API cost decision in 10 seconds

Ling-2.6-1T vs Qwen3.5 Plus 2026-02-15

Pick Ling-2.6-1T for lower cost; pick Qwen3.5 Plus 2026-02-15 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-2.6-1T for lower cost; pick Qwen3.5 Plus 2026-02-15 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $1.04 for Qwen3.5 Plus 2026-02-15, saving $0.65 (62.7% lower).

Cost-first pickLing-2.6-1T
Context-first pickQwen3.5 Plus 2026-02-15
Sample savings$0.6562.7%
10x traffic gap$6.53

Qwen3.5 Plus 2026-02-15 has more context, but Ling-2.6-1T saves $0.65 on the standard workload. At 10x that traffic, the same price gap is about $6.53. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Ling-2.6-1T stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickLing-2.6-1TQwen3.5 Plus 2026-02-15
Input-heavy / RAG5M input + 500K outputLing-2.6-1T$0.69$2.08
Balanced workload1M input + 1M outputLing-2.6-1T$0.7$1.82
Output-heavy chatbot1M input + 5M outputLing-2.6-1T$3.2$8.06
Cheaper input Ling-2.6-1T $0.075 vs $0.26 / 1M

Ling-2.6-1T is $0.18 cheaper per 1M input tokens (71.2% lower; 3.47x difference).

Cheaper output Ling-2.6-1T $0.625 vs $1.56 / 1M

Ling-2.6-1T is $0.94 cheaper per 1M output tokens (59.9% lower; 2.5x difference).

Larger context Qwen3.5 Plus 2026-02-15 262.14K vs 1M

Qwen3.5 Plus 2026-02-15 has 737.86K more context (3.81x larger).

Sample workload Ling-2.6-1T $0.39 vs $1.04

Ling-2.6-1T is $0.65 cheaper on the standard workload (62.7% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Ling-2.6-1T Calculating… Estimated API cost
Qwen3.5 Plus 2026-02-15 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-2.6-1T has the lower input price; Ling-2.6-1T has the lower output price; Qwen3.5 Plus 2026-02-15 offers the larger context window. For the 1M input plus 500K output sample, Ling-2.6-1T is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.39 for Ling-2.6-1T and $1.04 for Qwen3.5 Plus 2026-02-15.

Best Fit

Choose Ling-2.6-1T when you care most about lower input-token price, and lower output-token price.

Choose Qwen3.5 Plus 2026-02-15 when you care most about larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $1.04 for Qwen3.5 Plus 2026-02-15, saving $0.65 (62.7% lower).
  • Ling-2.6-1T is $0.65 cheaper on the standard workload (62.7% lower).
  • Ling-2.6-1T is $0.18 cheaper per 1M input tokens (71.2% lower; 3.47x difference).
  • Ling-2.6-1T is $0.94 cheaper per 1M output tokens (59.9% lower; 2.5x difference).
  • Qwen3.5 Plus 2026-02-15 has 737.86K more context (3.81x larger).
Head-to-Head Specs
FeatureLing-2.6-1T
(inclusionAI)
Qwen3.5 Plus 2026-02-15
(Qwen)
Input Price
prompt tokens per 1M
$0.075$0.26
Completion Price
per 1M tokens
$0.625$1.56
Sample Workload Cost
1M input + 500K output
$0.39$1.04
Context Window262.14K1M
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLing-2.6-1TOn the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $1.04 for Qwen3.5 Plus 2026-02-15, saving $0.65 (62.7% lower).
High-volume input processingLing-2.6-1TLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLing-2.6-1TLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workQwen3.5 Plus 2026-02-15A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Larger context near this budget

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

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

Open provider hubs

inclusionAI catalog

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

Open inclusionAI models

Qwen catalog

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

Open Qwen models
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...

Qwen3.5 Plus 2026-02-15

The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...