# GLM-4.7-Flash vs Recursive Flint

> GLM-4.7-Flash scores 63 (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-4-7-flash-vs-recursive-flint
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.md (~6,300 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-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-4.7-Flash scores 63 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 1 of 4 scored categories. Recursive Flint leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- GLM-4.7-Flash: grade B, 63/100, rank #27 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-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-4.7-Flash (B)

Good for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime

Ahead on:
- Reach, 80 against 75

Watch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January

### 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:
- Risk, 60 against 55
- Agent interface, 90 against 85
- Documentation and upkeep, 67 against 28

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-4.7-Flash | Recursive Flint | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 75 | GLM-4.7-Flash +5 |
| Risk | 20% (20 this run) | 55 | 60 | Recursive Flint +5 |
| Agent interface | 10% (10 this run) | 85 | 90 | Recursive Flint +5 |
| Documentation and upkeep | 10% (10 this run) | 28 | 67 | Recursive Flint +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **61 · C** | |

## Facts side by side

| Fact | GLM-4.7-Flash | Recursive Flint |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu 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 | 2026-01-29 | 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-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.

**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-4.7-Flash

1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`
2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build
3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
5. Pass `chat_template_kwargs: {"enable_thinking": false}` for short tasks, since thinking is on by default
6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench
7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000

### 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 <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> 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-4.7-Flash or Recursive Flint?

GLM-4.7-Flash scores 63 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 1 of 4 scored categories. Recursive Flint leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-7-flash", "b": "recursive-flint"}`. From a terminal: `anchor compare glm-4-7-flash recursive-flint`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json

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