Trinity Large Thinking vs Relace Search compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
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Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
| Spec | Trinity Large Thinking | Relace Search | Winner |
|---|---|---|---|
| Maker | Arcee ai | Relace | – |
| Type | Open-weight | Proprietary | ARTrinity Large Thinking |
| Context window | 262K tokens | 256K tokens | ARTrinity Large Thinking |
| Input price | $0.25/M · free self-host | $1/M | ARTrinity Large Thinking |
| Output price | $0.8/M · free self-host | $3/M | ARTrinity Large Thinking |
| Vision / multimodal | No | No | – |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | ARTrinity Large Thinking |
| License | Open weights | Proprietary | ARTrinity Large Thinking |
Trinity Large Thinking is ~3.8× cheaper than Relace Search on output tokens ($0.8 vs $3 per M tokens).
| Capability | Trinity Large Thinking | Relace Search |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✗ | ✗ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✗ | ✗ |
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
| Criterion | Trinity Large Thinking | Relace Search |
|---|---|---|
| Cost-efficiency | 5.0 | 4.0 |
| Context window | 4.0 | 4.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 3.5 | 3.5 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
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...
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...
Trinity Large Thinking is open-weight and competitive on many tasks, but Relace Search may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Yes. Trinity Large Thinking has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Relace Search is API-only and cannot be self-hosted.
Trinity Large Thinking costs $0.8/M output vs $3/M for Relace Search — roughly 4x cheaper via API, and free if you self-host.
Choose Trinity Large Thinking if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Relace Search if you want frontier capability through a managed API with zero infrastructure to run.
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