Llama 4 Scout Instruct: benchmark scores, price and ranking
Llama 4 Scout Instruct from Meta ranks #565 of 743 LLMs on the LLMs Tiger aggregate benchmark score (45.6), based on 1 benchmark results captured up to 2026-10-13.
- Organisation
- Meta
- Type
- Open-weight / local model
- Open weights
- Yes
- Released
- 2025-04-05 (source: curated)
- Aggregate rank
- #565 of 743 (score 45.6)
Across the 1 benchmark where at least ten models are scored, Llama 4 Scout Instruct is in the top 10% on 0 and in the top half on 0.
Llama 4 Scout Instruct benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| SWE-bench Verified | 9.1% | #69 of 73 | SWE-bench Verified leaderboard | 2026-10-13 |
About Llama 4 Scout Instruct
Llama 4 Scout Instruct is an open-weight model that can run on your own hardware from Meta. It was released on 2025-04-05, according to the developer's announcement. No API price is listed for it yet. We hold 1 benchmark result for it across 1 area, and none is in the top tenth of its board.
Where Llama 4 Scout Instruct stands
On our aggregate score, Llama 4 Scout Instruct is #565 of 743 ranked models, in the lower half. Its score is 45.6; the scale is centred on 50, so it sits 4.4 points below the typical model. That beats 24% of the ranked field. Among the 6 ranked Meta models it is #4. The next one up is Llama 4 Maverick Instruct at 46.7. The next one down is ollama/llama3.2:3b-instruct-fp16 at 44.5.
Llama 4 Scout Instruct, benchmark by benchmark
SWE-bench Verified is a weak spot. Llama 4 Scout Instruct scores 9.1%, #69 of 73, behind 93% of the models measured. The leader, Claude Opus 4.7 (max), reaches 83.5%. It covers real GitHub issues resolved. The board median is 56.4%, so Llama 4 Scout Instruct is 47.3 points below it. Source: SWE-bench Verified leaderboard, captured 2026-10-13.
Release history
The Meta model released before it was Llama 4 Maverick Instruct on 2025-04-05, which scores 46.7.
Reading these numbers
The 1 score on this page comes from 1 source: SWE-bench Verified leaderboard (1). 1 of them come from an official leaderboard and 0 from datasets or third-party runs. With 1 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #562 EXAONE-3.5-2.4B-Instruct (45.7)
- #563 qwen-plus-2025-01-25 (45.7)
- #564 MiMo-V2-Flash (45.6)
- #566 MiniMax-M3 (45.6)
- #567 open-mistral-nemo-2407 (45.6)
- #568 MiniMax-M2.7 (45.6)
More from Meta
- Llama3-SWE-RL-70B (#337, 48.6)
- llama3-70b-8192 (#381, 48.1)
- Llama 4 Maverick Instruct (#491, 46.7)
- ollama/llama3.2:3b-instruct-fp16 (#631, 44.5)
- SWE-Llama 7B (#683, 43.2)
Compare it on the live leaderboard → · How the aggregate score works