Ling-3.0-flash-Fin is available through the Novita AI Serverless API as a finance-enhanced Mixture-of-Experts model based on Ling-3.0-flash. The Novita model page describes a 124B-parameter model with approximately 5.1B activated parameters, a 256K-token context window, a 32K maximum output, text input and output, and reasoning and function-calling support. On August 31, 2026, Novita listed both input and output pricing at $0 per million tokens.
That combination makes the model worth evaluating for developers building research assistants, document-analysis pipelines, and other multi-step investment workflows. It does not make financial decisions on a developer’s behalf, and a finance-oriented model still needs task-specific evaluation, data controls, and human review before it is used in a consequential workflow.
Key Takeaways
- Ling-3.0-flash-Fin is available on Novita AI. The exact model ID is
inclusionai/ling-3.0-flash-fin, served through Novita’s serverless API. - The current listing shows a 256K context window and 32K maximum output. The underlying values are 262,144 context tokens and 32,768 maximum output tokens.
- Novita currently lists $0 per million input tokens and $0 per million output tokens. Pricing can change, so check the live model page before planning production spend.
- The model is text-only at the hosted endpoint. Novita lists reasoning and function calling among its hosted features and
chat/completionsas the endpoint family. - Its best fit is structured financial and investment workflow experimentation. Use it for analysis support and repeatable task steps, not as a substitute for professional advice, compliance review, or verified market data.
What Is Ling-3.0-flash-Fin?
Ling-3.0-flash-Fin is the finance-enhanced variant in InclusionAI’s Ling-3.0-flash family. Its sparse MoE design contains 124 billion total parameters, while approximately 5.1 billion parameters are activated for each token according to Novita’s current model description. The distinction is useful when reasoning about the model’s architecture, but it is not a promise about latency, throughput, or answer quality for a particular workload.
The hosted model is designed for text tasks. Novita’s listing identifies investment workflows as the target context and highlights capabilities for complex, multi-step work. In practice, that can include turning a long research packet into a structured brief, extracting fields from filings or reports, comparing stated assumptions, or coordinating a sequence of tool calls. Those are workflow patterns to test, not claims that the model independently verifies facts or provides regulated advice.
No public InclusionAI benchmark or release note for this finance variant was available to substantiate numeric performance claims when this page was prepared. For that reason, the page focuses on the verified Novita configuration and on evaluation criteria developers can apply to their own data.
Ling-3.0-flash-Fin API Access on Novita AI
Novita exposes inclusionai/ling-3.0-flash-fin through its serverless API. The model page shows chat/completions as the endpoint family and provides an OpenAI-compatible access path with this base URL:
https://api.novita.ai/openaiFor an OpenAI SDK client, the base URL is paired with the v1/chat/completions route and the exact model ID above. This article intentionally keeps the API entry point high-level; the Ling 3.0 Tiny quick start covers the separate step-by-step pattern for OpenAI-compatible chat completions.
The model page also shows a 30 requests-per-minute listing limit. Treat that as the current catalog value rather than a guarantee of application throughput. Your account, plan, request shape, and downstream tools may impose additional limits.
Ling-3.0-flash-Fin Specs and Pricing Summary
Values below were checked against the Novita Ling-3.0-flash-Fin model page on August 31, 2026.
| Field | Details |
|---|---|
| Display name | Ling 3.0 Flash Fin |
| Model ID | inclusionai/ling-3.0-flash-fin |
| Architecture | 124B total parameters; approximately 5.1B activated parameters |
| Access | Serverless API |
| Base URL | https://api.novita.ai/openai |
| Endpoint family | chat/completions |
| Context size | 262,144 tokens (displayed as 256K) |
| Maximum output | 32,768 tokens (displayed as 32K) |
| Input capability | Text |
| Output capability | Text |
| Hosted features | Reasoning and function calling |
| Input pricing | $0 per million tokens |
| Output pricing | $0 per million tokens |
| Listed request limit | 30 RPM |
| Best fit | Text-based financial research and multi-step workflow evaluation |
The zero-dollar price is the current displayed rate, not a lifetime price commitment. Before moving from evaluation to production, recheck the page and measure your own token use, latency, error rate, tool-call success, and output-review cost.
What Developers Can Evaluate
The finance-oriented positioning is most useful when it is translated into testable tasks. A representative evaluation set might include:
- Long-document extraction: identify dates, entities, stated assumptions, and numeric fields from a supplied report while preserving the source context.
- Structured research briefs: convert several provided documents into a consistent schema with citations or source spans supplied by the application.
- Multi-step planning: break a user-defined research task into bounded steps and request tool calls for retrieval or calculation rather than inventing missing data.
- Scenario comparison: organize assumptions and outcomes for user-specified scenarios without presenting the result as a forecast or recommendation.
- Output discipline: test JSON or function-call adherence, refusal behavior for missing evidence, and clear separation between supplied facts and model-generated interpretation.
These tests should use representative, permissioned data. A model response is not a source of truth for prices, filings, legal obligations, or suitability determinations; retrieval, calculation, and review belong in the surrounding application.
When To Use Ling-3.0-flash-Fin
Consider this model when you need a hosted text endpoint for:
- long-context analysis of supplied financial documents
- repeatable research and reporting workflows
- function-calling experiments with bounded external tools
- reasoning-oriented task decomposition
- an evaluation period with the current $0 per-million-token listing
The 256K context window can help keep larger task packets in one request, but context capacity is not the same as useful attention. Test how the model handles your document order, repeated facts, tables, and instructions near the beginning and end of a long prompt.
When Not To Use It
Choose another configuration or add application safeguards when you need image or audio input, because the current listing is text-only. Do not route unreviewed output directly into trade execution, customer eligibility decisions, compliance conclusions, or other high-impact actions. The model page does not establish financial accuracy, regulatory approval, or a service-level guarantee.
It may also be a poor fit for short, simple classification if a smaller model meets the quality target at lower latency or operational cost. Compare on your actual workload rather than selecting solely from total parameter count or the current promotional price.
How It Fits an API Workflow
At a high level, an application can send a bounded task and its permitted context to the OpenAI-compatible chat completions endpoint, allow the model to request approved tools when needed, and validate the returned structure before presenting it to a user or downstream system. Keep retrieval, calculations, audit logs, permissions, and human review outside the model boundary.
For a concrete request pattern, start with the Ling 3.0 Tiny quick-start guide, then replace its model identifier with the exact Ling-3.0-flash-Fin ID after checking the live model page and your account limits. The existing Ling-3.0-flash launch page provides family context, while this page is the finance-vertical availability and pricing reference.
Final Recommendation
Ling-3.0-flash-Fin is a sensible candidate for developers evaluating long-context, reasoning-enabled, text-only workflows around supplied financial information. Novita currently lists 256K context, 32K maximum output, function calling, and $0 input and output pricing per million tokens. Start with a small, reviewable test set; record quality and operational metrics; and recheck pricing before making a production commitment.
Explore Ling-3.0-flash-Fin on Novita AI
FAQ
What is the model ID?
The model ID is inclusionai/ling-3.0-flash-fin.
What context size does Novita list?
Novita lists a 262,144-token context size, displayed as 256K.
What is the maximum output?
Novita lists 32,768 maximum output tokens, displayed as 32K.
Is the API free?
As checked on August 31, 2026, Novita lists $0 per million input tokens and $0 per million output tokens. Check the live page for current pricing before production use.
Does it support function calling?
Yes. Novita currently lists function calling and reasoning among the hosted features.
Is Ling-3.0-flash-Fin a financial advisor?
No. It is a language model for developer-defined workflows. Applications should verify source data and keep human, legal, compliance, and investment decisions outside the model.
