Reasoning · Anthropic

Claude Haiku 4.5 Nano on GPQA Diamond: score, rank and what it means

Claude Haiku 4.5 Nano scores 64.5 on GPQA Diamond, placing it #369 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
64.5
0–100 scale
Rank
#369
of 600
Percentile
39th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
22.5
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Claude Haiku 4.5 Nano 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, Claude Haiku 4.5 Nano sits at #369 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 22.5 points. The median model in this index scores 68.8, which puts Claude Haiku 4.5 Nano below the midpoint.

Anthropic models average 77.3 on this benchmark. Claude Haiku 4.5 Nano is 12.8 points below that provider line, so it is a weaker result than the average Anthropic model.

Among mid-range models, the average is 62.9. Claude Haiku 4.5 Nano is 1.6 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 64.5 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 $6.47 per million input tokens, Claude Haiku 4.5 Nano 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
Gemma 3 Base Google DeepMind 65536 $3.09 71.1
Nemotron Nano Instruct NVIDIA 32768 $2.33 67.4
Arctic Instruct Snowflake 65536 $2.83 70.6
Nova Pro Mini Amazon 16384 $7.74 70.6
Claude Haiku 4.5 Nano Anthropic 8192 $6.47 68.7
ERNIE 4.5 Mini Baidu 32768 $8.62 71.0
Llama 3 DeepInfra Base DeepInfra 16384 $2.95 71.8

Other benchmarks for Claude Haiku 4.5 Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 73.9 355 41th
SWE-bench Verified 56.9 367 39th
HumanEval 71.0 388 35th
MATH-500 71.8 369 39th
AIME 2025 63.5 361 40th
LiveCodeBench 59.8 369 39th
IFEval 78.2 363 40th
TruthfulQA 71.6 385 36th
HellaSwag 80.5 378 37th
MMMU 64.3 386 36th
Arena Elo 1332.1 385 36th

FAQ

What score does Claude Haiku 4.5 Nano get on GPQA Diamond?

Claude Haiku 4.5 Nano scores 64.5 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Claude Haiku 4.5 Nano rank on GPQA Diamond?

It ranks #369 of 600 models, in the 39th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Claude Haiku 4.5 Nano good at GPQA Diamond?

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 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 Claude Haiku 4.5 Nano cost?

Claude Haiku 4.5 Nano costs $6.47 per million input tokens and $27.98 per million output tokens.

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