Mathematics · Stability AI

StableLM 2 Instruct on MATH-500: score, rank and what it means

StableLM 2 Instruct scores 67.7 on MATH-500, placing it #403 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
67.7
0–100 scale
Rank
#403
of 600
Percentile
33th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
29.0
points

Score distribution

The chart shows the top 10 models on MATH-500, with StableLM 2 Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, StableLM 2 Instruct sits at #403 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 29.0 points. The median model in this index scores 76.0, which puts StableLM 2 Instruct below the midpoint.

Stability AI models average 55.6 on this benchmark. StableLM 2 Instruct is 12.1 points above that provider line, so it is a stronger result than the average Stability AI model.

Among open-weight models, the average is 70.8. StableLM 2 Instruct is 3.1 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 67.7 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 $1.93 per million input tokens, StableLM 2 Instruct is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
ERNIE 4.5 Nano Baidu 32768 $6.01 65.6
SmolLM3 Hugging Face 8192 $1.71 63.1
StableLM 2 Chat Stability AI 8192 $1.79 64.6
Nemotron Nano Chat NVIDIA 32768 $2.21 66.2
StableLM 2 Instruct Stability AI 16384 $1.93 65.2
Nemotron Nano Instruct NVIDIA 32768 $2.33 67.4
Nova Lite Mini Amazon 16384 $5.43 65.0

Other benchmarks for StableLM 2 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 69.9 396 34th
GPQA Diamond 62.2 389 35th
SWE-bench Verified 54.5 389 35th
HumanEval 69.5 404 33th
AIME 2025 53.9 425 29th
LiveCodeBench 55.9 398 34th
IFEval 72.9 418 30th
TruthfulQA 70.0 400 33th
HellaSwag 77.5 408 32th
MMMU 63.0 399 34th
Arena Elo 1301.8 415 31th

FAQ

What score does StableLM 2 Instruct get on MATH-500?

StableLM 2 Instruct scores 67.7 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does StableLM 2 Instruct rank on MATH-500?

It ranks #403 of 600 models, in the 33rd percentile. The leader is GPT-5.2 Max at 96.7.

Is StableLM 2 Instruct good at MATH-500?

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 MATH-500 score calculated?

MATH-500 is a mathematics 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 Instruct cost?

StableLM 2 Instruct costs $1.93 per million input tokens and $8.75 per million output tokens.

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