Professional Edge AI Benchmarks
Practical benchmarks for production SLM deployment. Evaluate what matters: function calling, JSON extraction, and intent classification across edge platforms.
SLM-Bench is CycleCore's edge AI evaluation initiative that provides rigorous, transparent benchmarks for Small Language Models.
While academic benchmarks focus on general capabilities, SLM-Bench evaluates what matters for production deployment: function calling, JSON extraction, and intent classification across Raspberry Pi, laptops, and browsers.
The Problem: Existing benchmarks don't reflect real-world edge AI challenges. Academic tasks like MMLU and HellaSwag don't test function calling. No standardized energy measurement. No cross-platform validation.
Our Solution: Practical benchmarks that measure what matters for production. Independent evaluation service. Transparent methodology. Public leaderboard.
Practical tasks for production deployment: EdgeJSON (extraction), EdgeIntent (classification), EdgeFuncCall (tool use). Open-source, reproducible.
Compare 10+ SLMs across benchmark tasks. Free access. Transparent methodology. No pay-to-play rankings.
Get your SLM independently evaluated. Detailed reports, energy measurement, cross-platform testing. $2.5K-$7.5K per model.
Standardized power consumption testing with Joulescope hardware. Joules per task, tokens per joule, cost per 1M tokens.
Evaluate on Raspberry Pi 5, mid-range laptops, and browser (WebGPU). Real-world deployment scenarios.
CycleCore-certified fine-tuned SLMs. SmolLM2, Qwen2.5, Llama 3.2. Compare against production-ready baselines.
Open-source models and research
CycleCore Maaza Series
135M parameter micro language model for edge JSON extraction. Perfect on simple schemas (2-4 fields), deployable on Raspberry Pi and browsers.
Intended Hardware: Raspberry Pi, browsers (WebGPU), mobile devices
CycleCore Maaza Series
360M parameter small language model for high-accuracy JSON extraction. Handles complex nested structures (8+ fields), production-ready for most use cases.
Intended Hardware: Laptops, edge servers, mid-range devices
Baseline Model
500M parameter general-purpose language model from Alibaba Cloud. Tested on EdgeJSON to establish baseline performance for community models.
Intended Hardware: General-purpose CPU/GPU (flexible deployment)
Models available on HuggingFace. See SLM-Bench.com leaderboard for benchmarks and comparisons.
Note: All benchmark results reflect testing on the same hardware for fair comparison. "Intended Hardware" indicates each model's target deployment environment.
1,000+ real-world JSON schemas of diverse complexity. Test schema compliance, field accuracy, and error handling.
Baselines: Qwen2.5-0.5B (evaluated), SmolLM2-1.7B, Qwen2.5-1.5B
50-200 class taxonomy at enterprise scale. Few-shot and zero-shot variants. Measures accuracy, latency, and energy per inference.
Baseline: SmolLM2-360M, Qwen2.5-0.5B
Multi-turn tool use scenarios. Tests parameter extraction accuracy and error recovery with realistic APIs.
Baseline: Llama 3.2 3B, custom distilled models
$20
per model
$79
5 models (21% savings)
$499
per month
Independent evaluation. Transparent methodology. No pay-to-play rankings.
Validate your model's production readiness. Get independent evaluation on practical tasks. Benchmark against leading SLMs.
Make informed SLM procurement decisions. Compare models on tasks that matter for your deployment. Verify vendor claims.
Showcase your platform's AI capabilities. Get independent validation of performance and energy efficiency.
Access open-source benchmark suite. Reproduce results. Contribute to edge AI evaluation methodology.
We publish our methodology. We don't accept payment for rankings. We open-source our benchmark suite.
Evaluation-as-a-Service, not pay-to-play.
Visit SLM-Bench.com to view the leaderboard, explore benchmarks, and request professional evaluation.
Visit SLM-Bench.com →Want to learn more about our evaluation service or benchmark methodology? Get in touch.