Mathematics · OpenRouter

Auto Turbo on MATH-500: score, rank and what it means

Auto Turbo scores 71.2 on MATH-500, placing it #377 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
71.2
0–100 scale
Rank
#377
of 600
Percentile
37th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
25.5
points

Score distribution

The chart shows the top 10 models on MATH-500, with Auto Turbo 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, Auto Turbo sits at #377 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 25.5 points. The median model in this index scores 76.0, which puts Auto Turbo below the midpoint.

OpenRouter models average 70.2 on this benchmark. Auto Turbo is 1.0 points above that provider line, so it is a stronger result than the average OpenRouter model.

Among mid-range models, the average is 70.0. Auto Turbo is 1.2 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 71.2 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 $7.96 per million input tokens, Auto Turbo is above 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
Phi-3.5 Base Microsoft 65536 $2.24 66.6
Arctic Chat Snowflake 16384 $2.70 69.8
Auto Pro OpenRouter 8192 $8.41 71.2
Firefunction Turbo Fireworks AI 32768 $8.44 70.7
Auto Turbo OpenRouter 65536 $7.96 70.9
Yi-Large Base 01.AI 16384 $2.78 70.3
DBRX Base Databricks 32768 $2.63 69.1

Other benchmarks for Auto Turbo

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.0 305 49th
GPQA Diamond 69.1 291 52th
SWE-bench Verified 57.1 365 39th
HumanEval 75.7 332 45th
AIME 2025 64.8 340 43th
LiveCodeBench 60.2 366 39th
IFEval 80.7 318 47th
TruthfulQA 71.7 383 36th
HellaSwag 83.9 283 53th
MMMU 70.0 301 50th
Arena Elo 1345.5 371 38th

FAQ

What score does Auto Turbo get on MATH-500?

Auto Turbo scores 71.2 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Auto Turbo rank on MATH-500?

It ranks #377 of 600 models, in the 37th percentile. The leader is GPT-5.2 Max at 96.7.

Is Auto Turbo 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 Auto Turbo cost?

Auto Turbo costs $7.96 per million input tokens and $33.26 per million output tokens.

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