Nex-AGI Releases Agent Model Nex-N2.5-Pro
At a Glance
| Item | Value |
|---|---|
| Repository | nex-agi/Nex-N2.5-Pro |
| 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 announced “Nex-N2.5", a family of agent models designed for real-world long-horizon tasks. Available in three sizes—mini, Pro, and Max—the model weights are open-sourced on platforms like Hugging Face. A key feature is the enhanced continuous action and self-correction capabilities driven by visual feedback.
Specifications
- License: apache-2.0
- Library: transformers
- Pipeline task: text-generation
Performance
Evaluation results, based on model card descriptions and publisher measurements, include both text and multimodal benchmarks. Scores for Nex-N2.5-mini, Nex-N2.5-Pro, and Nex-N2.5-Max are listed alongside major competitor models for comparison.
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 |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | 73.4 | 82.7 | 86.1 | 89.1 | 88.8 |
| SWE-Bench Pro | 43.8 | 61.2 | 65.7 | 79.2 | 64.6 |
| DeepSWE v1.1 | 36.1 | 55.8 | 65.6 | 73.7 | 72.7 |
| AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC | AGENTIC |
| AutomationBench v1.0.6 5 | 32.3 | 44.2 | 50.2 | 50.3 | 45.8 |
| Toolathlon Verified | 54.6 | 68.5 | 74.7 | 76.5 | 74.9 |
| GDPval-AA v2 | 1446 | 1628 | 1713 | 1831 | 1711 |
| Job Bench | 28.5 | 41.4 | 53.6 | 65.7 | 45.4 |
| BrowseComp 6 | 83.4 | 89.7 | 92.6 | 90.8 | 90.4 |
Multimodal Benchmarks
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| Benchmark | Nex-N2.5-mini | Nex-N2.5-Pro | MiniMax-M3 | Claude Opus 5 | GPT-5.6 Sol |
|---|---|---|---|---|---|
| OSWorld-Verified 8 | 71.2 | 82.2 | 75.2 | 83.4 | 83.2 |
| OSWorld-2 | 30.5 | 56.4 | 22.3 | 68.3 | 62.7 |
| WebTest 8, 9 | 48.6 | 52.8 | — | — | 54.0 |
| WebArena-Verified 8 | 63.4 | 67.6 | — | — | 69.7 |
| OSWorld-G | 82.9 | 87.4 | — | 76.8 | 77.7 |
| Vision2Web 7 | 52.9 | 68.2 | 59.0 | — | 79.8 |
| SWE-MM | 25.5 | 38.2 | — | 59.4 | 40.2 |
| OmniDoc | 89.7 | 92.2 | 91.6 | — | 92.9 |
According to evaluations by the publishers, Nex-N2.5-Pro achieves 82.7 on Terminal-Bench 2.1 and 61.2 on SWE-Bench Pro. It also records a score of 82.2 on OSWorld-Verified. While it demonstrates solid performance in terminal operation and agent-related tasks, it trails behind the top-tier Max model and certain comparison models in some metrics. Note that the reasoning_effort parameter can be used to control the thinking mode.
Strengths and Use Cases
- Computer operation, web browsing, and visually grounded agent capabilities (computer use)
- Long-horizon tasks and autonomous program execution/testing
- Scientific research, knowledge work, and complex productivity tasks
- Function calling and utilization of reasoning parsers
Hardware Requirements
Estimated requirements (calculated by Local Model Watch) — 396.8B parameters
| Your VRAM | Quantization | File size | Est. memory needed |
|---|---|---|---|
| More than 98GB of VRAM (multi-GPU or CPU offload required) | IQ1_S | 81.8GB | 98.2GB |
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-N2.5-Pro-GGUF. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.
How to Get It
- Distribution: Model weights are available on platforms such as Hugging Face and ModelScope.
- Supported Engines & Deployment: Deployment via SGLang (using a custom
nexagi/sglang:v0.5.18-nex-patchDocker image) is provided, and can be launched from the command line.
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-N2.5-Pro-GGUF | IQ1_S 98.2GB (does not fit a single consumer GPU) |
| 2026-09-18 | DevQuasar | GGUF | DevQuasar/nex-agi.Nex-N2.5-Pro-GGUF | Q2_K 161.8GB (does not fit a single consumer GPU) |
In addition, 2 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.