Nex-N2-Pro vs o1-pro (batch) compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
o1-pro (batch) — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
| Spec | Nex-N2-Pro | o1-pro (batch) | Winner |
|---|---|---|---|
| Maker | Nex agi | OpenAI | – |
| Type | Open-weight | Proprietary | NENex-N2-Pro |
| Context window | 262K tokens | 200K tokens | NENex-N2-Pro |
| Input price | $0.25/M · free self-host | $75/M | NENex-N2-Pro |
| Output price | $1/M · free self-host | $300/M | NENex-N2-Pro |
| Vision / multimodal | Yes | Yes | = Tie |
| Tool / function calling | Yes | No | NENex-N2-Pro |
| Self-hostable | Yes | No (API only) | NENex-N2-Pro |
| License | Open weights | Proprietary | NENex-N2-Pro |
Nex-N2-Pro is ~300× cheaper than o1-pro (batch) on output tokens ($1 vs $300 per M tokens).
| Capability | Nex-N2-Pro | o1-pro (batch) |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✓ |
| Tool / function calling | ✓ | ✗ |
| 1M+ context window | ✗ | ✗ |
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
| Criterion | Nex-N2-Pro | o1-pro (batch) |
|---|---|---|
| Cost-efficiency | 4.5 | 2.0 |
| Context window | 4.0 | 4.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 3.5 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
Nex-N2-Pro is an agentic mixture-of-experts model from Nex AGI, with 17B active parameters out of 397B total. Built on the Qwen3.5 architecture, it accepts text and image input and produces...
The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide...
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Nex-N2-Pro is open-weight and competitive on many tasks, but o1-pro (batch) may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Yes. Nex-N2-Pro has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. o1-pro (batch) is API-only and cannot be self-hosted.
Nex-N2-Pro costs $1/M output vs $300/M for o1-pro (batch) — roughly 300x cheaper via API, and free if you self-host.
Choose Nex-N2-Pro if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose o1-pro (batch) if you want frontier capability through a managed API with zero infrastructure to run.
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