API cost decision in 10 seconds

R1 vs Qwen2.5 VL 72B Instruct

Pick Qwen2.5 VL 72B Instruct for lower cost; pick R1 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 Qwen2.5 VL 72B Instruct for lower cost; pick R1 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $1.95 for R1, saving $1.32 (67.9% lower).

Cost-first pickQwen2.5 VL 72B Instruct
Context-first pickR1
Sample savings$1.3267.9%
10x traffic gap$13.25

R1 has more context, but Qwen2.5 VL 72B Instruct saves $1.32 on the standard workload. At 10x that traffic, the same price gap is about $13.25. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen2.5 VL 72B Instruct stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickR1Qwen2.5 VL 72B Instruct
Input-heavy / RAG5M input + 500K outputQwen2.5 VL 72B Instruct$4.75$1.62
Balanced workload1M input + 1M outputQwen2.5 VL 72B Instruct$3.2$1
Output-heavy chatbot1M input + 5M outputQwen2.5 VL 72B Instruct$13.2$4
Cheaper input Qwen2.5 VL 72B Instruct $0.7 vs $0.25 / 1M

Qwen2.5 VL 72B Instruct is $0.45 cheaper per 1M input tokens (64.3% lower; 2.8x difference).

Cheaper output Qwen2.5 VL 72B Instruct $2.5 vs $0.75 / 1M

Qwen2.5 VL 72B Instruct is $1.75 cheaper per 1M output tokens (70% lower; 3.33x difference).

Larger context R1 163.84K vs 131.07K

R1 has 32.77K more context (1.25x larger).

Sample workload Qwen2.5 VL 72B Instruct $1.95 vs $0.62

Qwen2.5 VL 72B Instruct is $1.32 cheaper on the standard workload (67.9% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
R1 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; Qwen2.5 VL 72B Instruct has the lower output price; R1 offers the larger context window. For the 1M input plus 500K output sample, Qwen2.5 VL 72B Instruct is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $1.95 for R1 and $0.62 for Qwen2.5 VL 72B Instruct.

Best Fit

Choose R1 when you care most about larger context window.

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

Decision Notes
  • On the standard 1M input plus 500K output workload, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $1.95 for R1, saving $1.32 (67.9% lower).
  • Qwen2.5 VL 72B Instruct is $1.32 cheaper on the standard workload (67.9% lower).
  • Qwen2.5 VL 72B Instruct is $0.45 cheaper per 1M input tokens (64.3% lower; 2.8x difference).
  • Qwen2.5 VL 72B Instruct is $1.75 cheaper per 1M output tokens (70% lower; 3.33x difference).
  • R1 has 32.77K more context (1.25x larger).
Head-to-Head Specs
FeatureR1
(DeepSeek)
Qwen2.5 VL 72B Instruct
(Qwen)
Input Price
prompt tokens per 1M
$0.7$0.25
Completion Price
per 1M tokens
$2.5$0.75
Sample Workload Cost
1M input + 500K output
$1.95$0.62
Context Window163.84K131.07K
Release Date
Popularity#144#150

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionQwen2.5 VL 72B InstructOn the standard 1M input plus 500K output workload, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $1.95 for R1, saving $1.32 (67.9% 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 chatbotsQwen2.5 VL 72B InstructLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workR1A 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

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DeepSeek catalog

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Qwen catalog

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

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R1

DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....

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.