Pricing for Ling 3.0 Flash is $0.075 per 1M input tokens (competitively priced, median: $0.30) and $0.22 per 1M output tokens (competitively priced, median: $0.80).

Reasoning	Yes
Input modality	Supports: text
Output modality	Supports: text
Context window	262k ~393 A4 pages of size 12 Arial font
Total parameters	124B
Active parameters	5.1B 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

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

Intelligence

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

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

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.

Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase v1.1

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

HLE-Diamond

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

GDPval-AA v2.1

Agentic real-world work tasks, Elo

MLCR-AA

Medical long context reasoning

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

Openness Index

Artificial Analysis Openness Index: Score

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

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.

Performance

Measured by time per task (minutes), output speed (tokens per second), time to first answer token and time to first token (seconds)

Time per Intelligence Index Task

Output tokens divided by end-to-end output speed (minutes per Intelligence Index task) · Speed at the model's own endpoint or the median across providers · Lower is better

The weighted average time (minutes) per Artificial Analysis Intelligence Index task: output tokens per task divided by output speed, weighted by each benchmark's weight in the Intelligence Index.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Output Speed

Output tokens divided by total request time, including time to first token · Higher is better

Output tokens, including reasoning tokens, received per second over a request's full duration, from sending the request to receiving the final token. The headline value averages each Performance v3 workload over the past 5 days, then averages the workloads.

Time to First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Context Window

Context window: tokens limit · Higher is better

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.

Further details

								Weights
Ling 3.0 Flash	InclusionAI	-	124B 5.1B active at inference time	262k	$0.0475	-	116	🤗

Frequently Asked Questions

Common questions about Ling 3.0 Flash

Ling 3.0 Flash was released on August 4, 2026.

Ling 3.0 Flash was created by InclusionAI.

Ling 3.0 Flash has a time to first answer token of 12.96s (based on InclusionAI's API), which is somewhat higher than average compared to other open weight models of similar size (median: 8.59s).

Ling 3.0 Flash costs $0.075 per 1M input tokens (very competitive, median: $0.30) and $0.22 per 1M output tokens (very competitive, median: $0.80), based on InclusionAI's API.

Ling 3.0 Flash costs $0.075 per 1M input tokens and $0.22 per 1M output tokens (based on InclusionAI's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.0475 per 1M tokens. Pricing may vary by provider. Compare provider pricing

Yes, Ling 3.0 Flash is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Ling 3.0 Flash supports text input.

Ling 3.0 Flash supports text output.

No, Ling 3.0 Flash does not support image input. It can only process text.

No, Ling 3.0 Flash is not multimodal. It only supports text input.

Ling 3.0 Flash has a context window of 262k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Ling 3.0 Flash is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Ling 3.0 Flash has 124B parameters (5.1B active).

Ling 3.0 Flash is a Mixture of Experts (MoE) model with 124B total parameters, but only 5.1B active parameters are used during inference.

Ling 3.0 Flash is released under the MIT license. This license allows commercial use. View license

Yes, Ling 3.0 Flash is available via API through 2 providers. Compare API providers

Ling 3.0 Flash is available through 2 API providers. Compare providers

Ling 3.0 Flash: Intelligence, Performance & Price Analysis

Model summary

Verbosity

Technical specifications

67 models in this class

Intelligence

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

Openness Index

Artificial Analysis Openness Index: Score

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

Performance

Time per Intelligence Index Task

Time per Intelligence Index Task

Time Period

Output Speed

End-to-End Output Speed

Time to First Answer Token

Time to First Answer Token

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

Further details

More from InclusionAI

Frequently Asked Questions

When was Ling 3.0 Flash released?

Who created Ling 3.0 Flash?

What is the latency of Ling 3.0 Flash?

How much does Ling 3.0 Flash cost?

What is Ling 3.0 Flash API pricing?

Is Ling 3.0 Flash a reasoning model?

What input modalities does Ling 3.0 Flash support?

What output modalities does Ling 3.0 Flash support?

Can Ling 3.0 Flash process images?

Is Ling 3.0 Flash multimodal?

What is the context window of Ling 3.0 Flash?

Is Ling 3.0 Flash open source?

How many parameters does Ling 3.0 Flash have?

What are the active parameters of Ling 3.0 Flash?

What is the license for Ling 3.0 Flash?

Is Ling 3.0 Flash available via API?

Where can I use Ling 3.0 Flash?