Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3.1 Pro vs Nanbeige4.2-3B
Nanbeige4.2-3B 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 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
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 Local agents that need tool calling and a long context on a laptop or a small GPU
Ahead on
- Reach, 85 against 40
- Risk, 55 against 50
- Agent interface, 85 against 65
- Documentation and upkeep, 72 against 51
Watch for
Its own architecture: Transformers loads it only with the repository's code
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 Nanbeige4.2-3B +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.
Nanbeige4.2-3B 85
Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.
Documentation and upkeep Nanbeige4.2-3B +21
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.
Nanbeige4.2-3B 72
Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.
Agent interface Nanbeige4.2-3B +20
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.
Nanbeige4.2-3B 85
Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Risk Nanbeige4.2-3B +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.
Nanbeige4.2-3B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.
Score by category
| Category | Weight this run | Kimi K3.1 Pro | Nanbeige4.2-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 85 | Nanbeige4.2-3B +45 |
| Risk | 20%20 | 50 | 55 | Nanbeige4.2-3B +5 |
| Agent interface | 10%10 | 65 | 85 | Nanbeige4.2-3B +20 |
| Documentation and upkeep | 10%10 | 51 | 72 | Nanbeige4.2-3B +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 48.6/100 | C 61/100 |
Facts side by side
| Fact | Kimi K3.1 Pro | Nanbeige4.2-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Nanbeige LLM Lab (BOSS Zhipin) |
| 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 (Hub metadata only; the repository has no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-05 | 2026-09-11 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
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.
Nanbeige4.2-3B
A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.
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
Nanbeige4.2-3B
- Serve it with SGLang 0.5.23 or later and pass
--tool-call-parser nanbeige --reasoning-parser nanbeige - Pass
toolsin the request. The card recommendstool_call_format="xml" - Set
preserve_thinkingtrue for multi-turn tool use and coding, false for plain chat - Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card
- For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's
- If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it
Questions
Which is better for AI agents, Kimi K3.1 Pro or Nanbeige4.2-3B?
Nanbeige4.2-3B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.
Other comparisons with Kimi K3.1 Pro or Nanbeige4.2-3B
- Agents-A1-4B vs Kimi K3.1 Pro
- Agents-A1-4B vs Nanbeige4.2-3B
- Apertus v1.5 8B vs Kimi K3.1 Pro
- Apertus v1.5 8B vs Nanbeige4.2-3B
- Apodex-1.1-mini vs Kimi K3.1 Pro
- Apodex-1.1-mini vs Nanbeige4.2-3B
- Arete-27B vs Kimi K3.1 Pro
- Arete-27B vs Nanbeige4.2-3B
- BigBang-v1 vs Kimi K3.1 Pro
- BigBang-v1 vs Nanbeige4.2-3B
- Carbon-X vs Kimi K3.1 Pro
- Carbon-X vs Nanbeige4.2-3B
- Cola2 vs Kimi K3.1 Pro
- Cola2 vs Nanbeige4.2-3B
- Cosmos-Reason2-2B vs Kimi K3.1 Pro
- Cosmos-Reason2-2B vs Nanbeige4.2-3B
- Darwin-36B-Opus vs Kimi K3.1 Pro
- Darwin-36B-Opus vs Nanbeige4.2-3B
- DeepSeek-V3.2 vs Kimi K3.1 Pro
- DeepSeek-V3.2 vs Nanbeige4.2-3B
- DeepSeek V4.1 Flash vs Kimi K3.1 Pro
- DeepSeek V4.1 Flash vs Nanbeige4.2-3B
- DeepSeek-V4-Flash-0731 vs Kimi K3.1 Pro
- DeepSeek-V4-Flash-0731 vs Nanbeige4.2-3B
- Devstral Small 2 vs Kimi K3.1 Pro
- Devstral Small 2 vs Nanbeige4.2-3B
- DiffusionGemma 26B A4B IT vs Kimi K3.1 Pro
- DiffusionGemma 26B A4B IT vs Nanbeige4.2-3B
- EXAONE 4.5 33B vs Kimi K3.1 Pro
- EXAONE 4.5 33B vs Nanbeige4.2-3B
- Gemma 4 12B IT vs Kimi K3.1 Pro
- Gemma 4 12B IT vs Nanbeige4.2-3B
- Gemma 4 26B A4B IT vs Kimi K3.1 Pro
- Gemma 4 26B A4B IT vs Nanbeige4.2-3B
- Gemma 4 31B IT vs Kimi K3.1 Pro
- Gemma 4 31B IT vs Nanbeige4.2-3B
- Gemma 4 E2B IT vs Kimi K3.1 Pro
- Gemma 4 E2B IT vs Nanbeige4.2-3B
- Gemma 4 E4B IT vs Kimi K3.1 Pro
- Gemma 4 E4B IT vs Nanbeige4.2-3B
- GLM-4.5-Air vs Kimi K3.1 Pro
- GLM-4.5-Air vs Nanbeige4.2-3B
- GLM-4.7-Flash vs Kimi K3.1 Pro
- GLM-4.7-Flash vs Nanbeige4.2-3B
- GLM-5.3-Flash vs Kimi K3.1 Pro
- GLM-5.3-Flash vs Nanbeige4.2-3B
- GLM-5.3 vs Kimi K3.1 Pro
- GLM-5.3 vs Nanbeige4.2-3B
- gpt-oss-120b vs Kimi K3.1 Pro
- gpt-oss-120b vs Nanbeige4.2-3B
- gpt-oss-20b vs Kimi K3.1 Pro
- gpt-oss-20b vs Nanbeige4.2-3B
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 30B vs Nanbeige4.2-3B
- Granite 4.2 3B vs Kimi K3.1 Pro
- Granite 4.2 3B vs Nanbeige4.2-3B
- Granite 4.2 8B vs Kimi K3.1 Pro
- Granite 4.2 8B vs Nanbeige4.2-3B
- Hy-Memory-30B-A3B vs Kimi K3.1 Pro
- Hy-Memory-30B-A3B vs Nanbeige4.2-3B
- Hy3 vs Kimi K3.1 Pro
- Hy3 vs Nanbeige4.2-3B
- Inkling-Small vs Kimi K3.1 Pro
- Inkling-Small vs Nanbeige4.2-3B
- Inkling vs Kimi K3.1 Pro
- Inkling vs Nanbeige4.2-3B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.2-3B
- Kimi K3.1 Pro vs Kolibri-1
- Kimi K3.1 Pro vs Kronos
- Kimi K3.1 Pro vs Laguna M.1
- Kimi K3.1 Pro vs Laguna S 2.1
- Kimi K3.1 Pro vs Laguna XS 2.1
- Kimi K3.1 Pro vs LBS-27B
- Kimi K3.1 Pro vs LFM2.5-2.6B
- Kimi K3.1 Pro vs LFM3-1.7B
- Kimi K3.1 Pro vs LFM3-Audio-2B
- Kimi K3.1 Pro vs LFM3-Omni-2B
- Kimi K3.1 Pro vs LFM3-VL-2B
- Kimi K3.1 Pro vs Ling-3.0-tiny
- Kimi K3.1 Pro vs Ling-3.1-flash-1019
- Kimi K3.1 Pro vs LongCat-2.5
- Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B
- Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL
- Kimi K3.1 Pro vs MiniCPM-o 4.5
- Kimi K3.1 Pro vs MiniCPM-V 4.7 1B
- Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B
- Kimi K3.1 Pro vs MiniCPM5-2B
- Kimi K3.1 Pro vs MiniMax-M2.5
- Kimi K3.1 Pro vs MiniMax-M2.7
- Kimi K3.1 Pro vs MiniMax-M3.1-Flash
- Kimi K3.1 Pro vs Mistral Large 4
- Kimi K3.1 Pro vs Muse Glimmer 30B
- Kimi K3.1 Pro vs Nanbeige4.3-33B
- Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 4B
- Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B
- Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B
- Kimi K3.1 Pro vs North Mini Code 1.0
- Kimi K3.1 Pro vs occamy-1.0
- Kimi K3.1 Pro vs Olmo 3 7B Think
- Kimi K3.1 Pro vs Ornith-1.5-122B
- Kimi K3.1 Pro vs Ornith-1.5-35B-A3B
- Kimi K3.1 Pro vs Ornith-1.5-397B
- Kimi K3.1 Pro vs Ornith-1.5-9B
- Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT
- Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B
- Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3.5-0.8B
- Kimi K3.1 Pro vs Qwen3.5-122B-A10B
- Kimi K3.1 Pro vs Qwen3.5-2B
- Kimi K3.1 Pro vs Qwen3.5-397B-A17B
- Kimi K3.1 Pro vs Qwen3.5-4B
- Kimi K3.1 Pro vs Qwen3.5-9B
- Kimi K3.1 Pro vs Qwen3.6-27B
- Kimi K3.1 Pro vs Qwen3.6-35B-A3B
- Kimi K3.1 Pro vs Qwen3.8-27B
- Kimi K3.1 Pro vs Qwen3.8-Flash-Next
- Kimi K3.1 Pro vs Qwen3-Coder-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-Coder-Next
- Kimi K3.1 Pro vs Qwen3-Omni-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-2B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct
- Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2
- Kimi K3.1 Pro vs Recursive Flint
- Kimi K3.1 Pro vs Ripple-65B Preview
- Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview
- Kimi K3.1 Pro vs SmolLM3-3B
- Kimi K3.1 Pro vs Spark-X2.6-4B
- Kimi K3.1 Pro vs Step 3.7 Flash
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next
- Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)
- Kimi K3.1 Pro vs Baidu Unlimited-OCR-2
- Kimi K3.1 Pro vs WeVisOne-VL-2B
- Kimi K3.1 Pro vs WeVisOne-VL-4B
- Kimi K3.1 Pro vs ZAYA1-8B
- Kimi K3 vs Kimi K3.1 Pro
- Kimi K3 vs Nanbeige4.2-3B
- Kolibri-1 vs Nanbeige4.2-3B
- Kronos vs Nanbeige4.2-3B
- Laguna M.1 vs Nanbeige4.2-3B
- Laguna S 2.1 vs Nanbeige4.2-3B
- Laguna XS 2.1 vs Nanbeige4.2-3B
- LBS-27B vs Nanbeige4.2-3B
- LFM2.5-2.6B vs Nanbeige4.2-3B
- LFM3-1.7B vs Nanbeige4.2-3B
- LFM3-Audio-2B vs Nanbeige4.2-3B
- LFM3-Omni-2B vs Nanbeige4.2-3B
- LFM3-VL-2B vs Nanbeige4.2-3B
- Ling-3.0-tiny vs Nanbeige4.2-3B
- Ling-3.1-flash-1019 vs Nanbeige4.2-3B
- LongCat-2.5 vs Nanbeige4.2-3B
- MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.2-3B
- MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B
- MiniCPM-o 4.5 vs Nanbeige4.2-3B
- MiniCPM-V 4.7 1B vs Nanbeige4.2-3B
- MiniCPM-V 4.7 35B-A3B vs Nanbeige4.2-3B
- MiniCPM5-2B vs Nanbeige4.2-3B
- MiniMax-M2.5 vs Nanbeige4.2-3B
- MiniMax-M2.7 vs Nanbeige4.2-3B
- MiniMax-M3.1-Flash vs Nanbeige4.2-3B
- Mistral Large 4 vs Nanbeige4.2-3B
- Muse Glimmer 30B vs Nanbeige4.2-3B
- Nanbeige4.2-3B vs Nanbeige4.3-33B
- Nanbeige4.2-3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Nanbeige4.2-3B vs Nemotron 3.5 Lightning 30B A3B
- Nanbeige4.2-3B vs Nemotron 3 Nano 30B A3B
- Nanbeige4.2-3B vs Nemotron 3 Nano 4B
- Nanbeige4.2-3B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nanbeige4.2-3B vs Nemotron 3 Super 120B A12B
- Nanbeige4.2-3B vs Nemotron 3 Ultra 550B A55B
- Nanbeige4.2-3B vs North Mini Code 1.0
- Nanbeige4.2-3B vs occamy-1.0
- Nanbeige4.2-3B vs Olmo 3 7B Think
- Nanbeige4.2-3B vs Ornith-1.5-122B
- Nanbeige4.2-3B vs Ornith-1.5-35B-A3B
- Nanbeige4.2-3B vs Ornith-1.5-397B
- Nanbeige4.2-3B vs Ornith-1.5-9B
- Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.2-3B vs Qwen-AgentWorld-35B-A3B
- Nanbeige4.2-3B vs Qwen3-30B-A3B-Instruct-2507
- Nanbeige4.2-3B vs Qwen3-4B-Instruct-2507
- Nanbeige4.2-3B vs Qwen3.5-0.8B
- Nanbeige4.2-3B vs Qwen3.5-122B-A10B
- Nanbeige4.2-3B vs Qwen3.5-2B
- Nanbeige4.2-3B vs Qwen3.5-397B-A17B
- Nanbeige4.2-3B vs Qwen3.5-4B
- Nanbeige4.2-3B vs Qwen3.5-9B
- Nanbeige4.2-3B vs Qwen3.6-27B
- Nanbeige4.2-3B vs Qwen3.6-35B-A3B
- Nanbeige4.2-3B vs Qwen3.8-27B
- Nanbeige4.2-3B vs Qwen3.8-Flash-Next
- Nanbeige4.2-3B vs Qwen3-Coder-30B-A3B-Instruct
- Nanbeige4.2-3B vs Qwen3-Coder-Next
- Nanbeige4.2-3B vs Qwen3-Omni-30B-A3B-Instruct
- Nanbeige4.2-3B vs Qwen3-VL-2B-Instruct
- Nanbeige4.2-3B vs Qwen3-VL-30B-A3B-Instruct
- Nanbeige4.2-3B vs Qwen3-VL-32B-Instruct
- Nanbeige4.2-3B vs Qwen3-VL-4B-Instruct
- Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct
- Nanbeige4.2-3B vs Qwopus3.8-27B-Flash-V2
- Nanbeige4.2-3B vs Recursive Flint
- Nanbeige4.2-3B vs Ripple-65B Preview
- Nanbeige4.2-3B vs SenseNova-Loop-9B-Preview
- Nanbeige4.2-3B vs SmolLM3-3B
- Nanbeige4.2-3B vs Spark-X2.6-4B
- Nanbeige4.2-3B vs Step 3.7 Flash
- Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-27B
- Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-Flash-Next
- Nanbeige4.2-3B vs ThinkingCap (Qwen3.8 27B)
- Nanbeige4.2-3B vs Baidu Unlimited-OCR-2
- Nanbeige4.2-3B vs WeVisOne-VL-2B
- Nanbeige4.2-3B vs WeVisOne-VL-4B
- Nanbeige4.2-3B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.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/nanbeige-4-2-3b.json - From a terminal
anchor compare kimi-k3-1-pro nanbeige-4-2-3b(the CLI) - Over MCP
compare_tools {"a": "kimi-k3-1-pro", "b": "nanbeige-4-2-3b"}at/mcp, no key