How much does Ling 3.1 Flash cost?

Ling 3.1 Flash costs $0.045/M input tokens and $0.135/M output tokens, with separate rates for Cache Read at $0.009/M tokens.

What is the context length of Ling 3.1 Flash?

Ling 3.1 Flash has a 1,000,000 token context window. It supports up to 262,144 completion tokens.

Does Ling 3.1 Flash support tool calling and structured outputs?

Yes. Ling 3.1 Flash accepts tools and tool_choice for function calling. It does not support response_format, so JSON output is not enforced.

Which providers serve Ling 3.1 Flash?

Ling 3.1 Flash is served by 1 provider on OpenRouter: NovitaAI.

What other text models does inclusionAI have?

Ling 3.2 Flash (free), Ling 3.0 Flash VL, Ling 3.0 Tiny and 1 more are other text models from inclusionAI.

When was Ling 3.1 Flash released?

Ling 3.1 Flash was released on October 2, 2026.

inclusionAI: Ling 3.1 Flash

inclusionai/ling-3.1-flash

Model weights

Compare

Ling 3.1 Flash is a hybrid reasoning mixture-of-experts model from inclusionAI, with 25B active parameters out of 560B total.

Modalities

In / out price

85% off

$0.045 / $0.135per 1M

Context

1.0M

Released

Oct 2, 2026

Providers

This model is hosted by one provider. OpenRouter forwards every request without routing.

Latency / throughput


85% off	$0.30$0.045	$0.90$0.135	$0.06$0.009	1.40s	70 tok/s	100.00%

Input $0.045Output $0.13585% off
70 tok/s100.00% uptime

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Weighted average

Input

$0.02196/M tokens

Output

$0.1428/M tokens

Ling 3.1 Flash — Price history

Price history

No effective price data for this selection

		$0.0220	$0.143	87.4%	100.0%

Effective in $0.0220Effective out $0.143
Token share
100.0%

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Uptime (3d)

100.00%

Availability (3d)

99.51%

Availability over the last 3 days

Last 72 hours

Availability 99.51%

3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability

99.29%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for inclusionAI: Ling 3.1 Flash (Artificial Analysis)
Source	Benchmark	Score
Artificial Analysis	Ling 3.1 Flash 1019 Intelligence Index	29.6
Artificial Analysis	Ling 3.1 Flash 1019 HLE	43.5%
Artificial Analysis	Ling 3.1 Flash 1019 AA-LCR	82.3%
Artificial Analysis	Ling 3.1 Flash 1019 GDPval-AA	57.7%
Artificial Analysis	Ling 3.1 Flash 1019 CritPt	26.0%
Artificial Analysis	Ling 3.1 Flash 1019 SciCode	56.8%
Artificial Analysis	Ling 3.1 Flash 1019 Terminal-Bench 4.0	32.8%
Artificial Analysis	Ling 3.1 Flash 1019 AA-Omniscience Accuracy	26.5%
Artificial Analysis	Ling 3.1 Flash 1019 AA-Omniscience Non-Hallucination Rate	63.7%

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Claude Code

Claude Code is Anthropic's agentic coding tool that reads your entire codebase, plans and executes changes across files, runs tests, and iterates on failures, all from natural language prompts.

49.8Btokens

There are many coding agents, but this one is yours.

22.9Btokens

Portkey AI

Control panel for AI apps

19.9Btokens

Hermes Agent

Hermes Agent is an open-source, self-improving AI agent by Nous Research that runs persistently with memory across sessions, and builds reusable skills from experience. It comes with 40+ built-in tools, including web search, browser automation, and vision, plus scheduled automations and subagents.

15.3Btokens

omp

new

5.85Btokens

Rest of OpenRouter

4.73B

Top 5 Apps

1.28B

Not currently available through EU in-region routing. In-region routing docs

More models from inclusionai

Ling 3.2 Flash (free)

Ling 3.2 Flash is a hybrid reasoning model from inclusionAI that accepts text and image input, with a 1M-token context window and up to 200 images per request. Thinking can be switched off per request.

Text1.0M contextFree

Ling 3.1 Flash

Ling 3.1 Flash is a hybrid reasoning mixture-of-experts model from inclusionAI, with 25B active parameters out of 560B total.

Text1M context$0.045 / $0.135

Ming Image 0.1 Design Layer

Ming Image 0.1 Design Layer is an image-to-image model from inclusionAI that decomposes a flattened design image into separate RGBA layers, such as a background layer and foreground elements, and returns one image per layer. It requires exactly one reference image and a prompt describing the layer plan. Output format can be requested as PNG or WebP. Output dimensions follow the input image, so explicit sizes and aspect ratios are rejected instead of silently reshaped.

Image

Ming Image 0.1 Design

Ming Image 0.1 Design is a text-to-image model from inclusionAI aimed at graphic-design output, with an emphasis on legible text rendering inside the generated image. It generates from a prompt only and does not accept reference images. Output format can be requested as PNG, JPEG, or WebP. Image dimensions are chosen by the model rather than by the request, so explicit sizes and aspect ratios are rejected instead of silently reshaped.

Image

Ling 3.0 Flash VL

Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual agent capabilities. Hybrid instant/reasoning model with tool calling.

Text131K context$0.06 / $0.18

Ling 3.0 Flash Sante

Ling 3.0 Flash Sante is a health and medicine-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 medical knowledge reasoning, clinical safety, evidence-based retrieval, and long-horizon medical tasks, while retaining general capabilities in reasoning, coding, and agentic tasks.

Text262K context

Ling 3.0 Flash Fin

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 workflows that require complex multi-step tasks and long-horizon planning and execution, while retaining general capabilities in reasoning, coding, and mathematics.

Text262K context

Ling 3.0 Tiny

Ling 3.0 Tiny is a mixture-of-experts model from InclusionAI, with 1.3B active parameters out of 7.9B total. It is designed for responsive agents, instruction following, and multi-turn conversations, with switchable thinking and instant modes.

Text262K context$0.015 / $0.045

Ling 3.0 Flash

Ling-3.0-flash is a 124B-parameter Mixture-of-Experts (MoE) model, with approximately 5.1B parameters activated per token.

The model is designed with token efficiency and production-scale agentic inference as key priorities, enabling developers to complete more useful work within constrained token, latency, and serving-cost budgets.

Text262K context$0.021 / $0.063

Ring-2.6-1T

Ring-2.6-1T is a 1T-parameter-scale thinking model with 63B active parameters, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool use, and long-horizon task execution, delivering leading results on benchmarks including PinchBench, ClawEval, TAU2-Bench, and GAIA2-search.

With adaptive reasoning effort across high and xhigh modes, Ring-2.6-1T dynamically allocates reasoning budget based on task complexity. This enables stronger performance with lower token overhead, especially in tool-heavy and multi-turn agent workflows.

Ring-2.6-1T is designed for advanced coding agents, complex reasoning pipelines, and large-scale autonomous systems where execution quality, latency, and cost efficiency all matter.

Text262K context

Ling-2.6-1T

Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company’s trillion-parameter flagship, designed for real-world agents that require fast execution and high efficiency at scale. It uses a “fast thinking” approach to reduce costs to roughly a quarter of comparable models while maintaining top-tier performance.

The model achieves state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified, and is well suited for advanced coding, complex reasoning, and large-scale agent workflows where both capability and efficiency are critical.

Text262K context

Ling-2.6-flash

Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for real-world agents that require fast responses, strong execution, and high token efficiency. It delivers performance comparable to state-of-the-art models at a similar scale while significantly reducing token usage across coding, document processing, and lightweight agent workflows.

Text262K context

inclusionAI: Ling 3.1 Flash

Providers