Coding · Anyscale

Llama 3 Anyscale 8B on LiveCodeBench: score, rank and what it means

Llama 3 Anyscale 8B scores 46.2 on LiveCodeBench, placing it #484 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
46.2
0–100 scale
Rank
#484
of 600
Percentile
19th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
38.9
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Llama 3 Anyscale 8B 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 Anyscale 8B sits at #484 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 38.9 points. The median model in this index scores 64.7, which puts Llama 3 Anyscale 8B below the midpoint.

Anyscale models average 55.6 on this benchmark. Llama 3 Anyscale 8B is 9.4 points below that provider line, so it is a weaker result than the average Anyscale model.

Among small open-weight models, the average is 41.2. Llama 3 Anyscale 8B is 5.0 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 46.2 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 $0.97 per million input tokens, Llama 3 Anyscale 8B 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
Pythia Instruct EleutherAI 8192 $1.41 60.8
Llama 3 Hyperbolic 8B Hyperbolic 16384 $0.92 56.0
Yi-Lightning 8B 01.AI 4096 $1.16 59.2
Auto Nano OpenRouter 4096 $2.83 57.1
Llama 3 Anyscale 8B Anyscale 32768 $0.97 57.3
Yi-Large 8B 01.AI 32768 $0.88 56.1
Llama 3 Hyperbolic 7B Hyperbolic 4096 $0.94 55.6

Other benchmarks for Llama 3 Anyscale 8B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 60.8 515 14th
GPQA Diamond 53.8 484 19th
SWE-bench Verified 42.2 500 17th
HumanEval 59.3 511 15th
MATH-500 60.2 459 24th
AIME 2025 46.9 478 20th
IFEval 68.6 483 20th
TruthfulQA 62.2 500 17th
HellaSwag 73.5 466 22th
MMMU 56.8 475 21th
Arena Elo 1251.4 489 19th

FAQ

What score does Llama 3 Anyscale 8B get on LiveCodeBench?

Llama 3 Anyscale 8B scores 46.2 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Llama 3 Anyscale 8B rank on LiveCodeBench?

It ranks #484 of 600 models, in the 19th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Llama 3 Anyscale 8B good at LiveCodeBench?

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 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 Anyscale 8B cost?

Llama 3 Anyscale 8B costs $0.97 per million input tokens and $4.27 per million output tokens.

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