# GLM-5.3 vs MiMo-V2.6-Flash-RL > GLM-5.3 scores 64.6 (B) to MiMo-V2.6-Flash-RL's 61 (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-mimo-v2-6-flash-rl - Markdown: https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.md (~9,050 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 GLM-5.3 scores 64.6 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach and agent interface. Both do open-weight models you run yourself. - GLM-5.3: grade B, 64.6/100, rank #21 of 160 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 - MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 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: - Risk, 50 against 45 Watch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build ### MiMo-V2.6-Flash-RL (C) Good for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context Ahead on: - Reach, 65 against 55 - Agent interface, 85 against 70 Watch for: 178 GB at 4 bits, so it needs about 200 GB of memory ## Why the edge ### Agent interface: MiMo-V2.6-Flash-RL +15 - GLM-5.3 (70): A Jinja chat template sits in the repository, for 20. The tool-calling format is 10, since it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and JSON documented only for the hosted API. Reasoning control is 10, a per-request budget through reasoning_effort with the reasoning in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rope scaling. - MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85. ### Reach: MiMo-V2.6-Flash-RL +10 - GLM-5.3 (55): Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry. Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio have no local build. Z.ai publishes one quantised build, FP8, for 10, since the BF16 repository is the full precision. The GLM-5.3 License is a custom licence that is free for commercial use below a revenue threshold, for 10. The files download with no account or gate, for 10, and Z.ai's own API plus eight live providers on Hugging Face serve it, for 5. - MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65. ### Risk: GLM-5.3 +5 - GLM-5.3 (50): File safety is full, with safetensors only, no Python in the repository and no remote code needed in Transformers 5.12.0 or later. Provenance is full, files in the zai-org organisation Z.ai links to with a SHA-256 for every shard. Safety evaluation is 0, because the blog mentions safety evaluation and hardening but publishes no results, and agent guidance is 0, with nothing on refusals, prompt injection or cautions. Licence risk is 10, losing 5 for the security review that model-as-a-service operators above US$10 billion of revenue must pass. The security programme is 0, with no security.txt, no SECURITY.md and no advisories. - MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45. ## Score by category | Category | Weight | GLM-5.3 | MiMo-V2.6-Flash-RL | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 65 | MiMo-V2.6-Flash-RL +10 | | Risk | 20% (20 this run) | 50 | 45 | GLM-5.3 +5 | | Agent interface | 10% (10 this run) | 70 | 85 | MiMo-V2.6-Flash-RL +15 | | Documentation and upkeep | 10% (10 this run) | 58 | 59 | MiMo-V2.6-Flash-RL +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **64.6 · B** | **61 · C** | | ## Facts side by side | Fact | GLM-5.3 | MiMo-V2.6-Flash-RL | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai (Zhipu AI) | Xiaomi (MiMo team) | | 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) | MIT (metadata only, no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-15 | 2026-10-28 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.8/5 (4) | none | ## 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. **MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered. ## 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 ### MiMo-V2.6-Flash-RL 1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita 2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo` 3. Use temperature 1.0 and top_p 0.95, as the card recommends 4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in 5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle 6. The Use Restrictions section asks for human oversight of high-risk actions ## Questions ### Which is better for AI agents, GLM-5.3 or MiMo-V2.6-Flash-RL? GLM-5.3 scores 64.6 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3", "b": "mimo-v2-6-flash-rl"}`. 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[MiMo-V2.6-Flash-RL vs ZAYA1-8B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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