Ling-2.6-1T is $1.93 cheaper per 1M input tokens (96.2% lower; 26.7x difference).
API cost decision in 10 seconds
NewGoogle Gemini Pro Latest vs Ling-2.6-1T
Pick Ling-2.6-1T for lower cost; pick Google Gemini Pro Latest 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-2.6-1T for lower cost; pick Google Gemini Pro Latest 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 $8 for Google Gemini Pro Latest, saving $7.61 (95.2% lower).
Cost-first pickLing-2.6-1T
Context-first pickGoogle Gemini Pro Latest
Sample savings$7.6195.2%
10x traffic gap$76.12
Google Gemini Pro Latest has more context, but Ling-2.6-1T saves $7.61 on the standard workload. At 10x that traffic, the same price gap is about $76.12. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Ling-2.6-1T stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Google Gemini Pro Latest | Ling-2.6-1T |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Ling-2.6-1T | $16 | $0.69 |
| Balanced workload | 1M input + 1M output | Ling-2.6-1T | $14 | $0.7 |
| Output-heavy chatbot | 1M input + 5M output | Ling-2.6-1T | $62 | $3.2 |
Ling-2.6-1T is $11.38 cheaper per 1M output tokens (94.8% lower; 19.2x difference).
Google Gemini Pro Latest has 786.43K more context (4x larger).
Ling-2.6-1T is $7.61 cheaper on the standard workload (95.2% 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-2.6-1T has the lower input price; Ling-2.6-1T has the lower output price; Google Gemini Pro Latest 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 $8 for Google Gemini Pro Latest and $0.39 for Ling-2.6-1T.
Choose Google Gemini Pro Latest when you care most about larger context window.
Choose Ling-2.6-1T when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, Ling-2.6-1T is estimated at $0.39 vs $8 for Google Gemini Pro Latest, saving $7.61 (95.2% lower).
- Ling-2.6-1T is $7.61 cheaper on the standard workload (95.2% lower).
- Ling-2.6-1T is $1.93 cheaper per 1M input tokens (96.2% lower; 26.7x difference).
- Ling-2.6-1T is $11.38 cheaper per 1M output tokens (94.8% lower; 19.2x difference).
- Google Gemini Pro Latest has 786.43K more context (4x larger).
| Feature | NewGoogle Gemini Pro Latest (Google) | Ling-2.6-1T (inclusionAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $2 | $0.075 |
| Completion Price per 1M tokens | $12 | $0.625 |
| Sample Workload Cost 1M input + 500K output | $8 | $0.39 |
| Context Window | 1.05M | 262.14K |
| Release Date |
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 $8 for Google Gemini Pro Latest, saving $7.61 (95.2% 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 | Ling-2.6-1T | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Google Gemini Pro Latest | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
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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...