Nex-N2.5-Max

Nex N2.5 Max

Quick answer: Nex-N2.5-Max is Nex-AGI's flagship model in the N2.5 family — a 1.6-trillion-parameter, text-only Mixture-of-Experts model and the lab's first complete post-training effort at trillion-parameter scale. It scores 86.1% on Terminal-Bench 2.1, 65.7% on SWE-Bench Pro, and 65.6% on DeepSWE v1.1, positioning it as Nex-AGI's top text-agent model, released alongside the smaller multimodal Nex-N2.5-Pro and Nex-N2.5-mini.

At a Glance

Where Nex N2.5 Max leads

• 86.1% Terminal-Bench 2.1 — ahead of GPT-5.6 Sol (88.8%)/Kimi-K3 (88.3%) is not quite matched, but close to frontier tier and best in the Nex family
• 65.7% SWE-Bench Pro and 65.6% DeepSWE v1.1 — clear step up from Nex-N2.5-Pro (61.2% / 55.8%)
• 74.7% Toolathlon Verified, 53.6% Job Bench — leads its own family on agentic/tool-use tasks
• First trillion-parameter-scale post-training run completed by Nex-AGI — signals infra/training maturity at that scale

Where it lags

• Text-only — no native multimodal input, unlike Nex-N2.5-Pro
• 92.6% BrowseComp trails GPT-5.6 Sol (90.4%)/Kimi-K3(91.2%) comparably but still behind Claude Opus 5 (90.8%) is close; overall still behind top closed frontier models on several agentic evals (e.g. AutomationBench v1.0.6 at 50.2% vs GLM-5.3's 48.2% — competitive but not leading)
• Undisclosed active-parameter count (backbone is 1.6T total, active portion not stated in the model card)

Best for: teams wanting Nex-AGI's strongest open-weight text-agent model for coding/tool-use workloads where multimodal input isn't required.

What Nex N2.5 Max Is

Nex-N2.5-Max is the largest model in Nex-AGI's second-generation N2.5 release, built on a 1.6-trillion-parameter, text-only Mixture-of-Experts foundation model. Nex-AGI describes it as their first complete post-training effort carried out at full trillion-parameter scale, following broader expansion of agent training environments, task types, and productivity scenarios versus the prior N2 generation.

Unlike its siblings Nex-N2.5-Pro and Nex-N2.5-mini — which continue the multimodal, computer-use-focused line — Max is text-only, positioned as the flagship for long-horizon coding and tool-use agent tasks rather than visual grounding. It supports the same reasoning_effort control (none/medium/high) as the rest of the family and uses a deepseek-r1-style reasoning parser in its reference SGLang deployment (contrasted with the qwen3 parser used for Pro/mini), suggesting a distinct post-training reasoning-trace format at this scale.

Specifications

Pricing

Open-weight model; self-hosting cost only. Multi-node (2×8×H200) deployment recipe published by Nex-AGI; no hosted-API listing found at time of writing.

Public Benchmark Scores

Scores above are reported by Nex-AGI and shown for context. They are not Benchgen measurements. Coding tasks use Nex-AGI's NexAU harness; sampling uses temperature=0.7, top_p=0.95, top_k=40. Nex-N2.5-Max is text-only and is not evaluated on the multimodal benchmark table (OSWorld, WebArena, Vision2Web, etc.) published for Nex-N2.5-Pro/mini.

Nex N2.5 Max vs Alternatives

Nex-N2.5-Max closes much of the gap to closed frontier models (Claude Opus 5, GPT-5.6 Sol) on agentic coding benchmarks versus its own smaller Pro sibling, but still trails Claude Opus 5 and DeepSeek V4 Pro on SWE-Bench Pro and Job Bench specifically — the trade-off is open weights and self-hostability rather than category-leading raw scores.

How Nex N2.5 Max Performs on Real Agent Tasks

As Nex-AGI's first full trillion-parameter-scale post-training run, Nex-N2.5-Max's benchmark profile shows broad, consistent gains over Nex-N2.5-Pro across coding and tool-use evals (Terminal-Bench 2.1, SWE-Bench Pro, DeepSWE v1.1, Toolathlon Verified, Job Bench) rather than a narrow specialization — evidence the scale-up primarily improved general agentic reliability rather than one task category. Because it's text-only, teams needing computer-use/vision-grounded agent behavior should evaluate Nex-N2.5-Pro instead; Max is the better fit for pure coding/tool-orchestration agents at self-hosted scale.

Use Nex N2.5 Max via API

from openai import OpenAI  # self-host via the published SGLang multi-node recipe, or check for OpenRouter/provider listings

client = OpenAI(api_key="YOUR_API_KEY", base_url="http://localhost:8000/v1")

response = client.chat.completions.create(
    model="nex-agi/nex-n2.5-max",
    messages=[{"role": "user", "content": "Refactor this repository's test suite..."}],
)
print(response.choices[0].message.content)

Related Models & Benchmarks

• Compare: Nex N2.5 Pro · DeepSeek V4 Pro
• Benchmarks: Terminal-Bench 2.1 · SWE-Bench Pro · DeepSWE

Frequently Asked Questions

Specs and scores sourced from Nex-AGI's official Hugging Face model card. Last updated 2026-09-15.