Coding · Meta AI

Llama 4 Scout Base on LiveCodeBench: score, rank and what it means

Llama 4 Scout Base scores 67.6 on LiveCodeBench, placing it #234 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
67.6
0–100 scale
Rank
#234
of 600
Percentile
61th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
17.5
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Llama 4 Scout Base highlighted. Longer bars mean higher scores.

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, Llama 4 Scout Base sits at #234 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 17.5 points. The median model in this index scores 64.7, which puts Llama 4 Scout Base above the midpoint.

Meta AI models average 59.3 on this benchmark. Llama 4 Scout Base is 8.3 points above that provider line, so it is a stronger result than the average Meta AI model.

Among multimodal models, the average is 70.3. Llama 4 Scout Base is 2.7 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 67.6 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 $3.95 per million input tokens, Llama 4 Scout Base is below the median input price of $4.00 across the 600 models scored on LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3 DeepInfra Chat DeepInfra 200000 $3.74 75.0
Llama 3.1 Cerebras Base Cerebras 65536 $3.36 72.7
o4 Mini OpenAI 200000 $11.92 77.7
Command R+ Instruct Cohere 200000 $4.16 76.2
Llama 4 Scout Base Meta AI 32768 $3.95 75.3
Mistral Small 3 Chat Mistral AI 200000 $3.54 73.7
Reka Flash Reka AI 128000 $9.95 74.1

Other benchmarks for Llama 4 Scout Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.7 158 74th
GPQA Diamond 70.5 251 58th
SWE-bench Verified 65.1 213 65th
HumanEval 81.1 211 65th
MATH-500 78.4 250 58th
AIME 2025 68.2 293 51th
IFEval 82.3 268 55th
TruthfulQA 79.8 189 69th
HellaSwag 83.6 300 50th
MMMU 71.5 257 57th
Arena Elo 1395.6 225 63th

FAQ

What score does Llama 4 Scout Base get on LiveCodeBench?

Llama 4 Scout Base scores 67.6 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Llama 4 Scout Base rank on LiveCodeBench?

It ranks #234 of 600 models, in the 61st percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Llama 4 Scout Base good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench 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 Base cost?

Llama 4 Scout Base costs $3.95 per million input tokens and $18.97 per million output tokens.

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