Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-4B vs Recursive Flint
Qwen3.5-4B and Recursive Flint score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Recursive Flint leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 3023 open weights comparisons
Qwen3.5-4BAlibaba (Qwen team) · Open-weight model
C61.2/100#34 of 107 open-weight models
Recursive FlintRecursive · Open-weight model
C61/100#49 of 107 open-weight models
Which one, for what
Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU
Ahead on
- Reach, 85 against 75
Watch for
No quantised build from Alibaba for this size
Best 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 70
- Documentation and upkeep, 67 against 34
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5
Watch for
No independent benchmark results yet, every published score is Recursive's
Score by category
| Category | Weight this run | Qwen3.5-4B | Recursive Flint | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 75 | Qwen3.5-4B +10 |
| Risk | 20%20 | 55 | 60 | Recursive Flint +5 |
| Agent interface | 10%10 | 70 | 90 | Recursive Flint +20 |
| Documentation and upkeep | 10%10 | 34 | 67 | Recursive Flint +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61.2/100 | C 61/100 |
Facts side by side
| Fact | Qwen3.5-4B | Recursive Flint |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Recursive |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-02 | 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 | 3/5 (4) | 3.5/5 (4) |
Verdicts
Qwen3.5-4B
A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.
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
Qwen3.5-4B
- Smallest start is
ollama run qwen3.5:4b(Q4_K_M, about 4 GB with context) - Full precision
vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Turn thinking off with
"chat_template_kwargs": {"enable_thinking": false}in the request body, since it's on by default and/thinkand/nothinkaren't supported - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5without thinking - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
Recursive Flint
- 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 - 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 - 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
- Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
- 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
- Message content must be a string, flatten content-part arrays first
- 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, Qwen3.5-4B or Recursive Flint?
Qwen3.5-4B and Recursive Flint score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Recursive Flint leads on risk, agent interface and documentation and upkeep.
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- Nemotron 3 Ultra 550B A55B vs Qwen3.5-4B
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- occamy-1.0 vs Qwen3.5-4B
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- Qwen3.5-4B vs Qwen3-VL-8B-Instruct
- Qwen3.5-4B vs Qwopus3.8-27B-Flash-V2
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- Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next
- Recursive Flint vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/qwen3-5-4b-vs-recursive-flint.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-4b.json·/api/v1/tools/recursive-flint.json - From a terminal
anchor compare qwen3-5-4b recursive-flint(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-4b", "b": "recursive-flint"}at/mcp, no key