Reasoning · Lambda

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

Llama 3 Lambda Instruct scores 69.1 on GPQA Diamond, placing it #293 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
69.1
0–100 scale
Rank
#293
of 600
Percentile
51th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
17.9
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Llama 3 Lambda 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 Lambda Instruct sits at #293 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 17.9 points. The median model in this index scores 68.8, which puts Llama 3 Lambda Instruct above the midpoint.

Lambda models average 62.1 on this benchmark. Llama 3 Lambda Instruct is 7.0 points above that provider line, so it is a stronger result than the average Lambda model.

Among multimodal models, the average is 73.5. Llama 3 Lambda Instruct is 4.4 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 69.1 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 $3.92 per million input tokens, Llama 3 Lambda 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
Gemma 3 Instruct Google DeepMind 128000 $3.72 74.1
Llama 3 DeepInfra DeepInfra 128000 $3.67 74.0
Auto Turbo OpenRouter 65536 $7.96 70.9
Hunyuan-Large Base Tencent 200000 $3.64 74.0
Llama 3 Lambda Instruct Lambda 32768 $3.92 75.8
Grok 4 Nano xAI 16384 $8.97 72.7
Llama 3 DeepInfra Chat DeepInfra 200000 $3.74 75.0

Other benchmarks for Llama 3 Lambda Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.8 154 74th
SWE-bench Verified 64.3 226 62th
HumanEval 78.6 274 54th
MATH-500 78.6 247 59th
AIME 2025 74.8 158 74th
LiveCodeBench 70.5 181 70th
IFEval 84.2 211 65th
TruthfulQA 76.0 305 49th
HellaSwag 87.0 178 70th
MMMU 70.0 303 50th
Arena Elo 1382.5 268 55th

FAQ

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

Llama 3 Lambda Instruct scores 69.1 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Llama 3 Lambda Instruct rank on GPQA Diamond?

It ranks #293 of 600 models, in the 51st percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Llama 3 Lambda 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 Lambda Instruct cost?

Llama 3 Lambda Instruct costs $3.92 per million input tokens and $19.48 per million output tokens.

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