Ling-3.0-flash is $0.05 cheaper per 1M input tokens (72% lower; 3.57x difference).
API cost decision in 10 seconds
Ling-3.0-flash vs Ling-2.6-1T
Pick Ling-3.0-flash when budget is the priority.
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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 when budget is the priority.
On the standard 1M input plus 500K output workload, Ling-3.0-flash is estimated at $0.05 vs $0.39 for Ling-2.6-1T, saving $0.34 (86.5% lower).
Cost-first pickLing-3.0-flash
Context-first pickBoth models
Sample savings$0.3486.5%
10x traffic gap$3.35
The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $3.35. 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 | Ling-3.0-flash | Ling-2.6-1T |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Ling-3.0-flash | $0.14 | $0.69 |
| Balanced workload | 1M input + 1M output | Ling-3.0-flash | $0.08 | $0.7 |
| Output-heavy chatbot | 1M input + 5M output | Ling-3.0-flash | $0.34 | $3.2 |
Ling-3.0-flash is $0.56 cheaper per 1M output tokens (89.9% lower; 9.92x difference).
Both models report the same context window at 262.14K tokens.
Ling-3.0-flash is $0.34 cheaper on the standard workload (86.5% 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; both models report the same 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 $0.05 for Ling-3.0-flash and $0.39 for Ling-2.6-1T.
Choose Ling-3.0-flash when you care most about lower input-token price, and lower output-token price.
Choose Ling-2.6-1T when its provider, model quality, latency, or availability is more important than the numeric price/context winner.
- On the standard 1M input plus 500K output workload, Ling-3.0-flash is estimated at $0.05 vs $0.39 for Ling-2.6-1T, saving $0.34 (86.5% lower).
- Ling-3.0-flash is $0.34 cheaper on the standard workload (86.5% lower).
- Ling-3.0-flash is $0.05 cheaper per 1M input tokens (72% lower; 3.57x difference).
- Ling-3.0-flash is $0.56 cheaper per 1M output tokens (89.9% lower; 9.92x difference).
- Both models report the same context window at 262.14K tokens.
| Feature | Ling-3.0-flash (inclusionAI) | Ling-2.6-1T (inclusionAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.021 | $0.075 |
| Completion Price per 1M tokens | $0.063 | $0.625 |
| Sample Workload Cost 1M input + 500K output | $0.05 | $0.39 |
| Context Window | 262.14K | 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 $0.39 for Ling-2.6-1T, saving $0.34 (86.5% 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 | Tie | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- Ling 3.0 Tiny (free) can replace Ling-3.0-flash when lower sample workload cost matters most: $0.
- Ling-3.0-flash (free) can replace Ling-3.0-flash when lower sample workload cost matters most: $0.
- Ling-2.6-flash can replace Ling-3.0-flash when lower sample workload cost matters most: $0.03.
- 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
Cheaper alternatives
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Open provider hubsinclusionAI catalog
Review all tracked inclusionAI models before deciding whether this matchup is the right shortlist.
Open inclusionAI models*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...
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...