Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3-VL-8B-Instruct vs Recursive Flint
Recursive Flint scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach. 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-VL-8B-InstructAlibaba (Qwen team) · Open-weight model
C54.9/100#86 of 107 open-weight models
Recursive FlintRecursive · Open-weight model
C61/100#49 of 107 open-weight models
Which one, for what
Best for An agent with 8 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Reach, 100 against 75
Watch for
No safety evaluation, agent-use guidance or security contact
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 60
- Documentation and upkeep, 67 against 31
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 this run | Qwen3-VL-8B-Instruct | Recursive Flint | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 100 | 75 | Qwen3-VL-8B-Instruct +25 |
| Risk | 20%20 | 55 | 60 | Recursive Flint +5 |
| Agent interface | 10%10 | 60 | 90 | Recursive Flint +30 |
| Documentation and upkeep | 10%10 | 31 | 67 | Recursive Flint +36 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.9/100 | C 61/100 |
Facts side by side
| Fact | Qwen3-VL-8B-Instruct | 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 | 2025-11-01 | 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
Qwen3-VL-8B-Instruct
A small vision-language cousin for reading screenshots, documents, charts and video, in about 6 GB from Alibaba's Q4_K_M GGUF and every major runtime, under plain Apache 2.0. It has no thinking mode and documents tool calls only in the template. The main risk is no published safety work, so add your own guardrails before it drives a GUI.
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-VL-8B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:8b-instruct - In Ollama the plain
qwen3-vl:8btag is the Thinking edition, so name8b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
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-VL-8B-Instruct or Recursive Flint?
Recursive Flint scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.
Other comparisons with Qwen3-VL-8B-Instruct or Recursive Flint
- Agents-A1-4B vs Qwen3-VL-8B-Instruct
- Agents-A1-4B vs Recursive Flint
- Apertus v1.5 8B vs Qwen3-VL-8B-Instruct
- Apertus v1.5 8B vs Recursive Flint
- Apodex-1.1-mini vs Qwen3-VL-8B-Instruct
- Apodex-1.1-mini vs Recursive Flint
- BigBang-v1 vs Qwen3-VL-8B-Instruct
- BigBang-v1 vs Recursive Flint
- Carbon-X vs Qwen3-VL-8B-Instruct
- Carbon-X vs Recursive Flint
- Cosmos-Reason2-2B vs Qwen3-VL-8B-Instruct
- Cosmos-Reason2-2B vs Recursive Flint
- Darwin-36B-Opus vs Qwen3-VL-8B-Instruct
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- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Recursive Flint
- DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct
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- DeepSeek-V4-Flash-0731 vs Qwen3-VL-8B-Instruct
- DeepSeek-V4-Flash-0731 vs Recursive Flint
- DiffusionGemma 26B A4B IT vs Qwen3-VL-8B-Instruct
- DiffusionGemma 26B A4B IT vs Recursive Flint
- Gemma 4 12B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 12B IT vs Recursive Flint
- Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 26B A4B IT vs Recursive Flint
- Gemma 4 31B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 31B IT vs Recursive Flint
- Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E2B IT vs Recursive Flint
- Gemma 4 E4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E4B IT vs Recursive Flint
- GLM-4.5-Air vs Qwen3-VL-8B-Instruct
- GLM-4.5-Air vs Recursive Flint
- GLM-4.7-Flash vs Qwen3-VL-8B-Instruct
- GLM-4.7-Flash vs Recursive Flint
- GLM-5.3-Flash vs Qwen3-VL-8B-Instruct
- GLM-5.3-Flash vs Recursive Flint
- GLM-5.3 vs Qwen3-VL-8B-Instruct
- GLM-5.3 vs Recursive Flint
- gpt-oss-120b vs Qwen3-VL-8B-Instruct
- gpt-oss-120b vs Recursive Flint
- gpt-oss-20b vs Qwen3-VL-8B-Instruct
- gpt-oss-20b vs Recursive Flint
- Granite 4.2 3B vs Qwen3-VL-8B-Instruct
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- Hy3 vs Qwen3-VL-8B-Instruct
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- Inkling-Small vs Qwen3-VL-8B-Instruct
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- Inkling vs Qwen3-VL-8B-Instruct
- Inkling vs Recursive Flint
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct
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- Kimi K3 vs Qwen3-VL-8B-Instruct
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- Kolibri-1 vs Qwen3-VL-8B-Instruct
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- Laguna M.1 vs Qwen3-VL-8B-Instruct
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- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
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- Laguna XS 2.1 vs Qwen3-VL-8B-Instruct
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- LFM2.5-2.6B vs Qwen3-VL-8B-Instruct
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- Ling-3.0-tiny vs Qwen3-VL-8B-Instruct
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- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct
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- MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct
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- MiniMax-M2.5 vs Qwen3-VL-8B-Instruct
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- MiniMax-M2.7 vs Qwen3-VL-8B-Instruct
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- Muse Glimmer 30B vs Qwen3-VL-8B-Instruct
- Muse Glimmer 30B vs Recursive Flint
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Recursive Flint
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct
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- Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct
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- Nemotron 3 Ultra 550B A55B vs Qwen3-VL-8B-Instruct
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- occamy-1.0 vs Qwen3-VL-8B-Instruct
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- Olmo 3 7B Think vs Qwen3-VL-8B-Instruct
- Olmo 3 7B Think vs Recursive Flint
- Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-35B-A3B vs Recursive Flint
- Ornith-1.5-397B vs Qwen3-VL-8B-Instruct
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- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint
- Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-2B-Instruct vs Recursive Flint
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Recursive Flint
- Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-32B-Instruct vs Recursive Flint
- Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-4B-Instruct vs Recursive Flint
- Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-8B-Instruct vs SmolLM3-3B
- Qwen3-VL-8B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwopus3.8-27B-Flash-V2 vs Recursive Flint
- Recursive Flint vs SmolLM3-3B
- Recursive Flint vs Spark-X2.6-4B
- Recursive Flint vs Swift 1.5 Qwen3.8-27B
- 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-vl-8b-instruct-vs-recursive-flint.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-vl-8b-instruct.json·/api/v1/tools/recursive-flint.json - From a terminal
anchor compare qwen3-vl-8b-instruct recursive-flint(the CLI) - Over MCP
compare_tools {"a": "qwen3-vl-8b-instruct", "b": "recursive-flint"}at/mcp, no key