API cost decision in 10 seconds

🔥Claude Opus 4.7 vs 🔥DeepSeek V3.2

Pick DeepSeek V3.2 for lower cost; pick Claude Opus 4.7 only if the larger context window matters more.

Pricing data updated:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick DeepSeek V3.2 for lower cost; pick Claude Opus 4.7 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $17.5 for Claude Opus 4.7, saving $17.06 (97.5% lower).

Cost-first pickDeepSeek V3.2
Context-first pickClaude Opus 4.7
Sample savings$17.0697.5%
10x traffic gap$170.59

Claude Opus 4.7 has more context, but DeepSeek V3.2 saves $17.06 on the standard workload. At 10x that traffic, the same price gap is about $170.59. Use the calculator below to replace the sample workload with your own token volume.

Cheaper input DeepSeek V3.2 $5 vs $0.252 / 1M

DeepSeek V3.2 is $4.75 cheaper per 1M input tokens (95% lower; 19.8x difference).

Cheaper output DeepSeek V3.2 $25 vs $0.378 / 1M

DeepSeek V3.2 is $24.62 cheaper per 1M output tokens (98.5% lower; 66.1x difference).

Larger context Claude Opus 4.7 1M vs 131.07K

Claude Opus 4.7 has 868.93K more context (7.63x larger).

Sample workload DeepSeek V3.2 $17.5 vs $0.44

DeepSeek V3.2 is $17.06 cheaper on the standard workload (97.5% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Claude Opus 4.7 Calculating… Estimated API cost
DeepSeek V3.2 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

DeepSeek V3.2 has the lower input price; DeepSeek V3.2 has the lower output price; Claude Opus 4.7 offers the larger 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 $17.5 for Claude Opus 4.7 and $0.44 for DeepSeek V3.2.

Best Fit

Choose Claude Opus 4.7 when you care most about larger context window.

Choose DeepSeek V3.2 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, DeepSeek V3.2 is estimated at $0.44 vs $17.5 for Claude Opus 4.7, saving $17.06 (97.5% lower).
  • DeepSeek V3.2 is $17.06 cheaper on the standard workload (97.5% lower).
  • DeepSeek V3.2 is $4.75 cheaper per 1M input tokens (95% lower; 19.8x difference).
  • DeepSeek V3.2 is $24.62 cheaper per 1M output tokens (98.5% lower; 66.1x difference).
  • Claude Opus 4.7 has 868.93K more context (7.63x larger).
Head-to-Head Specs
Feature🔥Claude Opus 4.7
(Anthropic)
🔥DeepSeek V3.2
(DeepSeek)
Input Price
prompt tokens per 1M
$5$0.252
Completion Price
per 1M tokens
$25$0.378
Sample Workload Cost
1M input + 500K output
$17.5$0.44
Context Window1M131.07K
Release Date2026-04-162025-12-01
Popularity Rank
current rank
#2#7

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 $17.5 for Claude Opus 4.7, saving $17.06 (97.5% lower).
High-volume input processingDeepSeek V3.2Lower 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 workClaude Opus 4.7A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

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Provider catalogs

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Anthropic catalog

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DeepSeek catalog

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Claude Opus 4.7

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...

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...