# Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct > Qwen3-VL-4B-Instruct scores 52.6 (D) 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/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct - Markdown: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.md (~9,400 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 Qwen3-VL-4B-Instruct scores 52.6 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. Kimi K3.1 Pro leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself. - 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 - Qwen3-VL-4B-Instruct: grade D, 52.6/100, rank #137 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.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 ### 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 Ahead on: - Agent interface, 65 against 60 - Documentation and upkeep, 51 against 31 Watch for: 1,561 GB of files, so it needs a node of eight large accelerators at least ### Qwen3-VL-4B-Instruct (D) Good for: An agent with 4 GB or more to spare that needs a local model to read images, documents and video Ahead on: - Reach, 95 against 40 - Risk, 55 against 50 Watch for: No safety evaluation, agent-use guidance or security contact ## Why the edge ### Reach: Qwen3-VL-4B-Instruct +55 - 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. - Qwen3-VL-4B-Instruct (95): Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95. ### Documentation and upkeep: Kimi K3.1 Pro +20 - 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. - Qwen3-VL-4B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31. ### Risk: Qwen3-VL-4B-Instruct +5 - 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. - Qwen3-VL-4B-Instruct (55): Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55. ### Agent interface: Kimi K3.1 Pro +5 - 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. - Qwen3-VL-4B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60. ## Score by category | Category | Weight | Kimi K3.1 Pro | Qwen3-VL-4B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 95 | Qwen3-VL-4B-Instruct +55 | | Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-4B-Instruct +5 | | Agent interface | 10% (10 this run) | 65 | 60 | Kimi K3.1 Pro +5 | | Documentation and upkeep | 10% (10 this run) | 51 | 31 | Kimi K3.1 Pro +20 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **48.6 · D** | **52.6 · D** | | ## Facts side by side | Fact | Kimi K3.1 Pro | Qwen3-VL-4B-Instruct | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Moonshot AI | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Kimi K3.1 Pro License (modified MIT) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-11-05 | 2025-11-01 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## Verdicts **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. **Qwen3-VL-4B-Instruct.** Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI. ## Before you call either ### 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 ### Qwen3-VL-4B-Instruct 1. Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct` 2. In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct` 3. With the GGUF, load the mmproj vision file too, or images won't be read 4. Parse tool calls as JSON inside `` tags, or use SGLang's `--tool-call-parser qwen` 5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text 6. Validate JSON output yourself, since structured output isn't documented 7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later ## Questions ### Which is better for AI agents, Kimi K3.1 Pro or Qwen3-VL-4B-Instruct? Qwen3-VL-4B-Instruct scores 52.6 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. Kimi K3.1 Pro leads on agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3-1-pro", "b": "qwen3-vl-4b-instruct"}`. 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[Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.md) - [Kimi K3.1 Pro vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b.md) - [Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md) - [Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b.md) - [Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b.md) - [Kimi K3.1 Pro vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.md) - [Kimi K3.1 Pro vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.md) - [Kimi K3.1 Pro vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think.md) - 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[Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.md) - [Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.md) - [Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.md) - [Qwen3-VL-4B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp