Intelligence
31.9
32
General reasoning and benchmark headroom.
LimitedArcee AI: Trinity Large Thinking is a budget-priced text-first model from Arcee AI with fast runtime profile, large context posture, and the clearest fit around long-context research / agent workflows.
Benchmark blend
Dev workflow signal
Large
Budget tier
Arcee AI: Trinity Large Thinking currently reads as a budget text-first option with large context and a fast runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
LimitedTTFT 0.60s
Above averageHow much prompt and task state can stay in view.
Above average$0.88 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
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
Arcee AI text-first profile
Long-context research / Agent workflows with large context and fast runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Large context headroom supports repo-wide prompts and long research sessions.
Latency and throughput look responsive enough for interactive loops.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Current metadata points to a text-first profile rather than a broad multimodal one.
Long-context summarization, repo analysis, and policy or document review.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Broad reasoning, knowledge depth, and flagship benchmark posture.
Software implementation, debugging quality, and coding benchmark signal.
Long-horizon execution quality and interactive benchmark evidence.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.