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Ling 3.0 Flash Fin

inclusionAI

Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for real-world investment...

Open in Graph
data quality54

Updated 6 h ago · first seen 11 Sept 2026

model_01M294WVPVCC71FCT3VJDRH5BQ

Overview

Identity

Canonical model
Yesidentity confidence: mediumOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
Official checkpoints
None recordedofficial_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
Artifacts
None recordedSeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
Provider deployments
1
API aliases
inclusionai/ling-3.0-flash-finling-3-0-flash-finIdentifiers under which providers and evaluators refer to this model.
Folded evaluation variants
0Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.

Openness

Openness not classified yet — no sourced evidence to place this model in the ontology.

Key facts

Release date

Source:OpenRouter public model & pricing listingT2observed 7 d agomedium

Openrouter id

Source:OpenRouter public model & pricing listingT2observed 7 d agomedium

Capabilities

Modalities

Modalities
text
Input
text
Output
text

Capabilities

  • Tool calling

    Yes

    OpenRouter public model & pricing listing · T2

  • Structured output

    Yes

    OpenRouter public model & pricing listing · T2

  • Reasoning

    Yes

    OpenRouter public model & pricing listing · T2

  • Vision

    Unavailable

  • Audio

    Unavailable

  • Fine-tuning available

    Unavailable

Context window

Source:OpenRouter public model & pricing listingT2observed 7 d agomedium

Max output

Source:OpenRouter public model & pricing listingT2observed 7 d agomedium

Benchmarks3

Compare with another model →

Comparable same task and conditions · Partially comparable same task, conditions differ (effort, temperature, judge) · Not comparable different variant or metric

Benchmark results grouped by comparability group
Benchmark · groupBest scoreTrustConfigurationResultsvs leaderEvaluatedSource
SciCodecoding · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoningonconditions differ across rows → partially comparable1−20.7 ptvs Claude Fable 5.1obs. 18 Sept 2026artificialanalysis.aiT2
Artificial Analysis Intelligence Indexcomposite · indexIndependentreasoningonversion4.3conditions differ across rows → partially comparable1−30.3vs Claude Fable 5.1obs. 18 Sept 2026artificialanalysis.aiT2
Humanity's Last Examknowledge · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoningonconditions differ across rows → partially comparable1−36.5 ptvs Claude Fable 5.1obs. 18 Sept 2026artificialanalysis.aiT2

Current rows only, grouped by benchmark → canonical metric → comparability group (task configuration). Effort variants folded into this model appear as rows of the same group. 3 current rows in total. “vs leader” compares with the current leader of the benchmark's primary group only; other groups are not directly comparable. Comparability rules →

Providers & Pricing2

All offers in the price terminal →
Provider deployments, cheapest output first
ProviderContextInput / 1MCached inOutput / 1MStatusObservedSource
OpenRoutercheapest outputinclusionai/ling-3.0-flash-fin:free262.1Kout 32.8K—active2 h agosince 11 Sept 2026openrouter.aiT2
OpenRouterinclusionai/ling-3.0-flash-fin262.1Kout 235.9K$0.012active2 h agosince 11 Sept 2026openrouter.aiT2

USD per 1M tokens as published by each provider; native units (per-request fees, flex/priority tiers) are kept verbatim. Rows are append-only — every price change is kept in the history below. Cost of a workload →

Price history

Step lines per provider; amber markers are recorded changes. Click a marker or a row for the evidence behind that price.

Output price · USD / 1M tokens 1 provider

Output price history of Ling 3.0 Flash Fin$0$0.05$0.10$0.15$0.20$0.25Sept 26Sept 26Sept 26Sept 26Sept 26Sept 26Sept 26OpenRouter: first observed → $0.18 · 11 Sept 2026OpenRouter: $0.18 → $0 · 11 Sept 2026
  • OpenRouter
  • OpenRouter$0.18 → $011 Sept 2026
  • OpenRouterfirst observed $0.1811 Sept 2026

Input price · USD / 1M tokens 1 provider

Input price history of Ling 3.0 Flash Fin$0$0.02$0.04$0.06$0.08Sept 26Sept 26Sept 26Sept 26Sept 26Sept 26Sept 26OpenRouter: first observed → $0.06 · 11 Sept 2026OpenRouter: $0.06 → $0 · 11 Sept 2026
  • OpenRouter
  • OpenRouter$0.06 → $011 Sept 2026
  • OpenRouterfirst observed $0.0611 Sept 2026

Versions & Artifacts0

Version history

Context windowfirst observation only

11 Sept 2026current

Max outputfirst observation only

11 Sept 2026current

Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.

Artifacts 0

No artifact (checkpoint, quantisation, conversion or packaging) points to this model yet.

Timeline4

Full timeline →
  • Benchmark resultModelLing 3.0 Flash FininclusionAI

    Ling 3.0 Flash Fin scores 42.36% on SciCode

    artificial_analysis
    artificialanalysis.ai
  • Benchmark resultModelLing 3.0 Flash FininclusionAI

    Ling 3.0 Flash Fin scores 22.61% on Humanity's Last Exam

    artificial_analysis
    artificialanalysis.ai
  • Benchmark resultModelLing 3.0 Flash FininclusionAI

    Ling 3.0 Flash Fin scores 23.02 on Artificial Analysis Intelligence Index

    artificial_analysis
    artificialanalysis.ai
  • Property changedModelLing 3.0 Flash FininclusionAI

    Ling 3.0 Flash Fin: supported parameters changed from ["frequency_penalty", "include_reasoning", "logit_bias", … to ["frequency_penalty", "include_reasoning", "logit_bias", …

    Supported parametersfrequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p→frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_popenrouter
    openrouter.ai

Change history

Temporal, append-only claims: a new observation closes the previous claim instead of overwriting it. Rewind the record with the as-of picker.
1 claims · 1 propertiesShow all properties

Aa opennessaa_openness1

Claim history for Aa openness
ValueValid from → toStatusSourceConfidenceExtractor
open-weights → currentcurrentArtificial AnalysisT2mediumdeterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Provenance

Attributed facts

16

Source tiers

T216

Freshest observation

6 h ago

Conflicts

None

Source documents 2

Source documents
SourceDocumentTypeTierLast observedSnapshots
OpenRouter public model & pricing listingopenrouter.ai/api/v1/models catalogueT2· Quality secondary2 h ago100
Artificial Analysisartificialanalysis.ai/leaderboards/models leaderboardT2· Quality secondary6 h ago23

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.

Data quality (54/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →