API cost decision in 10 seconds

Trinity Large Thinking vs Relace Search

Pick Trinity Large Thinking when budget and context both matter.

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

Budget verdict

Pick Trinity Large Thinking when budget and context both matter.

On the standard 1M input plus 500K output workload, Trinity Large Thinking is estimated at $0.65 vs $2.5 for Relace Search, saving $1.85 (74.2% lower).

Cost-first pickTrinity Large Thinking
Context-first pickTrinity Large Thinking
Sample savings$1.8574.2%
10x traffic gap$18.55

Trinity Large Thinking is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $18.55. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Trinity Large Thinking stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickTrinity Large ThinkingRelace Search
Input-heavy / RAG5M input + 500K outputTrinity Large Thinking$1.53$6.5
Balanced workload1M input + 1M outputTrinity Large Thinking$1.07$4
Output-heavy chatbot1M input + 5M outputTrinity Large Thinking$4.47$16
Cheaper input Trinity Large Thinking $0.22 vs $1 / 1M

Trinity Large Thinking is $0.78 cheaper per 1M input tokens (78% lower; 4.55x difference).

Cheaper output Trinity Large Thinking $0.85 vs $3 / 1M

Trinity Large Thinking is $2.15 cheaper per 1M output tokens (71.7% lower; 3.53x difference).

Larger context Trinity Large Thinking 262.14K vs 256K

Trinity Large Thinking has 6.14K more context (1.02x larger).

Sample workload Trinity Large Thinking $0.65 vs $2.5

Trinity Large Thinking is $1.85 cheaper on the standard workload (74.2% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Trinity Large Thinking Calculating… Estimated API cost
Relace Search 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

Trinity Large Thinking has the lower input price; Trinity Large Thinking has the lower output price; Trinity Large Thinking offers the larger context window. For the 1M input plus 500K output sample, Trinity Large Thinking is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.65 for Trinity Large Thinking and $2.5 for Relace Search.

Best Fit

Choose Trinity Large Thinking when you care most about lower input-token price, lower output-token price, and larger context window.

Choose Relace Search when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Decision Notes
  • On the standard 1M input plus 500K output workload, Trinity Large Thinking is estimated at $0.65 vs $2.5 for Relace Search, saving $1.85 (74.2% lower).
  • Trinity Large Thinking is $1.85 cheaper on the standard workload (74.2% lower).
  • Trinity Large Thinking is $0.78 cheaper per 1M input tokens (78% lower; 4.55x difference).
  • Trinity Large Thinking is $2.15 cheaper per 1M output tokens (71.7% lower; 3.53x difference).
  • Trinity Large Thinking has 6.14K more context (1.02x larger).
Head-to-Head Specs
FeatureTrinity Large Thinking
(Arcee AI)		Relace Search
(Relace)
Input Price
prompt tokens per 1M		$0.22$1
Completion Price
per 1M tokens		$0.85$3
Sample Workload Cost
1M input + 500K output		$0.65$2.5
Context Window262.14K256K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionTrinity Large ThinkingOn the standard 1M input plus 500K output workload, Trinity Large Thinking is estimated at $0.65 vs $2.5 for Relace Search, saving $1.85 (74.2% lower).
High-volume input processingTrinity Large ThinkingLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsTrinity Large ThinkingLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workTrinity Large ThinkingA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Trinity Large Thinking (free) can replace Trinity Large Thinking when lower sample workload cost matters most: $0.
  • Trinity Mini can replace Trinity Large Thinking when lower sample workload cost matters most: $0.12.
  • Spotlight can replace Trinity Large Thinking when lower sample workload cost matters most: $0.27.
  • Relace Apply 3 can replace Relace Search when lower sample workload cost matters most: $1.48.
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.

Open provider hubs

Arcee AI catalog

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

Open Arcee AI models

Relace catalog

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

Open Relace models
Trinity Large Thinking

Trinity Large Thinking is a powerful open source reasoning model from the team at Arcee AI. It shows strong performance in PinchBench, agentic workloads, and reasoning tasks. Launch video: https://youtu.be/Gc82AXLa0Rg?si=4RLn6WBz33qT--B7...

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...