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ModelCap

Ling 3.0 Flash

Current board #76Official publisher releaseOpen weights7d #70 → #76

InclusionAI·inclusionai/ling-3.0-flash

Ling 3.0 Flash is ModelCap current-board rank #76 with Index 57.3 (Modeled · prior), from the ModelCap Index as of 22 Sept 2026, 02:49 UTC.

Ling 3.0 Flash listed OpenRouter input price is $0.021 per 1M tokens and listed output price is $0.063 per 1M tokens, from OpenRouter listed prices as of 22 Sept 2026, 02:49 UTC.

Ling 3.0 Flash has a published context limit of 262K tokens in the ModelCap catalogue as of 22 Sept 2026, 02:49 UTC.

Ling 3.0 Flash has 2 published OpenRouter serving endpoints, from OpenRouter as of 22 Sept 2026, 02:49 UTC.

Ling 3.0 Flash weight access is open weights, from ModelCap classification of published repository metadata as of 22 Sept 2026, 02:49 UTC.

Snapshot facts · download the public dataset · methodology

ModelCap Index · Current board
57.3#76
Estimated · 17% support · score interval 36.1–78.5
No admitted evaluation publication timestamp yet
Market Gravity
33.5Increased by 0.4
Secondary market signal
Input
$0.021
per 1M tokens
Output
$0.063
per 1M tokens
Context
262K
tokens
Providers
2
endpoints
Gateway spend
latest · Vercel share

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
NovitaCheapest$0.021$0.063262K100.0%
DeepInfrabf16$0.06$0.18131Kbf16100.0%

Hugging Face Inference Providers

Exact repository identity

Provider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.

ProviderInputOutputContextTTFTThroughputFeatures
deepinfra
live
$0.06$0.18131K2273 ms44.1 tok/stools · structured
novita
live
$0.06$0.18262K734 ms263.9 tok/stools

Reported evaluation leads (5)

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
cais/hle
hle
22.7
Unpinned
not published
Reported
Not used in launch estimate
MathArena/aime_2026
MathArena/aime_2026
93.2
Unpinned
not published
Reported
Not used in launch estimate
MathArena/hmmt_feb_2026
MathArena/hmmt_feb_2026
87
Unpinned
not published
Reported
Not used in launch estimate
ScaleAI/SWE-bench_Pro
SWE_Bench_Pro
56.6
Unpinned
not published
Reported
Not used in launch estimate
SWE-bench/SWE-bench_Multilingual
swe_bench_multilingual_%_resolved
72.4
Unpinned
not published
Reported
Not used in launch estimate

Other versions (2)

ModelCap Index · Current board

Methodology
57.3
Modeled · prior · calibrated architecture-and-release-era fallback from static public metadata
Current board
#76
Public rank basis
Modeled · prior (architecture)
Index support
17.2% · limited
Index interval
36.1–78.5
Rank posterior
Not available
Top 5 / Top 10 probability
Not available / Not available
Posterior as of
Snapshot timestamp unavailable
Evidence as of
No admitted evaluation publication timestamp
Identity binding
Exact catalogue product · inclusionai/inclusionai/ling-3.0-flash · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata inclusionAI/Ling-3.0-flash@e0dfe7cd0f6e3b572bbbc0a8a84947469e428cc3 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
57.3
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
57.3 ± 16.6
Independent share
0%
Shrink toward corpus
+4.9
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Corpus ladder · not independent
3 reported rows against measured corpus ladders
52.4 ± 24.347%
Publisher corpus prior · not independent
publisher-corpus-prior over 203 held-out anchors
61.6 ± 22.753%
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)
27.2
Liquidity (providers × uptime)
46.6
Open reach (HF downloads)
19.6
Surface (context / tools / modalities)
75.3
Freshness
79.4
  • · OpenRouter popularity #67
  • · 2 live providers
  • · HF 30d downloads 15946
  • · Catalogue: context 262144, reasoning, tools
  • · First seen 2026-07-23

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
50.2
34.3–66.1 uncertainty interval
Excluded from rank
Confidence
76%
Training support
209 models · 120 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
10.0 MAE

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

Deployment readiness

Metadata index
72.5
partial · 76% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
90.0
Evaluation provenance
0.0

Missing: artifactReproducibility.base-lineage, evaluationProvenance.admitted-evaluation-observations, evaluationProvenance.capability-family-breadth, evaluationProvenance.evaluation-source-diversity, evaluationProvenance.identity-and-source-confidence, evaluationProvenance.evaluation-dataset-revisions.

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

Market activity

Venue coverage
Market Gravity
33.5
OpenRouter weekly popularity
#67 of 322

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%
27.2

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
46.6

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

Open reach15%
19.6

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

Freshness5%
79.4

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

Specification

Context window
262K tokens
Max output
33K tokens
Inputs
Text
Outputs
Text
Tokenizer
Other
Cached input tokens
$0.0042 / 1M
First seen on OpenRouter
23 Jul 2026

Capabilities

  • Supported: Reasoning
  • Supported: Tool use
  • Not 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
e0dfe7cd0f6e
Parameters
127.5B
Architecture
BailingMoeV3ForCausalLM
Model type
bailing_hybrid
Hugging Face downloads (30d)
16K
Downloads (all time)
33K
Likes
417
Repository updated
4 Sept 2026, 05:41 UTC

Compare Ling 3.0 Flash with

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Benchmark leaderboards featuring Ling 3.0 Flash

All leaderboards
Public benchmark boards on which Ling 3.0 Flash has a published result
LeaderboardScoreSource rankConfigurationPublished
Humanity's Last Exam (Artificial Analysis run)Artificial Analysis23.7%#154 of 615hle:may-2025:text-only-2158:no-tools:pass-at-1:source-model="Ling 3.0 Flash":reasoning=true5 Sept 2026

Alternatives to Ling 3.0 Flash, by the numbers

Ranked neighbours

Models within 3 places of #76 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 Flash: common questions

How does Ling 3.0 Flash rank among AI models?

Ling 3.0 Flash holds ModelCap Index position #76 of 248 ranked language models as of 22 September 2026, with an Index score of 57.3 (interval 36.1–78.5); evidence: modeled · prior. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

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

Ling 3.0 Flash is listed at $0.021 per 1M input tokens and $0.063 per 1M output tokens as of 22 September 2026 across 2 providers. 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 $0.073 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 Flash?

Ling 3.0 Flash has a published context window of 262K tokens (262,144) and a maximum output of 33K tokens. Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.

Is Ling 3.0 Flash 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-flash on Hugging Face.

Which API providers serve Ling 3.0 Flash?

2 providers serve Ling 3.0 Flash through OpenRouter as of 22 September 2026: Novita and DeepInfra. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Ling 3.0 Flash been evaluated on?

Ling 3.0 Flash has published results on 1 public board tracked by ModelCap as of 22 September 2026: AA Humanity's Last Exam 23.7% (#154 of 615). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.

How does Ling 3.0 Flash compare with Intern S2 Preview?

Ling 3.0 Flash ranks #76 (Index 57.3) and Intern S2 Preview ranks #75 (Index 58.0) on the ModelCap Index as of 22 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.

When was Ling 3.0 Flash released?

Ling 3.0 Flash first appeared in the catalogue on 23 Jul 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.