Ling-2.6-1T is $0.28 cheaper per 1M input tokens (79.2% lower; 4.8x difference).
API cost decision in 10 seconds
Ling-2.6-1T vs Qwen2.5 72B Instruct
Pick Ling-2.6-1T when budget and context both matter.
Page updated:
Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Ling-2.6-1T when budget and context both matter.
On the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $0.56 for Qwen2.5 72B Instruct, saving $0.17 (30.8% lower).
Cost-first pickLing-2.6-1T
Context-first pickLing-2.6-1T
Sample savings$0.1730.8%
10x traffic gap$1.73
Ling-2.6-1T is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $1.73. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Cost winner changes by workload shape: input-heavy / RAG favors Ling-2.6-1T, balanced workload favors Ling-2.6-1T, and output-heavy chatbot favors Qwen2.5 72B Instruct.
| Workload shape | Token mix | Better pick | Ling-2.6-1T | Qwen2.5 72B Instruct |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Ling-2.6-1T | $0.69 | $2 |
| Balanced workload | 1M input + 1M output | Ling-2.6-1T | $0.7 | $0.76 |
| Output-heavy chatbot | 1M input + 5M output | Qwen2.5 72B Instruct | $3.2 | $2.36 |
Qwen2.5 72B Instruct is $0.22 cheaper per 1M output tokens (36% lower; 1.56x difference).
Ling-2.6-1T has 131.07K more context (2x larger).
Ling-2.6-1T is $0.17 cheaper on the standard workload (30.8% 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-2.6-1T has the lower input price; Qwen2.5 72B Instruct has the lower output price; Ling-2.6-1T 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 $0.56 for Qwen2.5 72B Instruct.
Choose Ling-2.6-1T when you care most about lower input-token price, and larger context window.
Choose Qwen2.5 72B Instruct when you care most about lower output-token price.
- On the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $0.56 for Qwen2.5 72B Instruct, saving $0.17 (30.8% lower).
- Ling-2.6-1T is $0.17 cheaper on the standard workload (30.8% lower).
- Ling-2.6-1T is $0.28 cheaper per 1M input tokens (79.2% lower; 4.8x difference).
- Qwen2.5 72B Instruct is $0.22 cheaper per 1M output tokens (36% lower; 1.56x difference).
- Ling-2.6-1T has 131.07K more context (2x larger).
| Feature | Ling-2.6-1T (inclusionAI) | Qwen2.5 72B Instruct (Qwen) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.075 | $0.36 |
| Completion Price per 1M tokens | $0.625 | $0.4 |
| Sample Workload Cost 1M input + 500K output | $0.39 | $0.56 |
| Context Window | 262.14K | 131.07K |
| Release Date | ||
| Popularity | #106 | #139 |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Ling-2.6-1T | On the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $0.56 for Qwen2.5 72B Instruct, saving $0.17 (30.8% lower). |
| High-volume input processing | Ling-2.6-1T | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Qwen2.5 72B Instruct | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Ling-2.6-1T | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- Ling-2.6-flash can replace Ling-2.6-1T when lower sample workload cost matters most: $0.03.
- Qwen3 Next 80B A3B Instruct (free) can replace Qwen2.5 72B Instruct when lower sample workload cost matters most: $0.
- Qwen3 Coder 480B A35B (free) can replace Qwen2.5 72B Instruct when lower sample workload cost matters most: $0.
- Qwen2.5 7B Instruct can replace Qwen2.5 72B Instruct when lower sample workload cost matters most: $0.09.
- Llama 4 Scout offers 10M context with $0.23 sample workload cost.
- Owl Alpha offers 1.05M context with $0 sample workload cost.
- DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
- MiMo-V2.5 offers 1.05M context with $0.28 sample workload cost.
- Hy3 preview · Tencent · #1
- DeepSeek V4 Flash · DeepSeek · #2
- MiMo-V2.5 · Xiaomi · #3
- Claude Sonnet 4.6 · Anthropic · #4
Cheaper alternatives
Review low-cost models sorted by a standard 1M input plus 500K output workload.
Open cheapest modelsLarger context alternatives
Find models with larger context windows for RAG, long documents, and codebase review.
Open largest context modelsProvider catalogs
Compare models within provider hubs before choosing a final API vendor.
Open provider hubsinclusionAI catalog
Review all tracked inclusionAI models before deciding whether this matchup is the right shortlist.
Open inclusionAI modelsQwen catalog
Check other Qwen models with comparable pricing, context, or release timing.
Open Qwen modelsLing-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...
Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...