API cost decision in 10 seconds

🔥DeepSeek V3.2 vs Qwen2.5 VL 72B Instruct

Pick DeepSeek V3.2 when budget is the priority.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick DeepSeek V3.2 when budget is the priority.

On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $0.62 for Qwen2.5 VL 72B Instruct, saving $0.18 (29.4% lower).

Cost-first pickDeepSeek V3.2
Context-first pickBoth models
Sample savings$0.1829.4%
10x traffic gap$1.84

The reported context window is tied, so cost and provider fit carry more weight. 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

Same prices, different token mixes.

DeepSeek V3.2 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickDeepSeek V3.2Qwen2.5 VL 72B Instruct
Input-heavy / RAG5M input + 500K outputDeepSeek V3.2$1.45$1.62
Balanced workload1M input + 1M outputDeepSeek V3.2$0.63$1
Output-heavy chatbot1M input + 5M outputDeepSeek V3.2$2.14$4
Cheaper input Qwen2.5 VL 72B Instruct $0.252 vs $0.25 / 1M

Qwen2.5 VL 72B Instruct is $0.002 cheaper per 1M input tokens (0.8% lower; 1.01x difference).

Cheaper output DeepSeek V3.2 $0.378 vs $0.75 / 1M

DeepSeek V3.2 is $0.37 cheaper per 1M output tokens (49.6% lower; 1.98x difference).

Larger context Tie 131.07K vs 131.07K

Both models report the same context window at 131.07K tokens.

Sample workload DeepSeek V3.2 $0.44 vs $0.62

DeepSeek V3.2 is $0.18 cheaper on the standard workload (29.4% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
DeepSeek V3.2 Calculating… Estimated API cost
Qwen2.5 VL 72B Instruct Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

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

Verdict

Qwen2.5 VL 72B Instruct has the lower input price; DeepSeek V3.2 has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, DeepSeek V3.2 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.44 for DeepSeek V3.2 and $0.62 for Qwen2.5 VL 72B Instruct.

Best Fit

Choose DeepSeek V3.2 when you care most about lower output-token price.

Choose Qwen2.5 VL 72B Instruct when you care most about lower input-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $0.62 for Qwen2.5 VL 72B Instruct, saving $0.18 (29.4% lower).
  • DeepSeek V3.2 is $0.18 cheaper on the standard workload (29.4% lower).
  • Qwen2.5 VL 72B Instruct is $0.002 cheaper per 1M input tokens (0.8% lower; 1.01x difference).
  • DeepSeek V3.2 is $0.37 cheaper per 1M output tokens (49.6% lower; 1.98x difference).
  • Both models report the same context window at 131.07K tokens.
Head-to-Head Specs
Feature🔥DeepSeek V3.2
(DeepSeek)
Qwen2.5 VL 72B Instruct
(Qwen)
Input Price
prompt tokens per 1M
$0.252$0.25
Completion Price
per 1M tokens
$0.378$0.75
Sample Workload Cost
1M input + 500K output
$0.44$0.62
Context Window131.07K131.07K
Release Date
Popularity#8#150

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionDeepSeek V3.2On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $0.62 for Qwen2.5 VL 72B Instruct, saving $0.18 (29.4% lower).
High-volume input processingQwen2.5 VL 72B InstructLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsDeepSeek V3.2Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workTieA larger context window leaves more room for retrieved passages, conversation history, or source files.

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Larger context alternatives

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Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

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DeepSeek catalog

Review all tracked DeepSeek models before deciding whether this matchup is the right shortlist.

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Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

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DeepSeek V3.2

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...

Qwen2.5 VL 72B Instruct

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.