Nex-N2-Pro
Nex AGI · released Jun 3, 2026 · nex-agi/Nex-N2-Pro
- Type
- Open weightsApache License 2.0
- Params
- 397B
- Context
- 262K
active per word not recorded by us · about 197K words of context
Our take
Written Sep 30, 2026Nex-N2-Pro is a downloadable text-and-image model you can run yourself or reach through a host, released under terms that allow commercial use, changes and redistribution. Nothing here measures how good its answers are, so the decision rests on the bill and on a trial of your own.
Use it for work where you can judge the output yourself and the bill matters more than a measured quality figure, or for long documents that would otherwise need splitting before they go in. Its licence allows commercial use, changes and redistribution (Apache License 2.0). Skip it if you need evidence of coding, reasoning or chat quality before committing, because nothing here measures any of them.
The case for it
- The licence allows commercial use, changes and redistribution (Apache License 2.0), so the terms are not the thing to weigh here.
- Two of the three hosts list the same rate, well under the third, so the host you pick changes the bill several times over.
- Text and images go into the same request, so a screenshot does not have to be described in words first.
The case against it
- No benchmark scores are supplied, so chat, coding and reasoning all need a trial on work you can check yourself.
- 397 billion parameters in total with no figure for how many work on any one token, so the memory it needs in use is unverified in our data.
How good is it?
We hold no score for this model.
So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.
Can you run it yourself?
GeForce RTX 4090 · 24 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M1 Pro (16-core GPU) · 32 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Comfortable fit
Apple M3 Ultra (80-core GPU) · 512 GB
Room to spare. 124.9 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked between 8 days and 25 days ago — each listing carries its own date.
- per 1M tokens
- $0.075 in / $0.25 out
- Context served
- 262K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.075 / $0.25checked 8 days ago | 262K | not measured | Unknown | Unknown | Unknown |
| Nex AGIfp8Through OpenRouter | $0.075 / $0.25checked 8 days ago | 262K236K max reply | not measured | No | Yes30 days | Unknown |
| SiliconFlowThrough OpenRouter | $0.50 / $2.50checked 25 days ago | 262K | not measured | No | No | Unknown |
Across the 3 listings we hold: 2 say they do not train on prompts, 0 say they do and 1 does not say. 0 appear in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouterOpenRouter's own listing | ✓ | ✗ | ✓ |
| Nex AGIfp8Through OpenRouter | ✓ | ✗ | ✓ |
| SiliconFlowThrough OpenRouter | ✓ | ✗ | ✗ |
Tool calling: 3 of 3 listings say yes. JSON output: 0 of 3 listings say yes, 3 say no. Strict schema: 2 of 3 listings say yes, 1 says no.
Models people weigh against Nex-N2-Pro
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No independent board has scored it, so we hold no quality figures at all.
- We do not hold the active parameter count for it, so how much of it runs on any one token is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 3 listings does not say whether it trains on prompts.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsApache License 2.0, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- nex-agi/Nex-N2-Pro
- Architecture
- Mixture of experts
- Takes in, gives back
- Text and images in, text out
- Catalogue slug
- nex-agi-nex-n2-pro