API cost decision in 10 seconds

Relace Search vs DeepSeek V3.2 Speciale

Pick DeepSeek V3.2 Speciale for lower cost; pick Relace Search 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 V3.2 Speciale for lower cost; pick Relace Search only if the larger context window matters more.

On the standard 1M input plus 500K output workload, DeepSeek V3.2 Speciale is estimated at $0.5 vs $2.5 for Relace Search, saving $2 (79.9% lower).

Cost-first pickDeepSeek V3.2 Speciale
Context-first pickRelace Search
Sample savings$279.9%
10x traffic gap$19.98

Relace Search has more context, but DeepSeek V3.2 Speciale saves $2 on the standard workload. At 10x that traffic, the same price gap is about $19.98. 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 Speciale stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickRelace SearchDeepSeek V3.2 Speciale
Input-heavy / RAG5M input + 500K outputDeepSeek V3.2 Speciale$6.5$1.65
Balanced workload1M input + 1M outputDeepSeek V3.2 Speciale$4$0.72
Output-heavy chatbot1M input + 5M outputDeepSeek V3.2 Speciale$16$2.44
Cheaper input DeepSeek V3.2 Speciale $1 vs $0.287 / 1M

DeepSeek V3.2 Speciale is $0.71 cheaper per 1M input tokens (71.3% lower; 3.48x difference).

Cheaper output DeepSeek V3.2 Speciale $3 vs $0.431 / 1M

DeepSeek V3.2 Speciale is $2.57 cheaper per 1M output tokens (85.6% lower; 6.96x difference).

Larger context Relace Search 256K vs 163.84K

Relace Search has 92.16K more context (1.56x larger).

Sample workload DeepSeek V3.2 Speciale $2.5 vs $0.5

DeepSeek V3.2 Speciale is $2 cheaper on the standard workload (79.9% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Relace Search Calculating… Estimated API cost
DeepSeek V3.2 Speciale 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 Speciale has the lower input price; DeepSeek V3.2 Speciale has the lower output price; Relace Search offers the larger context window. For the 1M input plus 500K output sample, DeepSeek V3.2 Speciale is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $2.5 for Relace Search and $0.5 for DeepSeek V3.2 Speciale.

Best Fit

Choose Relace Search when you care most about larger context window.

Choose DeepSeek V3.2 Speciale 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 Speciale is estimated at $0.5 vs $2.5 for Relace Search, saving $2 (79.9% lower).
  • DeepSeek V3.2 Speciale is $2 cheaper on the standard workload (79.9% lower).
  • DeepSeek V3.2 Speciale is $0.71 cheaper per 1M input tokens (71.3% lower; 3.48x difference).
  • DeepSeek V3.2 Speciale is $2.57 cheaper per 1M output tokens (85.6% lower; 6.96x difference).
  • Relace Search has 92.16K more context (1.56x larger).
Head-to-Head Specs
FeatureRelace Search
(Relace)
DeepSeek V3.2 Speciale
(DeepSeek)
Input Price
prompt tokens per 1M
$1$0.287
Completion Price
per 1M tokens
$3$0.431
Sample Workload Cost
1M input + 500K output
$2.5$0.5
Context Window256K163.84K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionDeepSeek V3.2 SpecialeOn the standard 1M input plus 500K output workload, DeepSeek V3.2 Speciale is estimated at $0.5 vs $2.5 for Relace Search, saving $2 (79.9% lower).
High-volume input processingDeepSeek V3.2 SpecialeLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsDeepSeek V3.2 SpecialeLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workRelace SearchA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Relace Apply 3 can replace Relace Search when lower sample workload cost matters most: $1.48.
  • DeepSeek V4 Flash (free) can replace DeepSeek V3.2 Speciale when lower sample workload cost matters most: $0.
  • DeepSeek V4 Flash can replace DeepSeek V3.2 Speciale when lower sample workload cost matters most: $0.2.
  • R1 Distill Qwen 32B can replace DeepSeek V3.2 Speciale when lower sample workload cost matters most: $0.43.
Larger context near this budget
  • Llama 4 Scout offers 10M context with $0.23 sample workload cost.
  • Grok 4.20 offers 2M context with $2.5 sample workload cost.
  • Owl Alpha offers 1.05M context with $0 sample workload cost.
  • DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

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

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Relace catalog

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

Open Relace models

DeepSeek catalog

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

Open DeepSeek models
Relace Search

The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search performs agentic...

DeepSeek V3.2 Speciale

DeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized for maximum reasoning and agentic performance. It builds on DeepSeek Sparse Attention (DSA) for efficient long-context processing, then scales post-training reinforcement learning...