Coding · Meta AI

Llama 3.3 Instruct on LiveCodeBench: score, rank and what it means

Llama 3.3 Instruct scores 67.1 on LiveCodeBench, placing it #247 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
67.1
0–100 scale
Rank
#247
of 600
Percentile
59th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
18.0
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Llama 3.3 Instruct 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 3.3 Instruct sits at #247 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 18.0 points. The median model in this index scores 64.7, which puts Llama 3.3 Instruct above the midpoint.

Meta AI models average 59.3 on this benchmark. Llama 3.3 Instruct is 7.8 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 3.3 Instruct is 3.2 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 67.1 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 $4.33 per million input tokens, Llama 3.3 Instruct is above 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 Modal Chat Modal 65536 $3.55 73.3
Reka Flash Turbo Reka AI 128000 $10.19 75.1
Llama 3 Baseten Base Baseten 16384 $3.16 72.4
Llama 3.1 Cerebras Instruct Cerebras 65536 $4.11 76.1
Llama 3.3 Instruct Meta AI 128000 $4.33 76.6
Codestral Chat Mistral AI 32768 $3.89 75.8
GPT-4.1 Mini OpenAI 128000 $10.43 74.3

Other benchmarks for Llama 3.3 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.7 254 58th
GPQA Diamond 70.1 257 57th
SWE-bench Verified 63.7 239 60th
HumanEval 82.1 185 69th
MATH-500 81.9 168 72th
AIME 2025 76.9 131 78th
IFEval 85.4 176 71th
TruthfulQA 79.0 219 64th
HellaSwag 86.9 182 70th
MMMU 72.0 245 59th
Arena Elo 1418.0 157 74th

FAQ

What score does Llama 3.3 Instruct get on LiveCodeBench?

Llama 3.3 Instruct scores 67.1 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Llama 3.3 Instruct rank on LiveCodeBench?

It ranks #247 of 600 models, in the 59th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Llama 3.3 Instruct 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 3.3 Instruct cost?

Llama 3.3 Instruct costs $4.33 per million input tokens and $19.59 per million output tokens.

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