Instruction following · Anyscale

Llama 3 Anyscale 1.5B on IFEval: score, rank and what it means

Llama 3 Anyscale 1.5B scores 62.3 on IFEval, placing it #553 of 600 models in the index. The benchmark average is 78.6 and the leader is Claude Opus 4.5 Pro at 96.1.

Score
62.3
0–100 scale
Rank
#553
of 600
Percentile
8th
within the index
Benchmark avg
78.6
all models
Leader
96.1
Claude Opus 4.5 Pro
Gap to leader
33.8
points

Score distribution

The chart shows the top 10 models on IFEval, with Llama 3 Anyscale 1.5B highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Pro 96.1
Claude Opus 4.5 Reasoning 95.9
GPT-5.2 Pro 95.6
GPT-5.2 Max 95.2
GPT-5.2 Reasoning 95.0
GPT-5.1 Pro 94.8
Gemini 3 Pro Max 94.7
GPT-5.1 Reasoning 94.6
Claude Opus 4.5 Max 94.5
o4 Reasoning 94.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
8 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 o4 Reasoning OpenAI 1000000 $19.69 87.5

What this score means

On IFEval, Llama 3 Anyscale 1.5B sits at #553 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 33.8 points. The median model in this index scores 81.2, which puts Llama 3 Anyscale 1.5B below the midpoint.

Anyscale models average 76.1 on this benchmark. Llama 3 Anyscale 1.5B is 13.8 points below that provider line, so it is a weaker result than the average Anyscale model.

Among small open-weight models, the average is 64.9. Llama 3 Anyscale 1.5B is 2.6 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 62.3 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 $0.43 per million input tokens, Llama 3 Anyscale 1.5B is below the median input price of $4.00 across the 600 models scored on IFEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Magistral 1.5B Mistral AI 4096 $0.67 52.7
Llama 3 DeepInfra 1.5B DeepInfra 32768 $0.48 48.8
Mistral Small 3 1.5B Mistral AI 32768 $0.42 49.2
Gemma 3 1.5B Google DeepMind 32768 $0.40 48.9
Llama 3 Anyscale 1.5B Anyscale 16384 $0.43 48.9
Yi-Lightning 1.5B 01.AI 4096 $0.54 50.0
Phi-3.5 8B Microsoft 16384 $0.59 51.2

Other benchmarks for Llama 3 Anyscale 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 56.2 567 6th
GPQA Diamond 42.7 577 4th
SWE-bench Verified 35.7 549 9th
HumanEval 50.5 570 5th
MATH-500 47.4 572 5th
AIME 2025 35.1 569 5th
LiveCodeBench 35.8 563 6th
TruthfulQA 56.8 554 8th
HellaSwag 67.0 562 6th
MMMU 48.8 557 7th
Arena Elo 1175.9 579 4th

FAQ

What score does Llama 3 Anyscale 1.5B get on IFEval?

Llama 3 Anyscale 1.5B scores 62.3 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.

How does Llama 3 Anyscale 1.5B rank on IFEval?

It ranks #553 of 600 models, in the 8th percentile. The leader is Claude Opus 4.5 Pro at 96.1.

Is Llama 3 Anyscale 1.5B good at IFEval?

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 IFEval score calculated?

IFEval is a instruction following 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 1.5B cost?

Llama 3 Anyscale 1.5B costs $0.43 per million input tokens and $2.27 per million output tokens.

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