Nex-N2.5-Pro (free)
By Nex AGI. 396.8 billion parameters, a context window of 262,144 tokens and the licence apache-2.0.
Facts
- Released
- 2026-09-08 the day the repository was first published on Hugging Face Hugging Face, read
- Licence
- apache-2.0 the licence the model card declares Hugging Face, read
- Open weights
- Yes the weights are published in this Hugging Face repository Hugging Face, read
- Parameters
- 396.8 billion counted from the safetensors weight files Hugging Face, read
- Active parameters
- 17.8 billion estimate estimated from the expert counts and sizes in the configuration model configuration, read
- Knowledge cutoff
- Unknown
- Context window
- 262,144 tokens the maximum position embeddings in the model configuration model configuration, read
- 262,144 tokens the context length OpenRouter lists OpenRouter, read
- Longest output
- 235,929 tokens the largest output OpenRouter's first provider allows OpenRouter, read
- Tool calling
- Yes the chat template in the tokenizer configuration accepts tool definitions Hugging Face, read
- Yes OpenRouter lists tools among the supported parameters OpenRouter, read
- Structured output
- Yes OpenRouter lists structured outputs among the supported parameters OpenRouter, read
- Reasoning controls
- Yes OpenRouter lists reasoning controls, with the efforts high, medium, none OpenRouter, read
- Inputs and outputs
- text in, text out Hugging Face, read
- Good for
- Reasoning: OpenRouter lists reasoning controls for it OpenRouter, read
- Vision: OpenRouter lists images among its inputs OpenRouter, read
Prices
No source lists a price for this model. It may only run on your own hardware.
Run it on your own hardware
Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.
| Quantization | Weights | Memory at 8,192 tokens | Memory at 32,768 tokens |
|---|---|---|---|
| IQ1_S | 81.8 GiB | 86.7 GiB | 87.4 GiB |
| IQ1_M | 91.3 GiB | 96.6 GiB | 97.3 GiB |
| IQ2_XXS | 107 GiB | 113 GiB | 114 GiB |
| IQ2_XS | 113 GiB | 119 GiB | 120 GiB |
| IQ2_S | 119 GiB | 126 GiB | 126 GiB |
| IQ2_M | 133 GiB | 140 GiB | 141 GiB |
| Q2_K | 140 GiB | 148 GiB | 149 GiB |
| IQ3_XXS | 158 GiB | 167 GiB | 167 GiB |
| IQ3_XS | 166 GiB | 175 GiB | 176 GiB |
| Q3_K_S | 168 GiB | 177 GiB | 177 GiB |
| Q3_K_M | 177 GiB | 186 GiB | 187 GiB |
| Q3_K_L | 186 GiB | 197 GiB | 197 GiB |
| IQ3_M | 195 GiB | 206 GiB | 206 GiB |
| IQ4_XS | 204 GiB | 215 GiB | 216 GiB |
| Q4_0 | 210 GiB | 221 GiB | 222 GiB |
| Q4_K_S | 217 GiB | 229 GiB | 230 GiB |
| Q4_1 | 232 GiB | 244 GiB | 245 GiB |
| IQ4_NL | 232 GiB | 245 GiB | 245 GiB |
| Q4_K_M | 234 GiB | 246 GiB | 247 GiB |
| Q5_K_S | 263 GiB | 277 GiB | 278 GiB |
| Q5_K_M | 285 GiB | 300 GiB | 300 GiB |
| Q6_K_S | 310 GiB | 327 GiB | 327 GiB |
| Q6_K | 326 GiB | 343 GiB | 344 GiB |
| Q8_0 | 393 GiB | 413 GiB | 414 GiB |
Start it with a local runtime
- Ollama
ollama run hf.co/bartowski/Nex-N2.5-Pro-GGUF:Q4_0 - LM Studio
lms get bartowski/Nex-N2.5-Pro-GGUF lms server start - llama.cpp server
llama-server -hf bartowski/Nex-N2.5-Pro-GGUF:Q4_0 --jinja - vLLM
vllm serve nex-agi/Nex-N2.5-Pro - SGLang
sglang serve --model-path nex-agi/Nex-N2.5-Pro --port 30000
Use it from your harness
Set up for Ollama with the model hf.co/bartowski/Nex-N2.5-Pro-GGUF:Q4_0. Each endpoint page has the same setup for its own address.
OpenCode
Put this in opencode.json in your project folder:
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"ollama": {
"npm": "@ai-sdk/openai-compatible",
"name": "Ollama",
"options": {
"baseURL": "http://localhost:11434/v1"
},
"models": {
"hf.co/bartowski/Nex-N2.5-Pro-GGUF:Q4_0": {
"name": "Nex-N2.5-Pro (free)",
"limit": {
"context": 262144,
"output": 235929
}
}
}
}
}
}- OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.
From OpenCode documentation, read .
Pi
Put this in ~/.pi/agent/models.json:
{
"providers": {
"ollama": {
"baseUrl": "http://localhost:11434/v1",
"api": "openai-completions",
"apiKey": "ollama",
"models": [
{
"id": "hf.co/bartowski/Nex-N2.5-Pro-GGUF:Q4_0"
}
]
}
}
}- The apiKey field can name an environment variable as $NAME.
From Pi documentation, read .
Codex
Put this in a terminal:
codex --oss --local-provider ollama -m hf.co/bartowski/Nex-N2.5-Pro-GGUF:Q4_0- Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.
From Codex documentation, read .
Claude Code
Put this in ~/.claude/settings.json, or variables in your shell:
export ANTHROPIC_BASE_URL="http://localhost:11434"
export ANTHROPIC_AUTH_TOKEN="ollama"
export ANTHROPIC_MODEL="hf.co/bartowski/Nex-N2.5-Pro-GGUF:Q4_0"
claude- Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.
From Claude Code documentation, read .
Published results
These are results other people published. Baltor did not run them and does not rank models by them.
- Results the maker published on the model card published by nex-agi Hugging Face, read
Sources of this page
Paid links
No link in this directory is a paid link or an ad, and no listing is paid for. The order and the contents of every list come from the sources named on this page.