Coding · OpenRouter

Auto Pro on LiveCodeBench: score, rank and what it means

Auto Pro scores 62.7 on LiveCodeBench, placing it #337 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
62.7
0–100 scale
Rank
#337
of 600
Percentile
44th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
22.4
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Auto Pro highlighted. Longer bars mean higher scores.

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, Auto Pro sits at #337 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 22.4 points. The median model in this index scores 64.7, which puts Auto Pro below the midpoint.

OpenRouter models average 58.5 on this benchmark. Auto Pro is 4.2 points above that provider line, so it is a stronger result than the average OpenRouter model.

Among mid-range models, the average is 58.3. Auto Pro is 4.4 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 62.7 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 $8.41 per million input tokens, Auto Pro is above the median input price of $4.00 across the 600 models scored on LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Firefunction Turbo Fireworks AI 32768 $8.44 70.7
Fusion Max OpenRouter 65536 $9.14 72.5
Llama 3 Baseten Baseten 128000 $3.62 74.9
ERNIE 4.5 Mini Baidu 32768 $8.62 71.0
Auto Pro OpenRouter 8192 $8.41 71.2
Yi-Large 01.AI 32768 $3.40 73.5
Yi-Large Chat 01.AI 65536 $3.49 73.5

Other benchmarks for Auto Pro

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.6 287 52th
GPQA Diamond 68.8 300 50th
SWE-bench Verified 60.5 316 47th
HumanEval 76.7 313 48th
MATH-500 71.4 375 38th
AIME 2025 65.5 335 44th
IFEval 80.7 317 47th
TruthfulQA 72.7 373 38th
HellaSwag 81.6 356 41th
MMMU 66.3 366 39th
Arena Elo 1356.0 353 41th

FAQ

What score does Auto Pro get on LiveCodeBench?

Auto Pro scores 62.7 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Auto Pro rank on LiveCodeBench?

It ranks #337 of 600 models, in the 44th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Auto Pro good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench is a coding 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 Pro cost?

Auto Pro costs $8.41 per million input tokens and $45.80 per million output tokens.

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