Nex AGI · language model
Nex-N2.5-Mini: pricing, context and benchmarks
Nex-N2.5-Mini is a language model from Nex AGI released on September 8, 2026. It costs $0.03 per million input tokens and $0.1 per million output tokens, with a 262.1K-token context window. No independent score has been published for it so far.
What you need to know about Nex-N2.5-Mini
Nex-N2.5-Mini was released by Nex AGI on September 8, 2026. It accepts text and images as input and produces text. Its context window reaches 262,144 tokens, larger than 58 % of the models in the registry, and it can write up to 235,929 tokens in one go.
It is a recent model: real-world feedback and independent scores are still scarce.
Nex-N2.5-Mini pricing
On OpenRouter, Nex-N2.5-Mini costs $0.03 per million input tokens and $0.1 per million output tokens. Counting three tokens read for every token written, its average price is $0.044 per million, more expensive than 3 % of paid models (17.3 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0001. At 1,000 tasks a day, budget $3 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.003 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Nex-N2.5-Mini. We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It does not support tool calling: it is not suited to driving an AI agent that has to act.
- It can answer in an enforced JSON format (structured outputs).
- It is a reasoning model: it "thinks" before answering, which helps on hard tasks but uses more output tokens.
Its weights are open and downloadable from Hugging Face (apache-2.0 licence): you can run it on your own machines without sending your data to a third party. With 35,107,181,936 billion parameters, it needs about 19,617,666,641 GB of memory at 4-bit quantisation. Check with the VRAM calculator. It was downloaded 3,065 times on Hugging Face last month.
Other Nex AGI models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Nex-N2.5-Pro | Sep 8, 2026 | $0.12 | — |
Verdict
Nex-N2.5-Mini scores 80/100 on our grid, which only uses published figures.
Strengths
- Low price: cheaper than 97 % of paid models.
- Open weights: self-hostable, your data stays with you.
Limitations
- No tool calling: unusable as an agent's brain.
- No independent score published: performance cannot be verified.
Avoid if…
- you are building an agent that must call tools;
- you want to host it on a single consumer computer (≈ 19,617,666,641 GB needed at 4-bit).
Alternatives
- Ling 3.0 Flash$0.03
- Laguna XS 2.1$0.08
- Flintfree
- Xing4.0 29B A4Bfree
- Nemotron 3.5 Lightning$0.1
- Gemma 4 26B A4B$0.11
Comparisons and tools
Learn more
Frequently asked questions
How much does Nex-N2.5-Mini cost?
$0.03 per million input tokens and $0.1 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0001.
How big is Nex-N2.5-Mini's context window?
262,144 tokens, roughly 197,000 words, with a maximum of 235,929 output tokens.
Can Nex-N2.5-Mini run locally?
Yes, its weights are published under the apache-2.0 licence. Expect about 19,617,666,641 GB of memory at 4-bit.
Is Nex-N2.5-Mini suitable for an AI agent?
No: without tool calling it cannot act, only answer.
What are the alternatives to Nex-N2.5-Mini?
The closest models on price and performance are Ling 3.0 Flash, Laguna XS 2.1, and Flint.
Related articles
- Mistral AI: models, pricing and agentsOctober 25, 2026
- LLM benchmarks: how to read model rankingsOctober 24, 2026
- Running a local LLM: hardware, software, and limitationsOctober 24, 2026
- Open-source or closed models for your AI agents?October 5, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score