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ModelCap

Ling 3.0 Tiny

Official publisher releaseOpen weights1.3d #140 → #139

InclusionAI·#139 of 352 on the current board·cheaper than #137 Solar Pro 4

Ling 3.0 Tiny is a language model from InclusionAI. It ranks #139 of 352 on ModelCap's current board, with an Index score of 48.1, modeled from the publisher's launch results against measured models. Its score range of 27.2–69.0 spans positions #47–#251. It is listed at $0.015 per million input tokens and $0.045 per million output tokens from one provider. It reads up to 262K tokens of context, and its weights are openly downloadable.

Figures as of 6 Nov 2026, 13:52 UTC · download the public dataset · methodology

ModelCap Index · Current board
48.1#139
Modeled from the publisher's launch results against measured models · range 27.2–69.0
Market Gravity
15.8Increased by 0.7
Secondary market signal
Input
$0.015
per 1M tokens
Output
$0.045
per 1M tokens
Context
262K
tokens
Providers
1
endpoint

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
Ant Digitalfp8$0.015$0.045262Kfp8100.0%

ModelCap Index · Current board

Methodology
48.1
Modeled from the publisher's launch results against measured models
Current board
#139 of 352
Score range
27.2–69.0
Rank range
#47–#251
Technical details
Public rank basis
Modeled · peer benchmarks (architecture)
Evidence label
Modeled · peer benchmarks · 6 reported rows against measured corpus ladders (53%); global-corpus-prior over 210 held-out anchors (47%); shrunk 4.7 up toward the measured corpus
Index support
26.1% · limited
Identity binding
Exact catalogue product · inclusionai/inclusionai/ling-3.0-tiny · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata inclusionAI/Ling-3.0-tiny@9a98e35fe1c9ee255f78dd64771c7ae15a799481 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
48.1
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
48.1 ± 16.3
Independent share
0%
Shrink toward corpus
+4.7
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Corpus ladder · not independent
6 reported rows against measured corpus ladders
43.4 ± 22.453%
Publisher corpus prior · not independent
global-corpus-prior over 210 held-out anchors
53.5 ± 23.847%
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)
5.2
Liquidity (providers × uptime)
24.3
Open reach (HF downloads)
22.1
Surface (context / tools / modalities)
75.3
Freshness
70.5
  • · OpenRouter popularity #251
  • · 1 live provider
  • · HF 30d downloads 14660
  • · Catalogue: context 262144, reasoning, tools
  • · First seen 2026-08-06

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
39.9
25.8–53.9 uncertainty interval
Excluded from rank
Confidence
78%
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
69.5
partial · 76% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
80.0
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
15.8
OpenRouter weekly popularity
#251 of 339

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%
5.2

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
24.3

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

Open reach15%
22.1

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

Freshness5%
70.5

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

Specification

Model ID
inclusionai/ling-3.0-tiny
Context window
262K tokens
Declared output cap
250K 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
Outputs
Text
Tokenizer
Other
Cached input tokens
$0.0030 / 1M
First seen on OpenRouter
6 Aug 2026

Capabilities

  • Supported: Reasoning
  • Supported: Tool use
  • Supported: Structured output
  • 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
mit
Pinned revision
9a98e35fe1c9
Parameters
7.9B
Architecture
BailingMoeV3ForCausalLM
Model type
bailing_hybrid
Hugging Face downloads (30d)
15K
Downloads (all time)
58K
Likes
570
Repository updated
24 Sept 2026, 08:58 UTC

Alternatives to Ling 3.0 Tiny, by the numbers

Ranked neighbours

Models within 3 places of #139 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 InclusionAI models

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

Ling 3.0 Tiny: common questions

How does Ling 3.0 Tiny rank among AI models?

Ling 3.0 Tiny holds ModelCap Index position #139 of 352 ranked language models as of 6 November 2026, with an Index score of 48.1 (interval 27.2–69.0); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Ling 3.0 Tiny cost per 1M tokens?

Ling 3.0 Tiny is listed at $0.015 per 1M input tokens and $0.045 per 1M output tokens as of 6 November 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 $0.052 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 Ling 3.0 Tiny?

Ling 3.0 Tiny has a published context window of 262K tokens (262,144). The snapshot lists a 250,000 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 Ling 3.0 Tiny open-weight?

Open weights (mit). 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 inclusionAI/Ling-3.0-tiny on Hugging Face.

Which API providers serve Ling 3.0 Tiny?

One provider serves Ling 3.0 Tiny through OpenRouter as of 6 November 2026: Ant Digital. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Ling 3.0 Tiny been evaluated on?

Ling 3.0 Tiny'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 6 November 2026. The evidence panel names each source and its publication date.

When was Ling 3.0 Tiny released?

Ling 3.0 Tiny first appeared in the catalogue on 6 Aug 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.