API cost decision in 10 seconds

Extract vs Ling 3.0 Tiny

Pick Ling 3.0 Tiny for lower cost; pick Extract only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Ling 3.0 Tiny for lower cost; pick Extract only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Ling 3.0 Tiny is estimated at $0.04 vs $0.04 for Extract, saving $0.0025 (6.3% lower).

Cost-first pickLing 3.0 Tiny
Context-first pickExtract
Sample savings$0.00256.3%
10x traffic gap$0.03

Extract has more context, but Ling 3.0 Tiny saves $0.0025 on the standard workload. At 10x that traffic, the same price gap is about $0.03. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors Ling 3.0 Tiny, balanced workload favors Extract, and output-heavy chatbot favors Extract.

Workload shapeToken mixBetter pickExtractLing 3.0 Tiny
Input-heavy / RAG5M input + 500K outputLing 3.0 Tiny$0.2$0.1
Balanced workload1M input + 1M outputExtract$0.04$0.06
Output-heavy chatbot1M input + 5M outputExtract$0.04$0.24
Cheaper input Ling 3.0 Tiny $0.04 vs $0.015 / 1M

Ling 3.0 Tiny is $0.03 cheaper per 1M input tokens (62.5% lower; 2.67x difference).

Cheaper output Extract $0 vs $0.045 / 1M

Extract is free for output tokens while Ling 3.0 Tiny costs $0.04 per 1M tokens.

Larger context Extract 1M vs 262.14K

Extract has 737.86K more context (3.81x larger).

Sample workload Ling 3.0 Tiny $0.04 vs $0.04

Ling 3.0 Tiny is $0.0025 cheaper on the standard workload (6.3% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Extract Calculating… Estimated API cost
Ling 3.0 Tiny Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Ling 3.0 Tiny has the lower input price; Extract has the lower output price; Extract offers the larger context window. For the 1M input plus 500K output sample, Ling 3.0 Tiny is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.04 for Extract and $0.04 for Ling 3.0 Tiny.

Best Fit

Choose Extract when you care most about lower output-token price, and larger context window.

Choose Ling 3.0 Tiny when you care most about lower input-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Ling 3.0 Tiny is estimated at $0.04 vs $0.04 for Extract, saving $0.0025 (6.3% lower).
  • Ling 3.0 Tiny is $0.0025 cheaper on the standard workload (6.3% lower).
  • Ling 3.0 Tiny is $0.03 cheaper per 1M input tokens (62.5% lower; 2.67x difference).
  • Extract is free for output tokens while Ling 3.0 Tiny costs $0.04 per 1M tokens.
  • Extract has 737.86K more context (3.81x larger).
Head-to-Head Specs
FeatureExtract
(ScaleDown)
Ling 3.0 Tiny
(inclusionAI)
Input Price
prompt tokens per 1M
$0.04$0.015
Completion Price
per 1M tokens
$0$0.045
Sample Workload Cost
1M input + 500K output
$0.04$0.04
Context Window1M262.14K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLing 3.0 TinyOn the standard 1M input plus 500K output workload, Ling 3.0 Tiny is estimated at $0.04 vs $0.04 for Extract, saving $0.0025 (6.3% lower).
High-volume input processingLing 3.0 TinyLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsExtractLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workExtractA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Cheaper alternatives

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Larger context alternatives

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Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

ScaleDown catalog

Review all tracked ScaleDown models before deciding whether this matchup is the right shortlist.

Open ScaleDown models

inclusionAI catalog

Check other inclusionAI models with comparable pricing, context, or release timing.

Open inclusionAI models
Extract

Extract is a description-driven entity extraction model from ScaleDown. Instead of a fixed set of entity types, callers describe the fields they want in plain English through `provider.options.scaledown.entities`, and the...

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...