Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3.1 Pro vs SmolLM3-3B
SmolLM3-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. 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
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
No category where it leads by five points or more, and no fact that sets it apart.
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, fully transparent tool-calling cousin on a laptop, CPU or edge device
Ahead on
- Reach, 85 against 40
- Risk, 65 against 50
- Agent interface, 80 against 65
- Documentation and upkeep, 74 against 51
Watch for
65,536 tokens trained; 131,072 needs a YaRN edit to config.json
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 SmolLM3-3B +45
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.
SmolLM3-3B 85
Memory 30, the smallest 4-bit build Hugging Face publishes is its own ONNX q4f16 at 2.1 GB, about 2.5 GB with the fifth added, and the full BF16 files that the counted runtimes load come to about 7.4 GB, both under 16 GB. Runtimes 20, released Transformers carries the model code, llama.cpp knows the architecture, and vLLM and SGLang run it through their Transformers backends in released versions; Ollama has no library entry and LM Studio's catalogue has none, so those two earn nothing. Builds 10, Hugging Face's ONNX repository has 4-bit builds, one of the checklist's kinds, and int8 builds, which are neither FP8 nor GGUF. They run in ONNX Runtime and Transformers.js rather than the runtimes above. Terms 15, plain Apache 2.0, scored by its terms since the repository holds no LICENSE file. Download 10, not gated. Hosted 0, no inference provider is listed for it on the Hub and none of the provider lists checked carries it.
Documentation and upkeep SmolLM3-3B +23
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.
SmolLM3-3B 74
Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply.
Risk SmolLM3-3B +15
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.
SmolLM3-3B 65
File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent.
Agent interface SmolLM3-3B +15
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.
SmolLM3-3B 80
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN.
Score by category
| Category | Weight this run | Kimi K3.1 Pro | SmolLM3-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 85 | SmolLM3-3B +45 |
| Risk | 20%20 | 50 | 65 | SmolLM3-3B +15 |
| Agent interface | 10%10 | 65 | 80 | SmolLM3-3B +15 |
| Documentation and upkeep | 10%10 | 51 | 74 | SmolLM3-3B +23 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 48.6/100 | C 61/100 |
Facts side by side
| Fact | Kimi K3.1 Pro | SmolLM3-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Hugging Face |
| 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 | 2025-09-10 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.3/5 (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.
SmolLM3-3B
A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025.
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
SmolLM3-3B
- Start it with
vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text - Pass
chat_template_kwargswithenable_thinkingfalse for short tasks, since thinking is on by default; a/thinkor/no_thinkflag in the system prompt overrides the kwarg - Put earlier tool calls in the assistant message content, since the template ignores a
tool_callsfield when it renders history - Add the YaRN
rope_scalingblock (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens - Use temperature 0.6 and top_p 0.95, the card's recommended sampling
- Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
- Validate JSON output yourself or use the runtime's guided decoding
Questions
Which is better for AI agents, Kimi K3.1 Pro or SmolLM3-3B?
SmolLM3-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.
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Machine-readable
- This page as Markdown
/compare/kimi-k3-1-pro-vs-smollm3-3b.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/smollm3-3b.json - From a terminal
anchor compare kimi-k3-1-pro smollm3-3b(the CLI) - Over MCP
compare_tools {"a": "kimi-k3-1-pro", "b": "smollm3-3b"}at/mcp, no key