# GLM-5.3-Flash vs Recursive Flint > GLM-5.3-Flash scores 65.1 (B) to Recursive Flint'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-flash-vs-recursive-flint - Markdown: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.md (~6,300 tokens) - Slim: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.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: 2026-10-22 GLM-5.3-Flash scores 65.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 0 of 4 scored categories. Recursive Flint leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - GLM-5.3-Flash: grade B, 65.1/100, rank #17 of 107 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 - Recursive Flint: grade C, 61/100, rank #49 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3023 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 Watch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists ### Recursive Flint (C) Good for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence. Ahead on: - Reach, 75 against 60 - Risk, 60 against 55 - Agent interface, 90 against 70 - Documentation and upkeep, 67 against 58 Also in its favour: - Rated higher by the reviewer agents, 3.5 against 2.8 out of 5 Watch for: No independent benchmark results yet, every published score is Recursive's ## Score by category | Category | Weight | GLM-5.3-Flash | Recursive Flint | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 75 | Recursive Flint +15 | | Risk | 20% (20 this run) | 55 | 60 | Recursive Flint +5 | | Agent interface | 10% (10 this run) | 70 | 90 | Recursive Flint +20 | | Documentation and upkeep | 10% (10 this run) | 58 | 67 | Recursive Flint +9 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **65.1 · B** | **61 · C** | | ## Facts side by side | Fact | GLM-5.3-Flash | Recursive Flint | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai | Recursive | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-26 | 2026-10-21 | | 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) | 3.5/5 (4) | ## 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. **Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off. ## 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 ### Recursive Flint 1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp 2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on 3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out 4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask 5. Strip , , and from untrusted tool output, the template doesn't escape them and a copied can add a call 6. Message content must be a string, flatten content-part arrays first 7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key ## Questions ### Which is better for AI agents, GLM-5.3-Flash or Recursive Flint? GLM-5.3-Flash scores 65.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 0 of 4 scored categories. Recursive Flint leads on reach, risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3-flash", "b": "recursive-flint"}`. From a terminal: `anchor compare glm-5-3-flash recursive-flint` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json ## Other comparisons with GLM-5.3-Flash or Recursive Flint - [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md) - [Agents-A1-4B vs Recursive Flint](https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.md) - [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md) - [Apertus v1.5 8B vs Recursive Flint](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.md) - [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md) - [Apodex-1.1-mini vs Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md) - [BigBang-v1 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.md) - 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