DeepSeek V4 Pro 0423 is $0.6 cheaper per 1M input tokens (60.3% lower; 2.52x difference).
API cost decision in 10 seconds
🔥DeepSeek V4 Pro 0423 vs GPT-5.6 Terra Pro (batch)
Pick DeepSeek V4 Pro 0423 for lower cost; pick GPT-5.6 Terra Pro (batch) only if the larger context window matters more.
Page updated:
Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick DeepSeek V4 Pro 0423 for lower cost; pick GPT-5.6 Terra Pro (batch) only if the larger context window matters more.
On the standard 1M input plus 500K output workload, DeepSeek V4 Pro 0423 is estimated at $0.79 vs $4 for GPT-5.6 Terra Pro (batch), saving $3.21 (80.2% lower).
Cost-first pickDeepSeek V4 Pro 0423
Context-first pickGPT-5.6 Terra Pro (batch)
Sample savings$3.2180.2%
10x traffic gap$32.06
GPT-5.6 Terra Pro (batch) has more context, but DeepSeek V4 Pro 0423 saves $3.21 on the standard workload. At 10x that traffic, the same price gap is about $32.06. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
DeepSeek V4 Pro 0423 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | DeepSeek V4 Pro 0423 | GPT-5.6 Terra Pro (batch) |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | DeepSeek V4 Pro 0423 | $2.38 | $8 |
| Balanced workload | 1M input + 1M output | DeepSeek V4 Pro 0423 | $1.19 | $7 |
| Output-heavy chatbot | 1M input + 5M output | DeepSeek V4 Pro 0423 | $4.37 | $31 |
DeepSeek V4 Pro 0423 is $5.21 cheaper per 1M output tokens (86.8% lower; 7.56x difference).
GPT-5.6 Terra Pro (batch) has 1.42K more context (1x larger).
DeepSeek V4 Pro 0423 is $3.21 cheaper on the standard workload (80.2% 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
DeepSeek V4 Pro 0423 has the lower input price; DeepSeek V4 Pro 0423 has the lower output price; GPT-5.6 Terra Pro (batch) offers the larger context window. For the 1M input plus 500K output sample, DeepSeek V4 Pro 0423 is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $0.79 for DeepSeek V4 Pro 0423 and $4 for GPT-5.6 Terra Pro (batch).
Choose DeepSeek V4 Pro 0423 when you care most about lower input-token price, and lower output-token price.
Choose GPT-5.6 Terra Pro (batch) when you care most about larger context window.
- On the standard 1M input plus 500K output workload, DeepSeek V4 Pro 0423 is estimated at $0.79 vs $4 for GPT-5.6 Terra Pro (batch), saving $3.21 (80.2% lower).
- DeepSeek V4 Pro 0423 is $3.21 cheaper on the standard workload (80.2% lower).
- DeepSeek V4 Pro 0423 is $0.6 cheaper per 1M input tokens (60.3% lower; 2.52x difference).
- DeepSeek V4 Pro 0423 is $5.21 cheaper per 1M output tokens (86.8% lower; 7.56x difference).
- GPT-5.6 Terra Pro (batch) has 1.42K more context (1x larger).
| Feature | 🔥DeepSeek V4 Pro 0423 (DeepSeek) | GPT-5.6 Terra Pro (batch) (OpenAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.3969 | $1 |
| Completion Price per 1M tokens | $0.7938 | $6 |
| Sample Workload Cost 1M input + 500K output | $0.79 | $4 |
| Context Window | 1.05M | 1.05M |
| Release Date | ||
| Popularity | #10 |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | DeepSeek V4 Pro 0423 | On the standard 1M input plus 500K output workload, DeepSeek V4 Pro 0423 is estimated at $0.79 vs $4 for GPT-5.6 Terra Pro (batch), saving $3.21 (80.2% lower). |
| High-volume input processing | DeepSeek V4 Pro 0423 | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | DeepSeek V4 Pro 0423 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | GPT-5.6 Terra Pro (batch) | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- DeepSeek V4 Flash (free) can replace DeepSeek V4 Pro 0423 when lower sample workload cost matters most: $0.
- DeepSeek V4 Flash 0423 can replace DeepSeek V4 Pro 0423 when lower sample workload cost matters most: $0.1.
- DeepSeek V4 Flash 0731 can replace DeepSeek V4 Pro 0423 when lower sample workload cost matters most: $0.17.
- R1 Distill Qwen 32B can replace DeepSeek V4 Pro 0423 when lower sample workload cost matters most: $0.43.
- Grok 4.20 Multi-Agent offers 2M context with $2.5 sample workload cost.
- Grok 4.20 offers 2M context with $2.5 sample workload cost.
- 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.
- 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
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 hubsDeepSeek catalog
Review all tracked DeepSeek models before deciding whether this matchup is the right shortlist.
Open DeepSeek modelsOpenAI catalog
Check other OpenAI models with comparable pricing, context, or release timing.
Open OpenAI modelsDeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
GPT-5.6 Terra Pro is the same underlying model as [GPT-5.6 Terra](https://openrouter.ai/openai/gpt-5.6-terra), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode