Gemini 2.5 Flash tops the TaskBench rankings, but day-to-day work isn’t decided by a single chart. The buzz around TaskBench highlights a wider debate: do task-completion scores predict real use? [1]
On the latest run, Gemini 2.5 Flash took the top overall task completion across TaskBench, boasting wins in context reasoning, SQL, agents, and normalization [1]. Yet critics say tiny deltas—like a 0.1% edge on 120 tests—are noise, not a real reorder. There’s also chatter about instability across days, since outputs depend on next-token predictions [1]. Determinism helps: seeds can make results repeatable and model signatures track exact checkpoints [1].
OCR and data-extraction live in a separate debate. While Gemini 2.5 Pro is described as solid for OCR, the scale brings API costs into focus. A hybrid/local approach often wins: preprocessing receipts, then feeding a vision-language model with tools like Docstrange and granite-docling by default in newer stacks, plus synthetic data from Donut [2]. The latest Flash Lite checkpoint and batch pricing add further nuance [2].
In practice, folks actually use a mix: • qwen3-coder-30b for coding in Python/JS/TS [3] • qwen3-vl-30b and qwen2.5-vl-32b for OCR/document understanding [3] • gpt-oss-120b for everything else [3] • Inference via llama.cpp; some run with LM Studio or Ollama for local hosting [3]
Bottom line: benchmarks are a guide, but real tasks demand local hosting options, cost awareness, and smarter OCR prompts. [1][3]
References
LLM Benchmarks: Gemini 2.5 Flash latest version takes the top spot
Gemini 2.5 Flash leads TaskBench; debate on rankings, stability, OS options, local hosting, update versions, and benchmark scope across models
View sourceIs Gemini 2.5 Pro still the best LLM for OCR and data extraction?
Discussion of LLMs for OCR on noisy receipts; hybrid/local workflows; comparisons and prompts to curb hallucinations and cost considerations.
View sourceWhat models do you find yourself actually using, and what for?
Users compare local LLMs (GPT-OSS, Qwen3, Gemma, GLM) across hardware, RAM/VRAM, speeds, usage, and quantization strategies, drivers, tooling, costs, limits.
View source