Ling-3.0-flash is $0.35 cheaper per 1M input tokens (94.4% lower; 17.9x difference).
API cost decision in 10 seconds
Ling-3.0-flash vs Gemini 3.6 Flash (batch)
Pick Ling-3.0-flash for lower cost; pick Gemini 3.6 Flash (batch) 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 Gemini 3.6 Flash (batch) 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 $1.31 for Gemini 3.6 Flash (batch), saving $1.26 (96% lower).
Cost-first pickLing-3.0-flash
Context-first pickGemini 3.6 Flash (batch)
Sample savings$1.2696%
10x traffic gap$12.6
Gemini 3.6 Flash (batch) has more context, but Ling-3.0-flash saves $1.26 on the standard workload. At 10x that traffic, the same price gap is about $12.6. 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 | Gemini 3.6 Flash (batch) |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Ling-3.0-flash | $0.14 | $2.81 |
| Balanced workload | 1M input + 1M output | Ling-3.0-flash | $0.08 | $2.25 |
| Output-heavy chatbot | 1M input + 5M output | Ling-3.0-flash | $0.34 | $9.75 |
Ling-3.0-flash is $1.81 cheaper per 1M output tokens (96.6% lower; 29.8x difference).
Gemini 3.6 Flash (batch) has 786.43K more context (4x larger).
Ling-3.0-flash is $1.26 cheaper on the standard workload (96% 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; Gemini 3.6 Flash (batch) 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 $0.05 for Ling-3.0-flash and $1.31 for Gemini 3.6 Flash (batch).
Choose Ling-3.0-flash when you care most about lower input-token price, and lower output-token price.
Choose Gemini 3.6 Flash (batch) when you care most about larger context window.
- On the standard 1M input plus 500K output workload, Ling-3.0-flash is estimated at $0.05 vs $1.31 for Gemini 3.6 Flash (batch), saving $1.26 (96% lower).
- Ling-3.0-flash is $1.26 cheaper on the standard workload (96% lower).
- Ling-3.0-flash is $0.35 cheaper per 1M input tokens (94.4% lower; 17.9x difference).
- Ling-3.0-flash is $1.81 cheaper per 1M output tokens (96.6% lower; 29.8x difference).
- Gemini 3.6 Flash (batch) has 786.43K more context (4x larger).
| Feature | Ling-3.0-flash (inclusionAI) | Gemini 3.6 Flash (batch) (Google) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.021 | $0.375 |
| Completion Price per 1M tokens | $0.063 | $1.875 |
| Sample Workload Cost 1M input + 500K output | $0.05 | $1.31 |
| Context Window | 262.14K | 1.05M |
| 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 $1.31 for Gemini 3.6 Flash (batch), saving $1.26 (96% 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 | Gemini 3.6 Flash (batch) | 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.
- Gemma 4 26B A4B (free) can replace Gemini 3.6 Flash (batch) when lower sample workload cost matters most: $0.
- 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
Review low-cost models sorted by a standard 1M input plus 500K output workload.
Open cheapest modelsLarger context 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 modelsGoogle catalog
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Open Google 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...
Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...