AI Benchmark Advisor — Select Trusted Benchmarks for Your Model

Suite Selection Advisor

Benchmark Advisor

Select your target capability, evaluation priority, and model tier to find calibrated, non-saturated benchmark suites.

1Capability Domain

2Evaluation Priority

3Model Scale Under Test

Recommended Suites for Coding

Calibrated for Frontier Reasoning with focus on Frontier Headroom.

5 Recommended

#1FrontierCodeActiveFrontier Discriminator

Trust Score

Sufficiently difficult to discriminate frontier reasoning models with test-time compute.

Expected Score50% – 95%

ContaminationLOW

Human Baseline45%

Refresh Cadencestatic

lm_eval --model hf --model_args pretrained=<your_model> --tasks frontiercode --batch_size auto

Benchmark Specs

#2LiveCodeBenchSaturatedFrontier Discriminator

Trust Score

Sufficiently difficult to discriminate frontier reasoning models with test-time compute.

Expected Score50% – 95%

ContaminationMEDIUM

Human Baseline65%

Refresh Cadencecontinuous

lm_eval --model hf --model_args pretrained=<your_model> --tasks --scenario codegeneration --batch_size auto

Benchmark Specs

#3SWE-bench ProActiveFrontier Discriminator

Trust Score

Sufficiently difficult to discriminate frontier reasoning models with test-time compute.

Expected Score50% – 95%

ContaminationMEDIUM

Human Baseline74%

Refresh Cadencestatic

lm_eval --model hf --model_args pretrained=<your_model> --tasks swe_bench_pro_eval.py --batch_size auto

Benchmark Specs

#4SWE-bench VerifiedSaturatedFrontier Discriminator

Trust Score

Sufficiently difficult to discriminate frontier reasoning models with test-time compute.

Expected Score50% – 95%

ContaminationHIGH

Human Baseline78.4%

Refresh Cadencestatic

lm_eval --model hf --model_args pretrained=<your_model> --tasks SWE-bench_Verified --batch_size auto

Benchmark Specs

#5Terminal-BenchActiveRecommended Suite

Trust Score

Active benchmark with solid headroom against top foundation models.

Expected Score70% – 90%

ContaminationHIGH

Human Baseline84%

Refresh Cadencestatic

lm_eval --model hf --model_args pretrained=<your_model> --tasks terminal-bench/terminal-bench-2-1 --batch_size auto

Benchmark Specs