Model reference · open weights

Nanbeige4.1

LLMs Nanbeige Text gen 1 build Open weights 17k dl/mo

Nanbeige4.1 is an open-weight language model from Nanbeige. Nanbeige4.1-3B (BF16) weighs 7.9 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • Nanbeige4.1-3B is a 3.9B parameter text-generation model developed by Nanbeige that supports English and Chinese.
  • It features a 262,144 token context length and is designed for reasoning, preference alignment, and agentic tasks including deep search.
  • The model is released under the Apache 2.0 license.

Summary of the Nanbeige/Nanbeige4.1-3B model card, 2026-10-01

What it is

Released byNanbeige
Released2026-02-10
Parameters3.9B
VRAM7.9 GB for the weights

What it runs on

Memory and cards for Nanbeige4.1-3B (BF16)

7.9 GBweights, file size
66 MBcache per 1K tokens
728 MBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB5145K11.6 GB
RTX 4060 Ti 16 GB123101K15.4 GB
RTX 3090 24 GB276220K23.4 GB
RTX 4090 24 GB276219K23.4 GB
RTX 5090 32 GB4110all 256K31.0 GB
L40S 48 GB6516all 256K44.0 GB
A100 80 GB12932all 256K78.2 GB
H100 80 GB12130all 256K78.1 GB
RTX PRO 6000 Blackwell 96 GB15037all 256K93.8 GB
DGX Spark (GB10) 128 GB unified17543all 256K107 GB
H200 141 GB23258all 256K138 GB
B200 180 GB30375all 256K176 GB
2× RTX 3060 12 GB
tensor parallel
256207K11.6 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
19.1 GB10.7 GB
511.3 GB19.3 GB
812.9 GB25.8 GB
1617.2 GB43.0 GB
3225.8 GB77.3 GB
6443.0 GB146 GB

One card, with vLLM's small-card settings.

From the model card

What Nanbeige says about Nanbeige4.1

Read the model card

Nanbeige4.1-3B is built upon Nanbeige4-3B-Base and represents an enhanced iteration of our previous reasoning model, Nanbeige4-3B-Thinking-2511, achieved through further post-training optimization with supervised fine-tuning (SFT) and reinforcement learning (RL). As a highly competitive open-source model at a small parameter scale, Nanbeige4.1-3B illustrates that compact models can simultaneously achieve robust reasoning, preference alignment, and effective agentic behaviors.

Specifically, Nanbeige4.1-3B exhibits the following key strengths:

  • Strong Reasoning: Nanbeige4.1-3B is capable of solving complex, multi-step problems through sustained and coherent reasoning within a single forward pass, and reliably produces correct final answers on challenging tasks such as LiveCodeBench-Pro, IMO-Answer-Bench, and AIME 2026 I.
  • Robust Preference Alignment: Nanbeige4.1-3B achieves solid alignment performance, outperforming not only same-scale models such as Qwen3-4B-2507 and Nanbeige4-3B-2511, but also substantially larger models including Qwen3-30B-A3B and Qwen3-32B on Arena-Hard-v2 and Multi-Challenge.
  • Agentic Capability: Nanbeige4.1-3B is the first general small model to natively support deep-search tasks and reliably sustain complex problem solving involving more than 500 rounds of tool invocations. It fills a long-standing gap in the small-model ecosystem where models are typically optimized for either general reasoning or agentic scenarios, but rarely excel at both.

Technical Report: Link

Performances

We evaluate Nanbeige4.1-3B across a broad and diverse set of benchmarks covering general reasoning, and deep-search capabilities.

General Reasoning Tasks

On general reasoning tasks including code, math, science, alignment, and tool-use benchmarks, Nanbeige4.1-3B not only significantly outperforms same-scale models such as Qwen3-4B, but also demonstrates overall superior performance compared to larger models including Qwen3-30B-A3B-2507 and Qwen3-32B.

BenchmarkQwen3-4B-2507Qwen3-8BQwen3-14BQwen3-32BQwen3-30B-A3B-2507Nanbeige4-3B-2511Nanbeige4.1-3B
Code
Live-Code-Bench-V657.449.455.955.766.046.076.9
Live-Code-Bench-Pro-Easy40.241.233.042.360.840.281.4
Live-Code-Bench-Pro-Medium5.33.51.83.53.55.328.1
Math
AIME 2026 I81.4670.4276.4675.8387.3084.187.40
HMMT Nov68.3348.3356.6757.0871.2566.6777.92
IMO-Answer-Bench48.0036.5641.8143.9454.3438.2553.38
Science
GPQA65.862.063.3868.473.482.283.8
HLE (Text-only)6.725.287.009.3111.7710.9812.60
Alignment
Arena-Hard-v234.926.336.956.060.260.073.2
Multi-Challenge41.1436.3036.9738.7249.4041.2052.21
Tool Use
BFCL-V444.8742.2045.1447.9048.653.856.50
Tau2-Bench45.942.0644.9645.2647.7041.7748.57

Deep Search Tasks

As a general small model, Nanbeige4.1-3B achieves deep-search performance comparable to specialized agents under 10B parameters. In contrast to existing small general models, which typically exhibit little to no deep-search capability, Nanbeige4.1-3B represents a substantial qualitative improvement over prior small general models.

Deep Search and Agent Benchmarks
ModelxBench-DeepSearch-2505xBench-DeepSearch-2510Browse-CompBrowse-Comp-ZHGAIA (Text-only)HLESEAL-0
Search-Specialized Small Agents
MiroThinker-v1.0-8B61–31.140.266.421.540.4
AgentCPM-Explore-4B70–25.029.063.919.140.0
Large Foundation Models (with Tools)
GLM-4.6-357B70–45.149.571.930.4–
Minimax-M2-230B72–44.048.575.731.8–
DeepSeek-V3.2-671B71–67.665.063.540.838.5
Small Foundation Models (with Tools)
Qwen3-4B-25073451.577.9228.3311.1315.74
Qwen3-8B3120.795.1519.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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