Ling-3.0-Flash-VL is available on Novita AI as a serverless multimodal model for applications that need text, image, and video input in one API workflow. The hosted listing for inclusionai/ling-3.0-flash-vl shows a 262,144-token context window, 32,768-token maximum output, reasoning, function calling, and $0 per million input and output tokens with a time-limited-free label, checked September 9, 2026. It is the visual-language member of the Ling-3.0-flash family, adding native visual understanding and visual agent capabilities rather than requiring a separate image-to-text pipeline.
Key Takeaways
- Ling-3.0-Flash-VL accepts text, images, and videos. Novita lists those three input modalities and text output for the hosted model.
- The Novita endpoint is a 256K-context, 32K-output deployment. The exact limits are 262,144 context tokens and 32,768 maximum output tokens.
- The model is sparse at large total capacity. The official InclusionAI model card describes 124B total parameters and about 5.5B activated parameters per token.
- Current token pricing is $0. Novita marks both input and output at $0 per million tokens and labels the offer time limited, so recheck the model page before budgeting production traffic.
- The main use-case difference from base Ling-3.0-flash is visual input. Use the Ling-3.0-flash overview for the text-only family entry point; this page focuses on the
-VLvariant.
What Is Ling-3.0-Flash-VL?
Ling-3.0-Flash-VL is a native multimodal model from InclusionAI and the visual-language extension of Ling-3.0-flash. The upstream model card describes a system that brings visual information into understanding, reasoning, planning, acting, and verification. That is a broader role than attaching an image captioner to a text-only language model: the image or video can remain part of the model’s reasoning context while it works through a task.
The model card reports 124B total parameters and approximately 5.5B activated parameters per token. This is a sparse Mixture-of-Experts design, so total parameter count and per-token active parameters describe different properties. The former indicates the overall model capacity; the latter describes the approximate amount of routed computation for each token. Neither number alone guarantees a particular latency or output quality on your workload.
The visual extension is the important distinction for this launch. Base Ling-3.0-flash is a text model on Novita’s current listing, while Ling-3.0-Flash-VL accepts image and video inputs as well as text. That makes the -VL page relevant to interface understanding, document and chart analysis, visual inspection, video question answering, and agents that need to interpret what is on screen.
How to Access It on Novita AI
Novita hosts the model as a serverless endpoint with the exact model ID inclusionai/ling-3.0-flash-vl. The model page lists chat/completions and Anthropic-compatible endpoint families, plus reasoning and function-calling features. For an existing OpenAI-compatible client, use Novita’s API base URL and select the exact model ID in your request configuration.
The model page is the source of truth for the hosted configuration. In particular, do not copy the upstream model card’s larger context claim into a Novita integration without checking the deployment limits: the model card describes up to 1M tokens at the model level, while Novita currently exposes 262,144 context tokens and 32,768 maximum output tokens for this listing.
The current catalog also shows a 30 requests-per-minute limit for the default tier, with higher tier values listed separately. Treat that as a catalog quota, not as a universal application throughput guarantee. Account tier, request size, concurrency, retries, and tool execution can all affect end-to-end throughput.
Specs and Pricing Summary
| Field | Details | Source / Date checked |
|---|---|---|
| Display name | Ling 3.0 Flash VL | Novita model page, September 9, 2026 |
| Model ID | inclusionai/ling-3.0-flash-vl | Novita model page, September 9, 2026 |
| Architecture | 124B total parameters; approximately 5.5B active per token; sparse MoE | Official InclusionAI model card, September 9, 2026 |
| Input modalities | Text, image, and video | Novita model page, September 9, 2026 |
| Output modality | Text | Novita model page, September 9, 2026 |
| Context window | 262,144 tokens on the Novita deployment | Novita model page, September 9, 2026 |
| Maximum output | 32,768 tokens | Novita model page, September 9, 2026 |
| Hosted features | Serverless, function calling, reasoning | Novita model page, September 9, 2026 |
| Endpoint families | chat/completions, Anthropic-compatible | Novita model page, September 9, 2026 |
| Catalog RPM | 30 RPM on the default listing tier | Novita model page, September 9, 2026 |
| Input price | $0 per million tokens, currently labeled time limited free | Novita model page, September 9, 2026 |
| Output price | $0 per million tokens, currently labeled time limited free | Novita model page, September 9, 2026 |
The $0 price is useful for evaluation, but it should not become an untested production assumption. Before launch, record the current price, add a budget guard, and decide what model or queue should receive traffic if the promotional window ends.
What Can Developers Build With It?
Understand documents, charts, and interfaces
Visual input can reduce the amount of preprocessing needed for documents, dashboards, screenshots, and software interfaces. A developer can pass a screenshot together with a task instruction and ask the model to identify fields, summarize visible state, or locate an interaction target. For documents and charts, test whether the model preserves units, legends, table structure, and spatial relationships rather than judging it from a short captioning example.
Reason over images and video
The upstream architecture uses a visual encoder and temporal-position handling for video. This is relevant when the answer depends on change over time, such as identifying when an event occurs, comparing two moments in a clip, or extracting a sequence of actions. Long videos still need careful sampling and prompt design: a large context limit does not automatically make every frame equally useful or affordable after pricing changes.
Support visual agents
The model’s visual-agent positioning fits workflows where perception is only one stage of a larger loop. An agent might inspect a browser screenshot, reason about the next action, call a tool, and verify the resulting screen. Function calling can provide the action boundary, while the visual context provides evidence for deciding what to do next. Keep permissions narrow and validate every tool argument; multimodal understanding does not remove the need for application-side safety checks.
Route mixed text and visual workloads
Teams that already route text tasks to Ling-3.0-flash can evaluate the -VL variant for requests that contain visual evidence. A practical router can send text-only traffic to the base model and reserve the visual variant for messages containing images, videos, or screenshot-derived state. Measure the full route, including image preparation, upload time, model latency, retries, and downstream tool calls.
Performance Signals and Evaluation
The official InclusionAI model card reports a score of 42 on the Artificial Analysis Intelligence Index v4.1.1 and describes multimodal capability dimensions including understanding, reasoning, and acting. That is a useful signal about the upstream release, not a guarantee for the Novita-hosted deployment or for your prompt distribution. The page also notes a 256K-context Terminal-Bench evaluation configuration, which is closer to the hosted context limit than the model card’s general “up to 1M” statement.
For a meaningful evaluation, use a small task set that reflects your product:
- Visual extraction: measure field accuracy on screenshots, forms, tables, and charts.
- Temporal reasoning: test whether answers identify the right event and time range in short videos.
- Agent execution: score both the chosen tool and the final state after the tool runs.
- Grounding: include images with ambiguous or irrelevant details and check unsupported assertions.
- Operations: record latency, token usage, failure rate, rate-limit behavior, and the effect of retries.
Do not use an upstream benchmark score as a substitute for these application checks. A model can perform well on a public benchmark while still needing prompt, preprocessing, or routing changes in a production interface.
When to Use Ling-3.0-Flash-VL
Choose Ling-3.0-Flash-VL when your request contains visual evidence and the answer requires more than a one-shot caption. It is a good candidate to evaluate for:
- screenshot and browser-interface understanding
- chart, table, and document question answering
- short-video event localization and summarization
- visual tool-using agents
- mixed text-and-image support workflows
The current free listing also makes it practical to build a representative test set before committing to a paid routing plan. Capture the date and pricing state when you run those tests so cost comparisons remain reproducible.
When to Choose Another Model
Choose the base Ling-3.0-flash model page for text-only workloads if you do not need visual inputs. A text-only route may simplify payload handling and reduce unnecessary multimodal processing.
Choose another model if you need a permanently fixed price, a different context or output ceiling, a tested latency service level, audio input, or a capability that is not listed for this deployment. Also keep a fallback if your application depends on the current time-limited-free offer. The safest production design treats the promotional price and catalog quota as changeable configuration, not as hard-coded product guarantees.
How It Fits an Existing API Workflow
At a high level, an existing OpenAI-compatible application needs a Novita API key, the Novita OpenAI-compatible base URL, and inclusionai/ling-3.0-flash-vl as the model ID. The request should use the multimodal message structure supported by the selected endpoint, with image or video content alongside the user’s text instruction.
Keep visual assets accessible to the endpoint and validate their format before sending them. For an agent workflow, define tools separately from visual content, restrict what each tool can do, and log the model’s selected action and the resulting application state. A dedicated quick-start article can cover SDK-specific request construction; this launch page is intentionally focused on availability, positioning, limits, and pricing.
Final Recommendation
Ling-3.0-Flash-VL is the Ling-3.0-flash family member to test when visual evidence must participate in reasoning or agent execution. Novita’s current serverless listing combines image and video input with a 262K context, 32K maximum output, reasoning, and function calling, while the $0 input and output price lowers the barrier to evaluation for the current time-limited window.
Start with a workload-specific test set, compare it with your existing text-only route, and record operational metrics as well as answer quality. If it meets your visual accuracy and tool-safety requirements, keep the Novita model ID and current pricing in configuration so a future catalog change does not require a code rewrite.
Try Ling-3.0-Flash-VL on Novita AI
FAQ
What is the Ling-3.0-Flash-VL model ID on Novita AI?
The exact model ID is inclusionai/ling-3.0-flash-vl.
Does Ling-3.0-Flash-VL support image and video input?
Yes. Novita’s current listing supports text, image, and video input, with text output.
What context window and output limit does Novita provide?
The hosted listing shows a 262,144-token context window and a 32,768-token maximum output. These are the Novita deployment values and should be checked again before production use.
Is Ling-3.0-Flash-VL free on Novita AI?
Novita currently lists $0 per million input tokens and $0 per million output tokens and labels the model time limited free. The price can change, so confirm the live model page before relying on it.
Does it support function calling and reasoning?
Yes. Novita lists both function calling and reasoning among the hosted features.
How is it different from Ling-3.0-flash?
Ling-3.0-Flash-VL adds native image and video input plus visual-agent capabilities. The current base Ling-3.0-flash page is a text-only family entry point, so use the -VL variant when visual evidence is part of the request.
Does the upstream model support a 1M-token context?
The official InclusionAI model card describes up to 1M tokens at the model level. Novita’s current hosted listing exposes 262,144 tokens, so use the hosted value when designing requests through Novita.
