Nex-AGI Releases Open-Weight Long-Task Model Nex-N2.5-mini
At a Glance
| Item | Value |
|---|---|
| Repository | nex-agi/Nex-N2.5-mini |
| Published | 2026-09-08 |
| License | apache-2.0 |
| Formats | safetensors |
| Source type | Primary source (the publisher itself) |
Values determined by this site’s code at collection time. Dates are JST.
Overview
Nex-AGI has released “Nex-N2.5-mini", a multimodal foundational model from its next-generation agent model family “Nex-N2.5" designed for long-running tasks in real-world environments. It focuses on computer operation, web browsing, and self-correction through visual feedback, and is available as an open-weight model.
Specifications
- License:
apache-2.0 - Architecture:
Qwen3_5MoeForConditionalGeneration(qwen3_5_moe)
Performance
Comparison tables for text and multimodal benchmarks provided in the model card are shown below. The comparisons include “Nex-N2.5-Pro", “Nex-N2.5-Max", and other major models.
Text Benchmarks
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| Benchmark / CODING 3 | Nex-N2.5-mini / CODING 3 | Nex-N2.5-Pro / CODING 3 | Nex-N2.5-Max / CODING 3 | Claude Opus 5 / CODING 3 | GPT-5.6 Sol / CODING 3 | Kimi-K3 / CODING 3 | GLM-5.3 / CODING 3 | DeepSeek-V4-Pro-0813 4 / CODING 3 | Qwen3.8-Max / CODING 3 |
|---|---|---|---|---|---|---|---|---|---|
| Terminal-Bench 2.1 | 73.4 | 82.7 | 86.1 | 89.1 | 88.8 | 88.3 | 88.2 | 87.9 | 86.6 |
| SWE-Bench Pro | 43.8 | 61.2 | 65.7 | 79.2 | 64.6 | 63.3 | 64.6 | 55.4 | 67.7 |
| DeepSWE v1.1 | 36.1 | 55.8 | 65.6 | 73.7 | 72.7 | 67.5 | 66.9 | 62.8 | 69.3 |
| AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC |
| AutomationBench v1.0.6 5 | 32.3 | 44.2 | 50.2 | 50.3 | 45.8 | 46.7 | 48.2 | 43.2 | 39.8 |
| Toolathlon Verified | 54.6 | 68.5 | 74.7 | 76.5 | 74.9 | 76.5 | 73.0 | 74.1 | 72.5 |
| GDPval-AA v2 | 1446 | 1628 | 1713 | 1831 | 1711 | 1675 | 1763 | 1580 | 1717 |
| Job Bench | 28.5 | 41.4 | 53.6 | 65.7 | 45.4 | 52.9 | 58.2 | 54.1 | 53.4 |
| BrowseComp 6 | 83.4 | 89.7 | 92.6 | 90.8 | 90.4 | 91.2 | — | — | — |
Multimodal Benchmarks
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| Benchmark | Nex-N2.5-mini | Nex-N2.5-Pro | MiniMax-M3 | Claude Opus 5 | GPT-5.6 Sol | Kimi-K3 | GLM-5.3-Flash | DeepSeek-V4-Flash-Vision | Qwen3.8-Max |
|---|---|---|---|---|---|---|---|---|---|
| OSWorld-Verified 8 | 71.2 | 82.2 | 75.2 | 83.4 | 83.2 | 84.8 | 62.3 | 76.7 | 86.1 |
| OSWorld-2 | 30.5 | 56.4 | 22.3 | 68.3 | 62.7 | 58.3 | — | — | 46.7 |
| WebTest 8, 9 | 48.6 | 52.8 | — | — | 54.0 | — | — | — | 52.3 |
| WebArena-Verified 8 | 63.4 | 67.6 | — | — | 69.7 | 71.6 | — | 62.3 | 66.8 |
| OSWorld-G | 82.9 | 87.4 | — | 76.8 | 77.7 | 79.6 | 83.3 | 59.4 | 84.9 |
| Vision2Web 7 | 52.9 | 68.2 | 59.0 | — | 79.8 | — | — | — | 75.1 |
| SWE-MM | 25.5 | 38.2 | — | 59.4 | 40.2 | 37.3 | 20.6 | 39.2 | 39.2 |
| OmniDoc | 89.7 | 92.2 | 91.6 | — | 92.9 | 91.1 | — | — | 92.1 |
According to the evaluation results from the publishers, Nex-N2.5-mini is the lightest model in the family, and its scores tend to be generally lower compared to the higher-tier Pro and Max models as well as large models from other companies. On the other hand, it demonstrates solid performance in certain tasks, scoring 73.4 on Terminal-Bench 2.1 and 83.4 on BrowseComp. In multimodal benchmarks such as OSWorld-Verified and OmniDoc, while its numbers are lower than the Pro model in the family and other models, it maintains a certain standard.
Strengths and Use Cases
It is built with a focus on operating as an agent for computer operations, web browsing, and visual grounding. A key characteristic is that it utilizes vision not merely as an input modality, but as a critical interface for perceiving the environment, verifying results, and progressing through tasks.
Hardware Requirements
Estimated requirements (calculated by Local Model Watch) — 35.1B parameters
| Your VRAM | Quantization | File size | Est. memory needed |
|---|---|---|---|
| 16GB (RTX 5060 Ti 16GB / 4060 Ti 16GB, etc.) | Q2_K | 12.9GB | 15.5GB |
| 24GB (RTX 4090 / 3090, etc.) | Q4_K_S | 19.5GB | 23.4GB |
| 32GB (RTX 5090, etc.) | Q5_K_M | 25.1GB | 30.2GB |
| 48GB (RTX 6000 Ada / A6000, etc.) | Q8_0 | 34.4GB | 41.3GB |
| 80GB class (A100 / H100) | BF16 | 64.6GB | 77.5GB |
Inference engine support (architecture name matched against each project’s own model registry in its source code, checked 2026-09-18): llama.cpp: registered, vLLM: registered, MLX (mlx-lm): registered.
Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. File sizes are measured from the converted build bartowski/nex-agi_Nex-N2.5-mini-GGUF. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.
Recent Models in the Same Size Class
Models with 15–40B parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.
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| Model | Parameters | Smallest VRAM tier | License | Our article |
|---|---|---|---|---|
| Edge0/Edge0-35B-A3B-preview | 34.7B | 80GB | apache-2.0 | Edge0-35B-A3B-Preview: Sparse MoE for Phone-Class Memory (2026-09-11) |
| bartowski/Gryphe_Pantheon-Reasoning-26B-A4B-1.1-V2-GGUF | 26.5B | 12GB | apache-2.0 | Pantheon-Reasoning-26B-A4B-1.1-V2 GGUF Quantizations (2026-09-11) |
How to Get It
Model weights are available as open source from Hugging Face and ModelScope.
- Hugging Face:
nex-agi/Nex-N2.5-mini
Quantized and Converted Variants
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| Added | Publisher | Format | Repository | Smallest VRAM tier (build, est. memory) |
|---|---|---|---|---|
| 2026-09-18 | bartowski | GGUF | bartowski/nex-agi_Nex-N2.5-mini-GGUF | Q2_K 15.5GB (fits in 16GB VRAM) |
| 2026-09-18 | mradermacher | GGUF | mradermacher/Nex-N2.5-mini-GGUF | Q2_K 14.5GB (fits in 16GB VRAM) |
| 2026-09-18 | mlx-community | MLX | mlx-community/Nex-N2.5-mini-OptiQ-4bit | MLX 4bit 25.8GB (fits in 32GB VRAM) |
| 2026-09-18 | mlx-community | MLX | mlx-community/Nex-N2.5-mini-oQ4 | MLX 22.8GB (fits in 24GB VRAM) |
In addition, 32 converted build(s) from other uploaders exist on Hugging Face; this site lists only builds from the model’s publisher or established quantization maintainers.
This section is appended automatically by Local Model Watch when a converted build of this model appears after publication. Memory figures are estimated from the size of the distributed files.