Reasoning · Groq

Llama 3.3 Groq Instruct on GPQA Diamond: score, rank and what it means

Llama 3.3 Groq Instruct scores 74.0 on GPQA Diamond, placing it #169 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
74.0
0–100 scale
Rank
#169
of 600
Percentile
72th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
13.0
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Llama 3.3 Groq 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 3.3 Groq Instruct sits at #169 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 13.0 points. The median model in this index scores 68.8, which puts Llama 3.3 Groq Instruct above the midpoint.

Groq models average 60.6 on this benchmark. Llama 3.3 Groq Instruct is 13.4 points above that provider line, so it is a stronger result than the average Groq model.

Among multimodal models, the average is 73.5. Llama 3.3 Groq Instruct is 0.5 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 74.0 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 $4.08 per million input tokens, Llama 3.3 Groq Instruct is above 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
Llama 4 Scout Meta AI 200000 $4.90 79.3
Qwen3 Instruct Alibaba Cloud 1000000 $5.06 79.8
DeepSeek-Coder DeepSeek 32768 $4.55 78.1
DeepSeek-Coder Chat DeepSeek 200000 $4.20 77.3
Llama 3.3 Groq Instruct Groq 65536 $4.08 76.7
Hunyuan-Turbo Tencent 128000 $10.55 75.8
Llama 3 Baseten Instruct Baseten 200000 $3.91 76.2

Other benchmarks for Llama 3.3 Groq Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.2 142 76th
SWE-bench Verified 65.1 212 65th
HumanEval 79.6 246 59th
MATH-500 79.3 227 62th
AIME 2025 74.7 160 73th
LiveCodeBench 66.5 265 56th
IFEval 85.7 167 72th
TruthfulQA 79.7 195 68th
HellaSwag 84.8 257 57th
MMMU 73.5 205 66th
Arena Elo 1394.0 234 61th

FAQ

What score does Llama 3.3 Groq Instruct get on GPQA Diamond?

Llama 3.3 Groq Instruct scores 74.0 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Llama 3.3 Groq Instruct rank on GPQA Diamond?

It ranks #169 of 600 models, in the 72nd percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Llama 3.3 Groq 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 3.3 Groq Instruct cost?

Llama 3.3 Groq Instruct costs $4.08 per million input tokens and $22.27 per million output tokens.

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