Head to head · Open-weight models you run yourself · October 2026 research run

Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct

Qwen3-VL-30B-A3B-Instruct scores 58 (C) 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.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Kimi K3.1 ProMoonshot AI · Open-weight model D48.6/100#151 of 160 open-weight models Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model C58/100#104 of 160 open-weight models

Which one, for what

Kimi K3.1 Pro D

Best 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 36

Watch for

1,561 GB of files, so it needs a node of eight large accelerators at least

Qwen3-VL-30B-A3B-Instruct C

Best for An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context

Ahead on

Reach, 95 against 40
Risk, 55 against 50

Watch for

No safety evaluation, agent-use guidance or security contact

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reach Qwen3-VL-30B-A3B-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-30B-A3B-Instruct 95

Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95.

Documentation and upkeep Kimi K3.1 Pro +15

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-30B-A3B-Instruct 36

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 newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36.

Risk Qwen3-VL-30B-A3B-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-30B-A3B-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 (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.

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-30B-A3B-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 multimodal function calling, with several calls allowed in a turn (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).

Score by category

Category	Weight this run	Kimi K3.1 Pro	Qwen3-VL-30B-A3B-Instruct	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	40	95	Qwen3-VL-30B-A3B-Instruct +55
Risk	20%20	50	55	Qwen3-VL-30B-A3B-Instruct +5
Agent interface	10%10	65	60	Kimi K3.1 Pro +5
Documentation and upkeep	10%10	51	36	Kimi K3.1 Pro +15
Negative events	≤15	0	0
Total		D 48.6/100	C 58/100

Facts side by side

Fact	Kimi K3.1 Pro	Qwen3-VL-30B-A3B-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	3/5 (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-30B-A3B-Instruct

A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.

Before you call either

Kimi K3.1 Pro

Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
With vLLM use 0.34.0 or later and the recipe's flags, --tool-call-parser kimi_k3 --reasoning-parser kimi_k3
Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
Set reasoning_effort to low, high or max per request. Max is the default and thinking can't be turned off
Kimi K3's model code doesn't load it, because the attention layers changed
Validate JSON output yourself, since the card doesn't document structured output for the weights

Qwen3-VL-30B-A3B-Instruct

Start it with llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:30b-a3b-instruct
In Ollama the plain qwen3-vl:30b tag is the Thinking edition, so name 30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
Validate JSON output yourself, since structured output isn't documented
vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later

Questions

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