API cost decision in 10 seconds
Mercury 2 vs 🔥DeepSeek V3.2
Pick DeepSeek V3.2 when budget and context both matter.
Budget verdict
Pick DeepSeek V3.2 when budget and context both matter.
On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $0.63 for Mercury 2, saving $0.18 (29.4% lower).
DeepSeek V3.2 is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $1.84. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
DeepSeek V3.2 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Mercury 2 | DeepSeek V3.2 |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | DeepSeek V3.2 | $1.63 | $1.45 |
| Balanced workload | 1M input + 1M output | DeepSeek V3.2 | $1 | $0.63 |
| Output-heavy chatbot | 1M input + 5M output | DeepSeek V3.2 | $4 | $2.14 |
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
Mercury 2 has the lower input price, DeepSeek V3.2 has the lower output price, and DeepSeek V3.2 offers the larger context window.
For a 1M input token plus 500K output token workload, the estimated API cost is $0.63 for Mercury 2 and $0.44 for DeepSeek V3.2.
Choose Mercury 2 when you care most about lower input-token price.
Choose DeepSeek V3.2 when you care most about lower output-token price, and larger context window.
| Feature | Mercury 2 (Inception) | 🔥DeepSeek V3.2 (DeepSeek) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.25 | $0.25 |
| Completion Price per 1M tokens | $0.75 | $0.38 |
| Sample Workload Cost 1M input + 500K output | $0.63 | $0.44 |
| Context Window | 128K | 131.07K |
| Release Date | 2026-03-04 | 2025-12-01 |
| Popularity | #8 |
Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple tokens in parallel, achieving...
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | DeepSeek V3.2 | On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $0.63 for Mercury 2, saving $0.18 (29.4% lower). |
| High-volume input processing | Mercury 2 | Lower prompt-token price matters most when prompts or retrieved passages dominate the bill. |
| Long responses and chatbots | DeepSeek V3.2 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | DeepSeek V3.2 | A larger context window leaves more room for retrieved passages and source files. |