4 posts
How to read LLM benchmark scores? A practical guide to reading model comparisons with an eye on data contamination, real performance, and the evaluation limit.
MMLU, HumanEval, SWE-bench Verified/Pro, ARC-AGI-2, GPQA Diamond, AIME, LiveCodeBench v6, Terminal-Bench 2.0, OSWorld, HLE, plus Turkish benchmarks (TR-MMLU, TUMLU) — what each one measures, the frontier thresholds, contamination and cherry-picking risks, and practical meaning for CTOs, investors, and engineers. 32+ references.
We benchmarked GPT-5.5, Claude Opus 4.7 and Gemini 3.1 Pro on Turkish workloads end to end: TR-MMLU and TUMLU benchmark numbers, a 50-prompt real-world test across legal, finance, code, creative writing and Q&A, an A/B in a Turkish enterprise, TL-based cost analysis and a decision matrix for picking the right model for each Turkish task. 35+ references.
The most comprehensive 2026 Turkish LLM benchmark: MMLU-TR, Belebele-TR, TruthfulQA-TR, Turkish HumanEval, MGSM-TR, and hallucination tests. Score tables for GPT-5, Claude Opus 4.7, Gemini 3, Mistral Large 3, Llama 4, DeepSeek V3, Qwen 2.5, and local Turkish models (Cezeri, BERTurk, Trendyol-LLM), with use-case mapping and transparent methodology.