Ling-3.0-flash is $0.38 cheaper per 1M input tokens (94.8% lower; 19x difference).
API cost decision in 10 seconds
NewQwen3.8 27B vs Ling-3.0-flash
Pick Ling-3.0-flash for lower cost; pick Qwen3.8 27B only if the larger context window matters more.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Ling-3.0-flash for lower cost; pick Qwen3.8 27B only if the larger context window matters more.
On the standard 1M input plus 500K output workload, Ling-3.0-flash is estimated at $0.05 vs $1.9 for Qwen3.8 27B, saving $1.85 (97.2% lower).
Cost-first pickLing-3.0-flash
Context-first pickQwen3.8 27B
Sample savings$1.8597.2%
10x traffic gap$18.47
Qwen3.8 27B has more context, but Ling-3.0-flash saves $1.85 on the standard workload. At 10x that traffic, the same price gap is about $18.47. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Ling-3.0-flash stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Qwen3.8 27B | Ling-3.0-flash |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Ling-3.0-flash | $3.5 | $0.14 |
| Balanced workload | 1M input + 1M output | Ling-3.0-flash | $3.4 | $0.08 |
| Output-heavy chatbot | 1M input + 5M output | Ling-3.0-flash | $15.4 | $0.34 |
Ling-3.0-flash is $2.94 cheaper per 1M output tokens (97.9% lower; 47.6x difference).
Qwen3.8 27B has 737.86K more context (3.81x larger).
Ling-3.0-flash is $1.85 cheaper on the standard workload (97.2% lower).
Estimate your workload cost
Your Workload Cost
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
Ling-3.0-flash has the lower input price; Ling-3.0-flash has the lower output price; Qwen3.8 27B offers the larger context window. For the 1M input plus 500K output sample, Ling-3.0-flash is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $1.9 for Qwen3.8 27B and $0.05 for Ling-3.0-flash.
Choose Qwen3.8 27B when you care most about larger context window.
Choose Ling-3.0-flash when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, Ling-3.0-flash is estimated at $0.05 vs $1.9 for Qwen3.8 27B, saving $1.85 (97.2% lower).
- Ling-3.0-flash is $1.85 cheaper on the standard workload (97.2% lower).
- Ling-3.0-flash is $0.38 cheaper per 1M input tokens (94.8% lower; 19x difference).
- Ling-3.0-flash is $2.94 cheaper per 1M output tokens (97.9% lower; 47.6x difference).
- Qwen3.8 27B has 737.86K more context (3.81x larger).
| Feature | NewQwen3.8 27B (Qwen) | Ling-3.0-flash (inclusionAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.4 | $0.021 |
| Completion Price per 1M tokens | $3 | $0.063 |
| Sample Workload Cost 1M input + 500K output | $1.9 | $0.05 |
| Context Window | 1M | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Ling-3.0-flash | On the standard 1M input plus 500K output workload, Ling-3.0-flash is estimated at $0.05 vs $1.9 for Qwen3.8 27B, saving $1.85 (97.2% lower). |
| High-volume input processing | Ling-3.0-flash | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Ling-3.0-flash | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Qwen3.8 27B | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- Qwen3 Next 80B A3B Instruct (free) can replace Qwen3.8 27B when lower sample workload cost matters most: $0.
- Qwen3 Coder 480B A35B (free) can replace Qwen3.8 27B when lower sample workload cost matters most: $0.
- Qwen3.7 Flash can replace Qwen3.8 27B when lower sample workload cost matters most: $0.1.
- Qwen3 30B A3B Instruct 2507 can replace Qwen3.8 27B when lower sample workload cost matters most: $0.14.
- DeepSeek V4 Flash 0731 offers 1.31M context with $0.17 sample workload cost.
- DeepSeek V4 Flash Latest offers 1.31M context with $0.11 sample workload cost.
- Llama 4 Scout offers 1.31M context with $0.25 sample workload cost.
- MiMo-V2.5 offers 1.05M context with $0.28 sample workload cost.
- DeepSeek V4 Flash 0731 · DeepSeek · #1
- MiMo-V2.5 · Xiaomi · #2
- Hy3 · Tencent · #3
- Ox Alpha · stealth · #4
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Open inclusionAI modelsQwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be...
*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enabling developers...