API cost decision in 10 seconds

Qwen3 235B A22B Instruct 2507 vs Ling-2.6-flash

Pick Ling-2.6-flash when budget is the priority.

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

Budget verdict

Pick Ling-2.6-flash when budget is the priority.

On the standard 1M input plus 500K output workload, Ling-2.6-flash is estimated at $0.03 vs $0.12 for Qwen3 235B A22B Instruct 2507, saving $0.1 (79.3% lower).

Cost-first pickLing-2.6-flash
Context-first pickBoth models
Sample savings$0.179.3%
10x traffic gap$0.96

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $0.96. 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-flash stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickQwen3 235B A22B Instruct 2507Ling-2.6-flash
Input-heavy / RAG5M input + 500K outputLing-2.6-flash$0.4$0.07
Balanced workload1M input + 1M outputLing-2.6-flash$0.17$0.04
Output-heavy chatbot1M input + 5M outputLing-2.6-flash$0.57$0.16
Cheaper input Ling-2.6-flash $0.071 vs $0.01 / 1M

Ling-2.6-flash is $0.06 cheaper per 1M input tokens (85.9% lower; 7.1x difference).

Cheaper output Ling-2.6-flash $0.1 vs $0.03 / 1M

Ling-2.6-flash is $0.07 cheaper per 1M output tokens (70% lower; 3.33x difference).

Larger context Tie 262.14K vs 262.14K

Both models report the same context window at 262.14K tokens.

Sample workload Ling-2.6-flash $0.12 vs $0.03

Ling-2.6-flash is $0.1 cheaper on the standard workload (79.3% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Qwen3 235B A22B Instruct 2507 Calculating… Estimated API cost
Ling-2.6-flash 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-flash has the lower input price; Ling-2.6-flash has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Ling-2.6-flash is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.12 for Qwen3 235B A22B Instruct 2507 and $0.03 for Ling-2.6-flash.

Best Fit

Choose Qwen3 235B A22B Instruct 2507 when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

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

Decision Notes
  • On the standard 1M input plus 500K output workload, Ling-2.6-flash is estimated at $0.03 vs $0.12 for Qwen3 235B A22B Instruct 2507, saving $0.1 (79.3% lower).
  • Ling-2.6-flash is $0.1 cheaper on the standard workload (79.3% lower).
  • Ling-2.6-flash is $0.06 cheaper per 1M input tokens (85.9% lower; 7.1x difference).
  • Ling-2.6-flash is $0.07 cheaper per 1M output tokens (70% lower; 3.33x difference).
  • Both models report the same context window at 262.14K tokens.
Head-to-Head Specs
FeatureQwen3 235B A22B Instruct 2507
(Qwen)		Ling-2.6-flash
(inclusionAI)
Input Price
prompt tokens per 1M		$0.071$0.01
Completion Price
per 1M tokens		$0.1$0.03
Sample Workload Cost
1M input + 500K output		$0.12$0.03
Context Window262.14K262.14K
Release Date
Popularity#42#45

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLing-2.6-flashOn the standard 1M input plus 500K output workload, Ling-2.6-flash is estimated at $0.03 vs $0.12 for Qwen3 235B A22B Instruct 2507, saving $0.1 (79.3% lower).
High-volume input processingLing-2.6-flashLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLing-2.6-flashLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workTieA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Qwen3 Next 80B A3B Instruct (free) can replace Qwen3 235B A22B Instruct 2507 when lower sample workload cost matters most: $0.
  • Qwen3 Coder 480B A35B (free) can replace Qwen3 235B A22B Instruct 2507 when lower sample workload cost matters most: $0.
  • Qwen2.5 7B Instruct can replace Qwen3 235B A22B Instruct 2507 when lower sample workload cost matters most: $0.09.
  • Qwen3.5-9B can replace Qwen3 235B A22B Instruct 2507 when lower sample workload cost matters most: $0.11.

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.

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Provider catalogs

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Qwen catalog

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inclusionAI catalog

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

Open inclusionAI models
Qwen3 235B A22B Instruct 2507

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...

Ling-2.6-flash

Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for real-world agents that require fast responses, strong execution, and high token efficiency....