Model vs model
Llama 4 Scout vs Trinity Large Thinking
Pick Trinity Large Thinking when output quality matters most: it leads Llama 4 Scout on the Intelligence Index, 10.8 against 6.5, and on every shared benchmark. Its main cost is price, at $0.39 per 1M tokens blended against $0.29. Choose Llama 4 Scout for high-volume, cheaper work, or for image inputs, which Trinity Large Thinking does not accept.
At a glance
Longer bar means the bigger number.
Blended price · per 1M tokens, lower is better
Intelligence Index · Artificial Analysis, higher is better
Context window · tokens it can read at once
Where they sit among all 44 models
Llama 4 Scout ranks 43rd of the 43 models with an Intelligence Index, Trinity Large Thinking 40th. Up and to the left is smarter for less money. Hover or tap a dot for its numbers, then click it to compare that model with Llama 4 Scout.
Which one to pick
Pick Llama 4 Scout if
- you want the lower bill ($0.29 vs $0.39 per 1M tokens blended)
- you need to send images
Pick Trinity Large Thinking if
- you want the higher overall score (10.8 vs 6.5)
- you care about SciCode (scientific coding): 40.6 vs 21.3
- you care about coding: 25.8 vs 8.2
- you need a longer context window (262K vs 128K tokens)
How smart is each one?
Of the 15 scores both models have, Trinity Large Thinking is ahead on 13, and 2 are within a point.
Every intelligence score we have
Out of 100, higher is better. Llama 4 Scout Trinity Large Thinking
Overall
Intelligence Index
6.510.8
By area
Coding
8.225.8Math
14Trinity Large Thinking: not scored yet
Agentic work
0.5 · 1
By field of work
Finance & accounting
6.610.5Legal
8.111.3Economics
10.516.6Engineering
9.516.2Strategy & operations
4.96.7
Benchmarks
Hard reasoning · Humanity's Last Exam
3.815.8Science · GPQA Diamond
58.775.2Knowledge · MMLU-Pro
75.2Trinity Large Thinking: not scored yet
Competitive coding · LiveCodeBench
29.9Trinity Large Thinking: not scored yet
Scientific coding · SciCode
21.340.6Terminal tasks · Terminal-Bench 4.0
0 · 0.5Tool use · τ-Bench Banking
3.35.8Instruction following · IFBench
39.556.3Long documents · AA-LCR
27.738Competition math · AIME 2025
14Trinity Large Thinking: not scored yet
Competition math · AIME 2024
28.3Trinity Large Thinking: not scored yet
Math · MATH-500
84.4Trinity Large Thinking: not scored yet
The specs
| Spec | Llama 4 Scout | Trinity Large Thinking |
|---|---|---|
| Input price | $0.17 per 1M | $0.25 per 1M |
| Output price | $0.66 per 1M | $0.80 per 1M |
| Cost per test task | Not tested | Not tested |
| Context window | 128K | 262K |
| Max output | 8K | 80K |
| Accepts | Text, Image | Text |
| Open weights | Yes | Yes |
| Lab | Meta | Arcee AI |
| Released | Apr 2025 | Apr 2026 |
| Knowledge cutoff | Aug 2024 | Not published |
Blended price weighs 3 parts input to 1 part output. List prices from models.dev.
Questions
Is Llama 4 Scout or Trinity Large Thinking cheaper?
Llama 4 Scout is cheaper. It charges $0.17 per 1M input tokens and $0.66 per 1M output tokens, against $0.25 and $0.80 for Trinity Large Thinking. Blended 3 parts input to 1 part output, that is $0.29 against $0.39 per 1M tokens.
Which is smarter, Llama 4 Scout or Trinity Large Thinking?
Trinity Large Thinking scores higher on the Artificial Analysis Intelligence Index, 10.8 against 6.5 for Llama 4 Scout. The biggest differences are on SciCode (21.3 vs 40.6) and Coding (8.2 vs 25.8).
Which has the longer context window?
Trinity Large Thinking accepts 262K tokens, against 128K for Llama 4 Scout.
Can I compare Llama 4 Scout and Trinity Large Thinking on my own prompt?
Yes. ModelCompare sends one prompt to both models at once and shows each answer side by side with what it cost. You can try it free without an account.
Related comparisons
Where the numbers come from
Intelligence Index and benchmark scores are from Artificial Analysis, at each model’s highest effort, except SimpleQA, which is from Epoch AI, and the Uncensored score, which is from the UGI Leaderboard. The hallucination test is ours: questions with one exact answer, fake evidence pushed against right answers and requests that tempt a model to name packages that don’t exist. Prices are list prices from models.dev. Example answers come from our own test runs. Updated October 25, 2026.