2b.md (~5,850 tokens) - Slim: https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm5-2b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/glm-5-3-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 GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - GLM-5.3: grade B, 64.6/100, rank #17 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.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 ### GLM-5.3 (B) Good for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider Ahead on: - Capability, 80 against 27 Watch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build ### 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: - Reach, 90 against 55 - Risk, 55 against 50 - Agent interface, 75 against 70 - Documentation and upkeep, 97 against 58 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ## Score by category | Category | Weight | GLM-5.3 | MiniCPM5-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 80 | 27 | GLM-5.3 +53 | | Reach | 25% (25 this run) | 55 | 90 | MiniCPM5-2B +35 | | Risk | 20% (20 this run) | 50 | 55 | MiniCPM5-2B +5 | | Agent interface | 10% (10 this run) | 70 | 75 | MiniCPM5-2B +5 | | Documentation and upkeep | 10% (10 this run) | 58 | 97 | MiniCPM5-2B +39 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **64.6 · B** | **60.2 · C** | | ## Facts side by side | Fact | GLM-5.3 | MiniCPM5-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai (Zhipu AI) | OpenBMB | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-14 | 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 | 2.8/5 (4) | 3/5 (4) | ## Verdicts **GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off. **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 ### GLM-5.3 1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe) 2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text 3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max 4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default 5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block 6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API 7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe ### 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, GLM-5.3 or MiniCPM5-2B? GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on reach, risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm5-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3", "b": "minicpm5-2b"}`. 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