Commonsense · Stability AI

StableLM 2 on HellaSwag: score, rank and what it means

StableLM 2 scores 77.2 on HellaSwag, placing it #413 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
77.2
0–100 scale
Rank
#413
of 600
Percentile
31th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
18.9
points

Score distribution

The chart shows the top 10 models on HellaSwag, with StableLM 2 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, StableLM 2 sits at #413 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 18.9 points. The median model in this index scores 83.6, which puts StableLM 2 below the midpoint.

Stability AI models average 71.3 on this benchmark. StableLM 2 is 5.9 points above that provider line, so it is a stronger result than the average Stability AI model.

Among open-weight models, the average is 80.2. StableLM 2 is 3.0 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 77.2 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 $1.91 per million input tokens, StableLM 2 is below 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
DeepSeek-V3.2 8B DeepSeek 32768 $2.02 66.2
ERNIE 4.5 Nano Baidu 32768 $6.01 65.6
DeepSeek-R1 7B DeepSeek 32768 $1.79 63.0
Firesearch Mini Fireworks AI 32768 $3.94 61.5
StableLM 2 Stability AI 8192 $1.91 65.0
Command A Nano Cohere 8192 $5.90 66.3
Embed 4 Turbo Cohere 4096 $5.13 64.0

Other benchmarks for StableLM 2

BenchmarkScoreRankPercentileRelative
MMLU-Pro 71.4 387 36th
GPQA Diamond 57.9 429 29th
SWE-bench Verified 52.0 407 32th
HumanEval 70.0 394 34th
MATH-500 68.3 397 34th
AIME 2025 56.5 408 32th
LiveCodeBench 55.2 402 33th
IFEval 73.2 413 31th
TruthfulQA 68.7 408 32th
MMMU 64.2 387 36th
Arena Elo 1294.3 426 29th

FAQ

What score does StableLM 2 get on HellaSwag?

StableLM 2 scores 77.2 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does StableLM 2 rank on HellaSwag?

It ranks #413 of 600 models, in the 31st percentile. The leader is GPT-5.2 Max at 96.1.

Is StableLM 2 good at HellaSwag?

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 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 StableLM 2 cost?

StableLM 2 costs $1.91 per million input tokens and $9.65 per million output tokens.

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