Trinity Large Thinking
openrouter/trinity-large-thinkingPor openrouter · familia: trinity · lanzado 2026-04-01
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding59
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)8/20
- Provider availability1/10
Agents66
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output48
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency62
- Headline price (log-scaled)62/95
- Has prompt-cache pricing0/5
Long context61
- Context window (100K → 2M)51/90
- Has published price for full window10/10
Production-readiness65
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $1.53 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $3.05 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.86 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $2.61 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $3.15 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalle de precios
Precio recomendado de openrouter · arcee-ai/trinity-large-thinking
Disponible en 1 proveedores
| Proveedor | ID de modelo del proveedor | Entrada / 1M | Salida / 1M | Contexto | Lanzado |
|---|---|---|---|---|---|
| OpenRouter openrouter | arcee-ai/trinity-large-thinking | $0.220 | $0.850 | 262K | 2026-04-01 |
Frequently asked questions
How much does Trinity Large Thinking cost?
Trinity Large Thinking costs $0.220 per 1M input tokens and $0.850 per 1M output tokens, sourced from openrouter. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Trinity Large Thinking?
Trinity Large Thinking has a context window of 262K tokens, with a max output of 80K tokens per reply. This is the total combined size of prompt + completion.
Does Trinity Large Thinking support tool calling?
Yes. Trinity Large Thinking supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Trinity Large Thinking support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Trinity Large Thinking in our data source. Verify with openrouter's official documentation before relying on it in production.
Can Trinity Large Thinking accept image input?
No. Trinity Large Thinking only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Trinity Large Thinking open-weight?
Yes. Trinity Large Thinking's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Trinity Large Thinking?
If Trinity Large Thinking doesn't fit, consider LFM2.5-1.2B-Instruct (free), LFM2.5-1.2B-Thinking (free), Owl Alpha. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More openrouter models
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- LFM2.5-1.2B-Thinking (free)Unknown pricing
- Owl AlphaUnknown pricing
- Pareto Code RouterUnknown pricing
- Elephant (free)Unknown pricing
Capability lists this model is in
Última actualización:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.