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Open weights model

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Released October 2026

Ling 3.1 Flash 1019 Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out API Provider Benchmarks

Performance

N/A

Time per Intelligence Index task (minutes)

Cost

In $0.30Out $0.90Cache discount 80%

$3.19

Cost per Intelligence Index task

Verbosity

Input tokens 20.7BOutput/input 1.157%

240M

Output tokens from Intelligence Index

Ling 3.1 Flash 1019 is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. The model supports text input, outputs text, and has a 1M tokens context window.

Ling 3.1 Flash 1019 scores 30 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 23). When evaluating the Intelligence Index, it generated 240M tokens, which is very verbose in comparison to the median of 99.3M.

Pricing for Ling 3.1 Flash 1019 is $0.30 per 1M input tokens (moderately priced, median: $0.43) and $0.90 per 1M output tokens (moderately priced, median: $1.84). On average, it costs $3.19 per task to evaluate Ling 3.1 Flash 1019 on the Intelligence Index.

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
Input modality	Supports: text
Output modality	Supports: text
Context window	1M ~1573 A4 pages of size 12 Arial font
Total parameters	560B
Active parameters	25B Number of parameters active per token during inference
License	MIT
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

No publicly available API

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

No publicly available API

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

No publicly available API

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Artificial Analysis Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

No publicly available API

Benchmarks

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

No publicly available API

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode-AA 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

No publicly available API

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience v1.1

AA-Omniscience Net Score

AA-Omniscience v1.1 Net Score (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

No publicly available API

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Token Use

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

No publicly available API

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

No publicly available API

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about Ling 3.1 Flash 1019

Ling 3.1 Flash 1019 was released on October 22, 2026.

Ling 3.1 Flash 1019 was created by InclusionAI.

Ling 3.1 Flash 1019 scores 30 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 23).

Ling 3.1 Flash 1019 costs $0.30 per 1M input tokens (better than average, median: $0.43) and $0.90 per 1M output tokens (better than average, median: $1.84), based on InclusionAI's API.

Ling 3.1 Flash 1019 costs $0.30 per 1M input tokens and $0.90 per 1M output tokens (based on InclusionAI's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.19 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Ling 3.1 Flash 1019 generated 240M output tokens, which is at the higher end compared to other open weight models of similar size (median: 99.3M).

Yes, Ling 3.1 Flash 1019 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Ling 3.1 Flash 1019 supports text input.

Ling 3.1 Flash 1019 supports text output.

No, Ling 3.1 Flash 1019 does not support image input. It can only process text.

No, Ling 3.1 Flash 1019 is not multimodal. It only supports text input.

Ling 3.1 Flash 1019 has a context window of 1M tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Ling 3.1 Flash 1019 is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Ling 3.1 Flash 1019 has 560B parameters (25B active).

Ling 3.1 Flash 1019 is a Mixture of Experts (MoE) model with 560B total parameters, but only 25B active parameters are used during inference.

Ling 3.1 Flash 1019 is released under the MIT license. This license allows commercial use. View license

Ling 3.1 Flash 1019 achieves a score of 30 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.

Yes, Ling 3.1 Flash 1019 is available via API through 1 provider. Compare API providers

Ling 3.1 Flash 1019 is available through 1 API provider. Compare providers

Ling 3.1 Flash 1019 Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Performance

Comparison Summary

Technical specifications

129 models in this class

Time per Task

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience v1.1

AA-Omniscience Net Score

AA-Omniscience Net Score

Openness Index

Artificial Analysis Openness Index: Score

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

When was Ling 3.1 Flash 1019 released?

Who created Ling 3.1 Flash 1019?

How intelligent is Ling 3.1 Flash 1019?

How much does Ling 3.1 Flash 1019 cost?

What is Ling 3.1 Flash 1019 API pricing?

How verbose is Ling 3.1 Flash 1019?

Is Ling 3.1 Flash 1019 a reasoning model?

What input modalities does Ling 3.1 Flash 1019 support?

What output modalities does Ling 3.1 Flash 1019 support?

Can Ling 3.1 Flash 1019 process images?

Is Ling 3.1 Flash 1019 multimodal?

What is the context window of Ling 3.1 Flash 1019?

Is Ling 3.1 Flash 1019 open source?

How many parameters does Ling 3.1 Flash 1019 have?

What are the active parameters of Ling 3.1 Flash 1019?

What is the license for Ling 3.1 Flash 1019?

How does Ling 3.1 Flash 1019 perform on benchmarks?

Is Ling 3.1 Flash 1019 available via API?

Where can I use Ling 3.1 Flash 1019?