Reasoning · Stability AI

StableLM 2 7B on GPQA Diamond: score, rank and what it means

StableLM 2 7B scores 40.7 on GPQA Diamond, placing it #583 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
40.7
0–100 scale
Rank
#583
of 600
Percentile
3th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
46.3
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with StableLM 2 7B 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, StableLM 2 7B sits at #583 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 46.3 points. The median model in this index scores 68.8, which puts StableLM 2 7B below the midpoint.

Stability AI models average 50.6 on this benchmark. StableLM 2 7B is 9.9 points below that provider line, so it is a weaker result than the average Stability AI model.

Among small open-weight models, the average is 49.3. StableLM 2 7B is 8.6 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 40.7 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 $0.28 per million input tokens, StableLM 2 7B 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
GPT-NeoX 8B EleutherAI 16384 $0.25 43.7
Nemotron Nano 7B NVIDIA 8192 $0.35 47.5
Nemotron Nano 8B NVIDIA 8192 $0.46 48.1
OLMo 2 1.5B Allen Institute for AI 16384 $0.28 44.0
StableLM 2 7B Stability AI 8192 $0.28 44.3
StableLM 2 8B Stability AI 32768 $0.38 47.0
Mixtral Groq 1.5B Groq 4096 $0.34 46.1

Other benchmarks for StableLM 2 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 50.2 594 1th
SWE-bench Verified 30.9 578 4th
HumanEval 45.2 589 2th
MATH-500 42.6 588 2th
AIME 2025 28.2 587 2th
LiveCodeBench 28.2 587 2th
IFEval 57.7 585 3th
TruthfulQA 52.7 584 3th
HellaSwag 65.2 582 3th
MMMU 46.0 581 3th
Arena Elo 1155.7 582 3th

FAQ

What score does StableLM 2 7B get on GPQA Diamond?

StableLM 2 7B scores 40.7 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does StableLM 2 7B rank on GPQA Diamond?

It ranks #583 of 600 models, in the 3rd percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is StableLM 2 7B 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 StableLM 2 7B cost?

StableLM 2 7B costs $0.28 per million input tokens and $1.47 per million output tokens.

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