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ModelCap

Nex-N2.5-Pro

Official publisher releaseOpen weights

Nex AGI·#29 of 350 on the current board·cheaper than #26 Seed 2.1 Turbo

Nex-N2.5-Pro is a language model from Nex AGI. It ranks #29 of 350 on ModelCap's current board, with an Index score of 75.6, modeled from the publisher's launch results against measured models. Its score range of 67.3–83.9 spans positions #5–#46. It is listed at $0.075 per million input tokens and $0.25 per million output tokens from one provider. It reads up to 262K tokens of context, and its weights are openly downloadable.

Figures as of 24 Oct 2026, 19:14 UTC · download the public dataset · methodology

ModelCap Index · Current board
75.6#29
Modeled from the publisher's launch results against measured models · range 67.3–83.9
Market Gravity
14.6Decreased by 0.4
Secondary market signal
Input
$0.075
per 1M tokens
Output
$0.25
per 1M tokens
Context
262K
tokens
Providers
1
endpoint

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
Nex AGIfp8$0.075$0.25262Kfp8—

Reported evaluation leads (4)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.

Dataset / taskMetricValueRevisionProvenance
datacurve/deep-swe
deep_swe
—55.8
Unpinned
not published
Reported
Not used in launch estimate
harborframework/terminal-bench-2.1
terminalbench_2_1
—82.7
Unpinned
not published
Reported
Used in launch estimate
hkust-nlp/Toolathlon
toolathlon_verified
—68.5
Unpinned
not published
Reported
Not used in launch estimate
ScaleAI/SWE-bench_Pro
SWE_Bench_Pro
—61.2
Unpinned
not published
Reported
Used in launch estimate

Other versions (1)

ModelCap Index · Current board

Methodology
75.6
Modeled from the publisher's launch results against measured models
Current board
#29 of 350
Score range
67.3–83.9
Rank range
#5–#46
Technical details
Public rank basis
Modeled · peer benchmarks (architecture)
Evidence label
Modeled · peer benchmarks · global-corpus-prior over 211 held-out anchors (8%); launch card against 8 resolved peers on 8 rows (92%); exceeds every named peer on 1 of 8 rows; optimism haircut 0 from cross-lab-probe; shrunk 2 toward the measured corpus
Index support
38.0% · limited
Identity binding
Exact catalogue product · nex-agi/nex-agi/nex-n2.5-pro · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata nex-agi/Nex-N2.5-Pro@99842a1e9a40904a671c3cbabbf0530e2a88d002 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
75.6
How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate
75.6 ± 6.5
Independent share
0%
Shrink toward corpus
-2.0
Peer ceiling share
13%
Optimism haircut
None required · cross lab probe, 7 probes
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Publisher corpus prior · not independent
global-corpus-prior over 211 held-out anchors
52.3 ± 23.38%
Launch card against resolved peers · not independent
launch card against 8 resolved peers on 8 rows
77.6 ± 6.892%
  • · Launch card row automationbench-v-1-0-6-5: inside the resolved peer range
  • · Launch card row deepswe-v-1-1: claims below every resolved peer
  • · Launch card row osworld-2: inside the resolved peer range
  • · Launch card row osworld-g: claims above every resolved peer
  • · Launch card row osworld-verified-8: inside the resolved peer range
  • · Launch card row swe-bench-pro: inside the resolved peer range
  • · Launch card row terminal-bench-2-1: claims below every resolved peer
  • · Launch card row toolathlon-verified: claims below every resolved peer
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
—
Evidence status
No admitted measured evidence
General preference
—
Coding
—
Agents & tools
—
Reasoning
—
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
8.2
Liquidity (providers × uptime)
22.1
Open reach (HF downloads)
2.1
Surface (context / tools / modalities)
90.3
Freshness
83.9
  • · OpenRouter popularity #212
  • · 1 live provider
  • · HF 30d downloads 1961
  • · Catalogue: context 262144, reasoning, tools, modalities Text/Image
  • · First seen 2026-09-08

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
65.5
52.1–78.8 uncertainty interval
Excluded from rank
Confidence
80%
Training support
221 models · 129 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
8.3 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
68.7
partial · 76% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
77.4
Evaluation provenance
0.0

Not yet published: the base model it derives from, admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
14.6
OpenRouter weekly popularity
#212 of 342

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
8.2

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
22.1

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
2.1

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
83.9

Time since first public availability, on a six-month half-life.

Specification

Model ID
nex-agi/nex-n2.5-pro
Context window
262K tokens
Declared output cap
236K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

Inputs
Text, Image
Outputs
Text
Tokenizer
Qwen3
Cached input tokens
$0.015 / 1M
First seen on OpenRouter
8 Sept 2026

Capabilities

  • Supported: Reasoning
  • Supported: Tool use
  • Supported: Structured output
  • Not supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
apache-2.0
Pinned revision
99842a1e9a40
Parameters
396.8B
Architecture
Qwen3_5MoeForConditionalGeneration
Model type
qwen3_5_moe
Hugging Face downloads (30d)
2K
Downloads (all time)
34K
Likes
678
Repository updated
17 Sept 2026, 05:52 UTC

Alternatives to Nex-N2.5-Pro, by the numbers

Ranked neighbours

Models within 3 places of #29 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked Nex AGI models

The same publisher's other current models with a public Index position.

Nex-N2.5-Pro: common questions

How does Nex-N2.5-Pro rank among AI models?

Nex-N2.5-Pro holds ModelCap Index position #29 of 350 ranked language models as of 24 October 2026, with an Index score of 75.6 (interval 67.3–83.9); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Nex-N2.5-Pro cost per 1M tokens?

Nex-N2.5-Pro is listed at $0.075 per 1M input tokens and $0.25 per 1M output tokens as of 24 October 2026. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.275 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Nex-N2.5-Pro?

Nex-N2.5-Pro has a published context window of 262K tokens (262,144). The snapshot lists a 235,929 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is Nex-N2.5-Pro open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is nex-agi/Nex-N2.5-Pro on Hugging Face.

Which API providers serve Nex-N2.5-Pro?

One provider serves Nex-N2.5-Pro through OpenRouter as of 24 October 2026: Nex AGI. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Nex-N2.5-Pro been evaluated on?

Nex-N2.5-Pro's Index position rests on the evidence listed on this page; it has no result on the public boards ModelCap tracks as separate leaderboards as of 24 October 2026. The evidence panel names each source and its publication date.

When was Nex-N2.5-Pro released?

Nex-N2.5-Pro first appeared in the catalogue on 8 Sept 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.