Llama 4 Scout 8B on SWE-bench Verified: score, rank and what it means
Llama 4 Scout 8B scores 48.1 on SWE-bench Verified, placing it #436 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.
Score distribution
The chart shows the top 10 models on SWE-bench Verified, with Llama 4 Scout 8B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 3 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 4 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
| 5 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 6 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 7 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 8 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On SWE-bench Verified, Llama 4 Scout 8B sits at #436 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 33.6 points. The median model in this index scores 61.0, which puts Llama 4 Scout 8B below the midpoint.
Meta AI models average 56.9 on this benchmark. Llama 4 Scout 8B is 8.8 points below that provider line, so it is a weaker result than the average Meta AI model.
Among small open-weight models, the average is 39.0. Llama 4 Scout 8B is 9.1 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 48.1 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $1.46 per million input tokens, Llama 4 Scout 8B is below the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Firesearch Mini | Fireworks AI | 32768 | $3.94 | 61.5 |
| Reka Flash Nano | Reka AI | 16384 | $3.93 | 61.0 |
| GLM-4.5 8B | Zhipu AI | 32768 | $1.46 | 61.3 |
| Sonar Nano | Perplexity | 4096 | $4.37 | 61.9 |
| Llama 4 Scout 8B | Meta AI | 8192 | $1.46 | 61.2 |
| GPT-NeoX Base | EleutherAI | 16384 | $1.22 | 59.7 |
| Magistral 8B | Mistral AI | 4096 | $1.33 | 60.0 |
Other benchmarks for Llama 4 Scout 8B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 67.5 | 425 | 29th | |
| GPQA Diamond | 58.9 | 418 | 30th | |
| HumanEval | 66.3 | 426 | 29th | |
| MATH-500 | 62.7 | 439 | 27th | |
| AIME 2025 | 50.2 | 458 | 24th | |
| LiveCodeBench | 49.7 | 452 | 25th | |
| IFEval | 71.8 | 430 | 28th | |
| TruthfulQA | 65.8 | 446 | 26th | |
| HellaSwag | 73.7 | 462 | 23th | |
| MMMU | 58.1 | 457 | 24th | |
| Arena Elo | 1266.8 | 466 | 22th |
FAQ
What score does Llama 4 Scout 8B get on SWE-bench Verified?
Llama 4 Scout 8B scores 48.1 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does Llama 4 Scout 8B rank on SWE-bench Verified?
It ranks #436 of 600 models, in the 27th percentile. The leader is GPT-5.2 Pro at 81.7.
Is Llama 4 Scout 8B good at SWE-bench Verified?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.
How is the SWE-bench Verified score calculated?
SWE-bench Verified is a coding benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does Llama 4 Scout 8B cost?
Llama 4 Scout 8B costs $1.46 per million input tokens and $6.51 per million output tokens.