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Pricing 101: Token Math & Cost-Per-Completion ExplainedLatest article
Published on 2026.01.13 by DeepInfraPricing 101: Token Math & Cost-Per-Completion Explained

LLM pricing can feel opaque until you translate it into a few simple numbers: input tokens, output tokens, and price per million. Every request you send—system prompt, chat history, RAG context, tool-call JSON—counts as input; everything the model writes back counts as output. Once you know those two counts, the cost of a completion is […]

Recent articles
From Precision to Quantization: A Practical Guide to Faster, Cheaper LLMsPublished on 2026.01.13 by DeepInfraFrom Precision to Quantization: A Practical Guide to Faster, Cheaper LLMs

Large language models live and die by numbers—literally trillions of them. How finely we store those numbers (their precision) determines how much memory a model needs, how fast it runs, and sometimes how good its answers are. This article walks from the basics to the deep end: we’ll start with how computers even store a […]

Nemotron 3 Nano vs GPT-OSS-20B: Performance, Benchmarks & DeepInfra ResultsPublished on 2026.01.13 by DeepInfraNemotron 3 Nano vs GPT-OSS-20B: Performance, Benchmarks & DeepInfra Results

The open-source LLM landscape is becoming increasingly diverse, with models optimized for reasoning, throughput, cost-efficiency, and real-world agentic applications. Two models that stand out in this new generation are NVIDIA’s Nemotron 3 Nano and OpenAI’s GPT-OSS-20B, both of which offer strong performance while remaining openly available and deployable across cloud and edge systems. Although both […]

Nemotron 3 Nano Explained: NVIDIA’s Efficient Small LLM and Why It MattersPublished on 2026.01.13 by DeepInfraNemotron 3 Nano Explained: NVIDIA’s Efficient Small LLM and Why It Matters

The open-source LLM space has exploded with models competing across size, efficiency, and reasoning capability. But while frontier models dominate headlines with enormous parameter counts, a different category has quietly become essential for real-world deployment: small yet high-performance models optimized for edge devices, private on-prem systems, and cost-sensitive applications. NVIDIA’s Nemotron family brings together open […]

GLM-4.6 vs DeepSeek-V3.2: Performance, Benchmarks & DeepInfra ResultsPublished on 2026.01.13 by DeepInfraGLM-4.6 vs DeepSeek-V3.2: Performance, Benchmarks & DeepInfra Results

The open-source LLM ecosystem has evolved rapidly, and two models stand out as leaders in capability, efficiency, and practical usability: GLM-4.6, Zhipu AI’s high-capacity reasoning model with a 200k-token context window, and DeepSeek-V3.2, a sparsely activated Mixture-of-Experts architecture engineered for exceptional performance per dollar. Both models are powerful. Both are versatile. Both are widely adopted […]

LLM API Provider Performance KPIs 101: TTFT, Throughput & End-to-End GoalsPublished on 2026.01.13 by DeepInfraLLM API Provider Performance KPIs 101: TTFT, Throughput & End-to-End Goals

Fast, predictable responses turn a clever demo into a dependable product. If you’re building on an LLM API provider like DeepInfra, three performance ideas will carry you surprisingly far: time-to-first-token (TTFT), throughput, and an explicit end-to-end (E2E) goal that blends speed, reliability, and cost into something users actually feel. This beginner-friendly guide explains each KPI […]

Build an OCR-Powered PDF Reader & Summarizer with DeepInfra (Kimi K2)Published on 2026.01.13 by DeepInfraBuild an OCR-Powered PDF Reader & Summarizer with DeepInfra (Kimi K2)

This guide walks you from zero to working: you’ll learn what OCR is (and why PDFs can be tricky), how to turn any PDF—including those with screenshots of tables—into text, and how to let an LLM do the heavy lifting to clean OCR noise, reconstruct tables, and summarize the document. We’ll use DeepInfra’s OpenAI-compatible API […]