Commonsense · DeepSeek

DeepSeek-R1 on HellaSwag: score, rank and what it means

DeepSeek-R1 scores 89.8 on HellaSwag, placing it #90 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
89.8
0–100 scale
Rank
#90
of 600
Percentile
85th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
6.3
points

Score distribution

The chart shows the top 10 models on HellaSwag, with DeepSeek-R1 highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.1
GPT-5.2 Turbo 95.5
GPT-5.2 Pro 95.3
Claude Opus 4.5 Pro 95.2
Claude Sonnet 4.5 Reasoning 95.2
Grok 4 Reasoning 95.2
GPT-5.2 Vision 94.6
Gemini 3 Pro Turbo 94.6
GPT-5.1 Reasoning 94.4
o4 Pro 94.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
8 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
9 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
10 o4 Pro OpenAI 1000000 $19.37 87.3

What this score means

On HellaSwag, DeepSeek-R1 sits at #90 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 6.3 points. The median model in this index scores 83.6, which puts DeepSeek-R1 above the midpoint.

DeepSeek models average 82.7 on this benchmark. DeepSeek-R1 is 7.1 points above that provider line, so it is a stronger result than the average DeepSeek model.

Among multimodal models, the average is 86.8. DeepSeek-R1 is 3.0 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 89.8 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 $5.83 per million input tokens, DeepSeek-R1 is above the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
GPT-4.1 Turbo OpenAI 1000000 $16.12 82.9
GLM-4.6 Chat Zhipu AI 400000 $4.89 79.8
GPT-5.2 Mini OpenAI 200000 $13.34 80.4
Sonar Pro Reasoning Perplexity 128000 $13.94 80.5
DeepSeek-R1 DeepSeek 128000 $5.83 82.8
Gemini 3 Flash Google DeepMind 128000 $15.67 83.6
Claude 3.7 Sonnet Pro Anthropic 128000 $16.07 83.3

Other benchmarks for DeepSeek-R1

BenchmarkScoreRankPercentileRelative
MMLU-Pro 83.8 85 86th
GPQA Diamond 77.8 89 85th
SWE-bench Verified 72.2 73 88th
HumanEval 86.4 97 84th
MATH-500 89.1 55 91th
AIME 2025 81.1 72 88th
LiveCodeBench 77.4 70 88th
IFEval 91.0 53 91th
TruthfulQA 82.9 107 82th
MMMU 79.5 66 89th
Arena Elo 1454.1 65 89th

FAQ

What score does DeepSeek-R1 get on HellaSwag?

DeepSeek-R1 scores 89.8 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does DeepSeek-R1 rank on HellaSwag?

It ranks #90 of 600 models, in the 85th percentile. The leader is GPT-5.2 Max at 96.1.

Is DeepSeek-R1 good at HellaSwag?

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 HellaSwag score calculated?

HellaSwag is a commonsense 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 DeepSeek-R1 cost?

DeepSeek-R1 costs $5.83 per million input tokens and $25.23 per million output tokens.

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