Qwen3 Coder Next is $1.89 cheaper per 1M input tokens (94.5% lower; 18.2x difference).
API cost decision in 10 seconds
NewGoogle Gemini Pro Latest vs Qwen3 Coder Next
Pick Qwen3 Coder Next 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 Qwen3 Coder Next 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, Qwen3 Coder Next is estimated at $0.51 vs $8 for Google Gemini Pro Latest, saving $7.49 (93.6% lower).
Cost-first pickQwen3 Coder Next
Context-first pickGoogle Gemini Pro Latest
Sample savings$7.4993.6%
10x traffic gap$74.9
Google Gemini Pro Latest has more context, but Qwen3 Coder Next saves $7.49 on the standard workload. At 10x that traffic, the same price gap is about $74.9. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Qwen3 Coder Next stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Google Gemini Pro Latest | Qwen3 Coder Next |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Qwen3 Coder Next | $16 | $0.95 |
| Balanced workload | 1M input + 1M output | Qwen3 Coder Next | $14 | $0.91 |
| Output-heavy chatbot | 1M input + 5M output | Qwen3 Coder Next | $62 | $4.11 |
Qwen3 Coder Next is $11.2 cheaper per 1M output tokens (93.3% lower; 15x difference).
Google Gemini Pro Latest has 786.43K more context (4x larger).
Qwen3 Coder Next is $7.49 cheaper on the standard workload (93.6% 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
Qwen3 Coder Next has the lower input price; Qwen3 Coder Next has the lower output price; Google Gemini Pro Latest offers the larger context window. For the 1M input plus 500K output sample, Qwen3 Coder Next 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 $0.51 for Qwen3 Coder Next.
Choose Google Gemini Pro Latest when you care most about larger context window.
Choose Qwen3 Coder Next when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, Qwen3 Coder Next is estimated at $0.51 vs $8 for Google Gemini Pro Latest, saving $7.49 (93.6% lower).
- Qwen3 Coder Next is $7.49 cheaper on the standard workload (93.6% lower).
- Qwen3 Coder Next is $1.89 cheaper per 1M input tokens (94.5% lower; 18.2x difference).
- Qwen3 Coder Next is $11.2 cheaper per 1M output tokens (93.3% lower; 15x difference).
- Google Gemini Pro Latest has 786.43K more context (4x larger).
| Feature | NewGoogle Gemini Pro Latest (Google) | Qwen3 Coder Next (Qwen) |
|---|---|---|
| Input Price prompt tokens per 1M | $2 | $0.11 |
| Completion Price per 1M tokens | $12 | $0.8 |
| Sample Workload Cost 1M input + 500K output | $8 | $0.51 |
| Context Window | 1.05M | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Qwen3 Coder Next | On the standard 1M input plus 500K output workload, Qwen3 Coder Next is estimated at $0.51 vs $8 for Google Gemini Pro Latest, saving $7.49 (93.6% lower). |
| High-volume input processing | Qwen3 Coder Next | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Qwen3 Coder Next | 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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Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...