Reasoning · Anyscale

Llama 3 Anyscale Instruct on GPQA Diamond: score, rank and what it means

Llama 3 Anyscale Instruct scores 73.4 on GPQA Diamond, placing it #190 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
73.4
0–100 scale
Rank
#190
of 600
Percentile
68th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
13.6
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Llama 3 Anyscale Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Reasoning 87.0
GPT-5.1 Max 86.4
Claude Opus 4.5 Pro 86.0
GPT-5.1 Reasoning 85.6
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.2 84.9
o4 Turbo 84.8
Claude Opus 4.5 Max 84.6
Grok 4 Turbo 84.5
Claude Sonnet 4.5 Max 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 GPT-5.2 OpenAI 2000000 $19.71 88.0
7 o4 Turbo OpenAI 2000000 $19.34 86.6
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 Grok 4 Turbo xAI 1000000 $19.21 86.7
10 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2

What this score means

On GPQA Diamond, Llama 3 Anyscale Instruct sits at #190 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 13.6 points. The median model in this index scores 68.8, which puts Llama 3 Anyscale Instruct above the midpoint.

Anyscale models average 61.3 on this benchmark. Llama 3 Anyscale Instruct is 12.1 points above that provider line, so it is a stronger result than the average Anyscale model.

Among multimodal models, the average is 73.5. Llama 3 Anyscale Instruct is 0.1 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 73.4 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 $3.91 per million input tokens, Llama 3 Anyscale Instruct is below the median input price of $4.00 across the 600 models scored on GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Phi-4 Instruct Microsoft 128000 $3.79 75.3
QwQ Instruct Alibaba Cloud 65536 $4.62 79.0
Reka Flash Max Reka AI 32768 $11.17 75.9
o4 Mini OpenAI 200000 $11.92 77.7
Llama 3 Anyscale Instruct Anyscale 200000 $3.91 75.7
Llama 3.1 Turbo Instruct Together AI 32768 $4.21 76.7
Nova Pro Reasoning Amazon 200000 $13.48 79.4

Other benchmarks for Llama 3 Anyscale Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.9 280 53th
SWE-bench Verified 61.4 293 51th
HumanEval 77.9 289 52th
MATH-500 79.6 218 64th
AIME 2025 74.5 165 73th
LiveCodeBench 71.4 161 73th
IFEval 83.5 228 62th
TruthfulQA 79.5 201 67th
HellaSwag 85.2 233 61th
MMMU 69.8 310 48th
Arena Elo 1391.8 241 60th

FAQ

What score does Llama 3 Anyscale Instruct get on GPQA Diamond?

Llama 3 Anyscale Instruct scores 73.4 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Llama 3 Anyscale Instruct rank on GPQA Diamond?

It ranks #190 of 600 models, in the 68th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Llama 3 Anyscale Instruct good at GPQA Diamond?

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 GPQA Diamond score calculated?

GPQA Diamond is a reasoning 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 Instruct cost?

Llama 3 Anyscale Instruct costs $3.91 per million input tokens and $14.49 per million output tokens.

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