Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507
Qwen3-4B-Instruct-2507 scores 54.5 (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 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
Which one, for what
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
- Documentation and upkeep, 51 against 26
Watch for
1,561 GB of files, so it needs a node of eight large accelerators at least
Best for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.
Ahead on
- Reach, 90 against 40
- Risk, 55 against 50
- Agent interface, 75 against 65
Watch for
No safety results, prompt-injection 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 Qwen3-4B-Instruct-2507 +50
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.
Qwen3-4B-Instruct-2507 90
Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3 architecture, Ollama's released build pins a llama.cpp that has it, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 0, on 4 November 2026 the Hub's provider mapping showed only Featherless AI with status error, Nscale was gone and Alibaba's Model Studio doesn't list it. 30 + 25 + 10 + 15 + 10 + 0 = 90.
Documentation and upkeep Kimi K3.1 Pro +25
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.
Qwen3-4B-Instruct-2507 26
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26.
Agent interface Qwen3-4B-Instruct-2507 +10
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.
Qwen3-4B-Instruct-2507 75
Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.
Risk Qwen3-4B-Instruct-2507 +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.
Qwen3-4B-Instruct-2507 55
File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Score by category
| Category | Weight this run | Kimi K3.1 Pro | Qwen3-4B-Instruct-2507 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 90 | Qwen3-4B-Instruct-2507 +50 |
| Risk | 20%20 | 50 | 55 | Qwen3-4B-Instruct-2507 +5 |
| Agent interface | 10%10 | 65 | 75 | Qwen3-4B-Instruct-2507 +10 |
| Documentation and upkeep | 10%10 | 51 | 26 | Kimi K3.1 Pro +25 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 48.6/100 | C 54.5/100 |
Facts side by side
| Fact | Kimi K3.1 Pro | Qwen3-4B-Instruct-2507 |
|---|---|---|
| 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 | 2025-09-17 |
| 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) |
How the scores have moved
Kimi K3.1 Pro
No score change since it was listed.
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.
Qwen3-4B-Instruct-2507
A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published.
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_effortto 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
Qwen3-4B-Instruct-2507
- Quickest start is
ollama run qwen3:4b-instruct, 2.5 GB. Plainqwen3:4bis the Thinking-2507 edition, not this one - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes - Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
- Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
- Drop the context to 32,768 tokens if memory runs out, as the card suggests
- Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, Kimi K3.1 Pro or Qwen3-4B-Instruct-2507?
Qwen3-4B-Instruct-2507 scores 54.5 (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 documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kimi-k3-1-pro.json·/api/v1/tools/qwen3-4b-instruct-2507.json - From a terminal
anchor compare kimi-k3-1-pro qwen3-4b-instruct-2507(the CLI) - Over MCP
compare_tools {"a": "kimi-k3-1-pro", "b": "qwen3-4b-instruct-2507"}at/mcp, no key