5 posts
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.
An enterprise decision matrix between self-hosted LLM and API: ~500M tokens/day break-even, H100/H200/B200 GPU cost, quantization impact, KVKK + BDDK + ITAR/EAR constraints, AI sovereignty strategy, and three anonymized Turkish sector cases (banking, healthcare, SMB) on hybrid architecture. 2026 reference guide for Turkish enterprises.
A 2026 snapshot of the Turkish open-source LLM ecosystem: Trendyol-LLM, Cosmos-Llama, KanarYa, Kumru AI, the TÜBİTAK BİLGEM domestic model, and the T3 AI Baykar defense model. Detailed decision guide covering MMLU-TR and TUMLU benchmarks, licensing, tokenization gap, VRAM requirements, self-hosting needs, and which model to pick for which use case.
Detailed comparison of the three most powerful 2026 open-weight LLM families — DeepSeek (V3 + R1), Qwen (2.5 + 3), and Meta Llama (4). Architecture (MoE vs dense), benchmarks (MMLU, HumanEval, GSM8K), Turkish performance, license (MIT vs Apache vs Llama Community), cost (self-hosted vs API), hardware (VRAM, GPU), fine-tune friendliness, ecosystem (Hugging Face, vLLM, Ollama), KVKK / data sovereignty advantages. Use cases for Turkish enterprises.
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.