Model reference · open weights
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.
Summary of the Nanbeige/Nanbeige4.1-3B model card, 2026-10-01
What it is
| Released by | Nanbeige |
|---|---|
| Released | 2026-02-10 |
| Parameters | 3.9B |
| VRAM | 7.9 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB | 5 | 1 | 45K | 11.6 GB |
| RTX 4060 Ti 16 GB | 12 | 3 | 101K | 15.4 GB |
| RTX 3090 24 GB | 27 | 6 | 220K | 23.4 GB |
| RTX 4090 24 GB | 27 | 6 | 219K | 23.4 GB |
| RTX 5090 32 GB | 41 | 10 | all 256K | 31.0 GB |
| L40S 48 GB | 65 | 16 | all 256K | 44.0 GB |
| A100 80 GB | 129 | 32 | all 256K | 78.2 GB |
| H100 80 GB | 121 | 30 | all 256K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 150 | 37 | all 256K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 175 | 43 | all 256K | 107 GB |
| H200 141 GB | 232 | 58 | all 256K | 138 GB |
| B200 180 GB | 303 | 75 | all 256K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 25 | 6 | 207K | 11.6 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 9.1 GB | 10.7 GB |
| 5 | 11.3 GB | 19.3 GB |
| 8 | 12.9 GB | 25.8 GB |
| 16 | 17.2 GB | 43.0 GB |
| 32 | 25.8 GB | 77.3 GB |
| 64 | 43.0 GB | 146 GB |
One card, with vLLM's small-card settings.
From 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:
Technical Report: Link
We evaluate Nanbeige4.1-3B across a broad and diverse set of benchmarks covering general reasoning, and deep-search capabilities.
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.
| Benchmark | Qwen3-4B-2507 | Qwen3-8B | Qwen3-14B | Qwen3-32B | Qwen3-30B-A3B-2507 | Nanbeige4-3B-2511 | Nanbeige4.1-3B |
|---|---|---|---|---|---|---|---|
| Code | |||||||
| Live-Code-Bench-V6 | 57.4 | 49.4 | 55.9 | 55.7 | 66.0 | 46.0 | 76.9 |
| Live-Code-Bench-Pro-Easy | 40.2 | 41.2 | 33.0 | 42.3 | 60.8 | 40.2 | 81.4 |
| Live-Code-Bench-Pro-Medium | 5.3 | 3.5 | 1.8 | 3.5 | 3.5 | 5.3 | 28.1 |
| Math | |||||||
| AIME 2026 I | 81.46 | 70.42 | 76.46 | 75.83 | 87.30 | 84.1 | 87.40 |
| HMMT Nov | 68.33 | 48.33 | 56.67 | 57.08 | 71.25 | 66.67 | 77.92 |
| IMO-Answer-Bench | 48.00 | 36.56 | 41.81 | 43.94 | 54.34 | 38.25 | 53.38 |
| Science | |||||||
| GPQA | 65.8 | 62.0 | 63.38 | 68.4 | 73.4 | 82.2 | 83.8 |
| HLE (Text-only) | 6.72 | 5.28 | 7.00 | 9.31 | 11.77 | 10.98 | 12.60 |
| Alignment | |||||||
| Arena-Hard-v2 | 34.9 | 26.3 | 36.9 | 56.0 | 60.2 | 60.0 | 73.2 |
| Multi-Challenge | 41.14 | 36.30 | 36.97 | 38.72 | 49.40 | 41.20 | 52.21 |
| Tool Use | |||||||
| BFCL-V4 | 44.87 | 42.20 | 45.14 | 47.90 | 48.6 | 53.8 | 56.50 |
| Tau2-Bench | 45.9 | 42.06 | 44.96 | 45.26 | 47.70 | 41.77 | 48.57 |
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.
| Model | xBench-DeepSearch-2505 | xBench-DeepSearch-2510 | Browse-Comp | Browse-Comp-ZH | GAIA (Text-only) | HLE | SEAL-0 |
|---|---|---|---|---|---|---|---|
| Search-Specialized Small Agents | |||||||
| MiroThinker-v1.0-8B | 61 | – | 31.1 | 40.2 | 66.4 | 21.5 | 40.4 |
| AgentCPM-Explore-4B | 70 | – | 25.0 | 29.0 | 63.9 | 19.1 | 40.0 |
| Large Foundation Models (with Tools) | |||||||
| GLM-4.6-357B | 70 | – | 45.1 | 49.5 | 71.9 | 30.4 | – |
| Minimax-M2-230B | 72 | – | 44.0 | 48.5 | 75.7 | 31.8 | – |
| DeepSeek-V3.2-671B | 71 | – | 67.6 | 65.0 | 63.5 | 40.8 | 38.5 |
| Small Foundation Models (with Tools) | |||||||
| Qwen3-4B-2507 | 34 | 5 | 1.57 | 7.92 | 28.33 | 11.13 | 15.74 |
| Qwen3-8B | 31 | 2 | 0.79 | 5.15 | 19. |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.