# GLM-5.3-Flash vs Kimi K3.1 Pro > GLM-5.3-Flash scores 65.1 (B) to Kimi K3.1 Pro's 48.6 (D) 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-flash-vs-kimi-k3-1-pro - Markdown: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3-1-pro.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-Flash scores 65.1 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. Both do open-weight models you run yourself. - GLM-5.3-Flash: grade B, 65.1/100, rank #18 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json - Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.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-Flash (B) Good for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider Ahead on: - Reach, 60 against 40 - Risk, 55 against 50 - Agent interface, 70 against 65 - Documentation and upkeep, 58 against 51 Watch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists ### Kimi K3.1 Pro (D) Good for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API Watch for: 1,561 GB of files, so it needs a node of eight large accelerators at least ## Why the edge ### Reach: GLM-5.3-Flash +20 - GLM-5.3-Flash (60): Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5. - Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40. ### Documentation and upkeep: GLM-5.3-Flash +7 - GLM-5.3-Flash (58): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows. - Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51. ### Risk: GLM-5.3-Flash +5 - GLM-5.3-Flash (55): File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms. The security programme is 0, with no security.txt on z.ai or docs.z.ai, no SECURITY.md on GitHub and no advisories found. - Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50. ### Agent interface: GLM-5.3-Flash +5 - GLM-5.3-Flash (70): A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because 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 only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling. - Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65. ## Score by category | Category | Weight | GLM-5.3-Flash | Kimi K3.1 Pro | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 40 | GLM-5.3-Flash +20 | | Risk | 20% (20 this run) | 55 | 50 | GLM-5.3-Flash +5 | | Agent interface | 10% (10 this run) | 70 | 65 | GLM-5.3-Flash +5 | | Documentation and upkeep | 10% (10 this run) | 58 | 51 | GLM-5.3-Flash +7 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **65.1 · B** | **48.6 · D** | | ## Facts side by side | Fact | GLM-5.3-Flash | Kimi K3.1 Pro | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai | Moonshot AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Kimi K3.1 Pro License (modified MIT) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-26 | 2026-11-05 | | 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-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw. **Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet. ## Before you call either ### GLM-5.3-Flash 1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe) 2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs 3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max 4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block 5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt 6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai ### Kimi K3.1 Pro 1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators 2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3` 3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires 4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off 5. Kimi K3's model code doesn't load it, because the attention layers changed 6. Validate JSON output yourself, since the card doesn't document structured output for the weights ## Questions ### Which is better for AI agents, GLM-5.3-Flash or Kimi K3.1 Pro? GLM-5.3-Flash scores 65.1 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3-1-pro.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3-flash", "b": "kimi-k3-1-pro"}`. 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[Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.md) - [Kimi K3.1 Pro vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.md) - [Kimi K3.1 Pro vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.md) - [Kimi K3.1 Pro vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.md) - [Kimi K3.1 Pro vs ZAYA1-8B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.md) - [Kimi K3 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp