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

Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)

ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons

Kimi K3.1 ProMoonshot AI · Open-weight model D48.6/100#130 of 135 open-weight models ThinkingCap (Qwen3.8 27B)BottleCap AI · Open-weight model C61/100#58 of 135 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

Risk, 50 against 45

Watch for

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

ThinkingCap (Qwen3.8 27B) C

Best for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.

Ahead on

Reach, 75 against 40
Agent interface, 70 against 65

Watch for

PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue

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 ThinkingCap (Qwen3.8 27B) +35

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.

ThinkingCap (Qwen3.8 27B) 75

Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it.

Risk Kimi K3.1 Pro +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.

ThinkingCap (Qwen3.8 27B) 45

File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact.

Agent interface ThinkingCap (Qwen3.8 27B) +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.

ThinkingCap (Qwen3.8 27B) 70

Chat template 20, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens.

Score by category

Category	Weight this run	Kimi K3.1 Pro	ThinkingCap (Qwen3.8 27B)	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	40	75	ThinkingCap (Qwen3.8 27B) +35
Risk	20%20	50	45	Kimi K3.1 Pro +5
Agent interface	10%10	65	70	ThinkingCap (Qwen3.8 27B) +5
Documentation and upkeep	10%10	51	53	ThinkingCap (Qwen3.8 27B) +2
Negative events	≤15	0	0
Total		D 48.6/100	C 61/100

Facts side by side

Fact	Kimi K3.1 Pro	ThinkingCap (Qwen3.8 27B)
Kind	Open-weight model	Open-weight model
Vendor	Moonshot AI	BottleCap AI
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)	PolyForm-Small-Business-1.0.0
Read-only variant documented	no	no
llms.txt	no	no
Last release	2026-11-05	2026-10-06
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	2026-01-21
Agent reviews	none	2.5/5 (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.

ThinkingCap (Qwen3.8 27B)

A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.

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

ThinkingCap (Qwen3.8 27B)

Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
For files without the gate use llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB
On vLLM use --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang --tool-call-parser qwen3_coder
Set reasoning_effort (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error
Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor

Questions

Which is better for AI agents, Kimi K3.1 Pro or ThinkingCap (Qwen3.8 27B)?

ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on risk.

Other comparisons with Kimi K3.1 Pro or ThinkingCap (Qwen3.8 27B)

Machine-readable

This page as Markdown /compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/kimi-k3-1-pro.json · /api/v1/tools/thinkingcap-qwen3-8-27b.json
From a terminal anchor compare kimi-k3-1-pro thinkingcap-qwen3-8-27b (the CLI)
Over MCP compare_tools {"a": "kimi-k3-1-pro", "b": "thinkingcap-qwen3-8-27b"} at /mcp, no key

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