Coding · Meta AI

Llama 4 Scout Instruct on SWE-bench Verified: score, rank and what it means

Llama 4 Scout Instruct scores 70.1 on SWE-bench Verified, placing it #108 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
70.1
0–100 scale
Rank
#108
of 600
Percentile
82th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
11.6
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 4 Scout Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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 Instruct sits at #108 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 11.6 points. The median model in this index scores 61.0, which puts Llama 4 Scout Instruct above the midpoint.

Meta AI models average 56.9 on this benchmark. Llama 4 Scout Instruct is 13.2 points above that provider line, so it is a stronger result than the average Meta AI model.

Among multimodal models, the average is 66.4. Llama 4 Scout Instruct is 3.7 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 70.1 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 $4.59 per million input tokens, Llama 4 Scout Instruct is above 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.

ModelProviderContextInput / 1MAvg score
Gemini 2.5 Pro Google DeepMind 200000 $15.20 81.6
Kimi K2 Moonshot AI 128000 $5.85 83.0
Kimi K2 Base Moonshot AI 65536 $4.82 79.2
Claude 3.7 Sonnet Anthropic 200000 $14.91 81.6
Llama 4 Scout Instruct Meta AI 32768 $4.59 79.1
Nova Pro Reasoning Amazon 200000 $13.48 79.4
Kimi K2 Chat Moonshot AI 400000 $5.74 82.1

Other benchmarks for Llama 4 Scout Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.0 183 70th
GPQA Diamond 76.1 121 80th
HumanEval 81.6 194 68th
MATH-500 84.7 114 81th
AIME 2025 77.3 125 79th
LiveCodeBench 73.8 113 81th
IFEval 85.2 185 69th
TruthfulQA 79.9 181 70th
HellaSwag 88.4 127 79th
MMMU 73.0 221 63th
Arena Elo 1429.1 122 80th

FAQ

What score does Llama 4 Scout Instruct get on SWE-bench Verified?

Llama 4 Scout Instruct scores 70.1 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 Instruct rank on SWE-bench Verified?

It ranks #108 of 600 models, in the 82nd percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 4 Scout Instruct good at SWE-bench Verified?

Yes, relative to the index: it is above the median and inside the top half of scores. 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 Instruct cost?

Llama 4 Scout Instruct costs $4.59 per million input tokens and $20.15 per million output tokens.

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