---
title: "Ling-2.6-1T Cost Calculator - Hugging Face"
description: "Calculate the cost of using Ling-2.6-1T from Hugging Face. Input costs $0.30 and output $2.50 per 1M tokens."
url: "https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface/model/ling-2.6-1t"
markdown: "https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface/model/ling-2.6-1t.md"
---

# Ling-2.6-1T Cost Calculator - Hugging Face

> Calculate the cost of using Ling-2.6-1T from Hugging Face. Input costs $0.30 and output $2.50 per 1M tokens.

## Important Links

- [View MCP Gateway](https://www.getmaxim.ai/bifrost/resources/mcp-gateway.md)
- [Features](https://www.getmaxim.ai/bifrost/#features)
- [Enterprise](https://www.getmaxim.ai/bifrost/enterprise)
- [Pricing](https://www.getmaxim.ai/bifrost/pricing.md)
- [Docs](https://docs.getbifrost.ai)
- [GitHub](https://github.com/maximhq/bifrost)
- [Book a Demo](https://www.getmaxim.ai/bifrost/book-a-demo)

## About Ling-2.6-1T

Ling-2.6-1T is a chat model from Hugging Face, one of 203 chat models they offer. It is priced at $0.30 per 1M input tokens and $2.50 per 1M output tokens, ranking 1149 out of 2912 chat models by cost and cheaper than 61% of models in this category. Ling-2.6-1T supports function calling, structured output. Its 262K-token context window is in the top 34% among chat models.

## Pricing

Published Hugging Face pricing for Ling-2.6-1T.

- **$0.30 / 1M tokens Input.**
- **$2.50 / 1M tokens Output.**

## Technical Specifications

- **Chat Mode.**
- **262,144 Max Input Tokens.**
- **262,144 Max Tokens.**

## Model Capabilities

Ling-2.6-1T supports: Function Calling, Response Schema, Tool Choice.

## How Pricing Compares

At $0.30 per 1M input tokens and $2.50 per 1M output tokens, Ling-2.6-1T ranks 1149 out of 2912 chat models by input cost. It is more affordable compared to the median of $0.55 for chat models, and is cheaper than 61% of models in this category.

Ling-2.6-1T is one of 203 Hugging Face chat models, with support for function calling, structured output. its 262K-token context window places it in the top 34% of chat models.

| Model | Provider | Input $/1M | Output $/1M | vs Ling-2.6-1T |
| --- | --- | --- | --- | --- |
| amazon.nova-2-lite-v1:0 | AWS Bedrock | $0.30 | $2.50 | 0% |
| apac.amazon.nova-2-lite-v1:0 | AWS Bedrock | $0.33 | $2.75 | +10% |
| eu.amazon.nova-2-lite-v1:0 | AWS Bedrock | $0.33 | $2.75 | +10% |
| us.amazon.nova-2-lite-v1:0 | AWS Bedrock | $0.33 | $2.75 | +10% |
| anthropic.claude-3-haiku-20240307-v1:0 | AWS Bedrock | $0.25 | $1.25 | -17% |

## More from Hugging Face

- [Muse-Glimmer-30B ($0.30/1M input)](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface/model/muse-glimmer-30b.md)
- [MiniMax-M3 ($0.30/1M input)](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface/model/minimax-m3.md)

## Related Resources

- [Hugging Face status](https://www.getmaxim.ai/bifrost/provider-status/huggingface.md)
- [All Hugging Face pricing](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface.md)

## FAQ

### Is Ling-2.6-1T cheaper than amazon.nova-2-lite-v1:0?

No. Ling-2.6-1T costs $0.30 per 1M input tokens while amazon.nova-2-lite-v1:0 costs $0.30 per 1M input tokens, making amazon.nova-2-lite-v1:0 0% more affordable for input. However, Ling-2.6-1T may offer different capabilities or performance characteristics that justify the price difference.

### How does Ling-2.6-1T pricing compare to the average chat model?

Ling-2.6-1T input pricing is $0.30 per 1M tokens, which is 45% below the median of $0.55 for chat models. It ranks 1149 out of 2912 chat models by input cost, making it cheaper than 61% of models in this category. For output, it costs $2.50 per 1M tokens compared to the median of $1.50.

### What makes Ling-2.6-1T different from other Hugging Face models?

Among Hugging Face's 203 chat models, Ling-2.6-1T ranks 115 by input cost.

### What are the best alternatives to Ling-2.6-1T?

The most comparable chat models to Ling-2.6-1T are: amazon.nova-2-lite-v1:0 from AWS Bedrock ($0.30/1M input tokens); apac.amazon.nova-2-lite-v1:0 from AWS Bedrock ($0.33/1M input tokens); eu.amazon.nova-2-lite-v1:0 from AWS Bedrock ($0.33/1M input tokens); us.amazon.nova-2-lite-v1:0 from AWS Bedrock ($0.33/1M input tokens). These alternatives were selected based on similar capabilities, pricing, and provider diversity. You can compare any of these models in detail using the Bifrost Model Library.

### How do I check if Hugging Face is down?

You can monitor Hugging Face service health in real time on the Bifrost Hugging Face Status page. It tracks current component status, active incidents, and historical reliability data pulled from the official status page every 60 seconds.

### How do I calculate Ling-2.6-1T costs?

Ling-2.6-1T is priced based on input and output tokens. Use the interactive calculator at the top of this page to estimate costs for your specific workload. Enter your expected input and output tokens volume and the calculator will show the total cost breakdown. For reference, processing 1M input tokens costs $0.30 and generating 1M output tokens costs $2.50.

## Related Resources

- [All Hugging Face pricing](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface.md)
- [LLM cost calculator](https://www.getmaxim.ai/bifrost/llm-cost-calculator.md)
- [Model library](https://www.getmaxim.ai/bifrost/model-library.md)
- [Docs: Bifrost docs](https://docs.getbifrost.ai)
- [GitHub: maximhq/bifrost](https://github.com/maximhq/bifrost)
- [Pricing: Bifrost pricing](https://www.getmaxim.ai/bifrost/pricing.md)
- [Enterprise: Bifrost enterprise](https://www.getmaxim.ai/bifrost/enterprise)
- [Book a Demo: Bifrost demo](https://www.getmaxim.ai/bifrost/book-a-demo)
- [Resources: Bifrost resources](https://www.getmaxim.ai/bifrost/resources.md)

---

*This is a markdown version of [https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface/model/ling-2.6-1t](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/huggingface/model/ling-2.6-1t) for AI/LLM consumption.*
