text-embedding-3-large

text-embedding-3-large is a large language model from OpenAI, first released on 25 January 2024. It is proprietary (closed-weights) and sits in the embeddings, openai, and enterprise categories of our catalog. OpenAI’s flagship embedding model for RAG, search and clustering. This page covers text-embedding-3-large pricing, benchmarks, API limits, speed, modalities, best use cases, and how it compares with similar models — so you can decide whether it belongs in your stack in 2026.

As a language model, text-embedding-3-large is evaluated on reasoning quality, coding ability, latency, context window size, and dollars-per-million-tokens. The context window is 8k tokens (about 6k words), which determines how much prompt, document, and conversation history you can send in one request. At a typical 3:1 input-to-output mix, the blended API price is about $0.13 per 1M tokens.

Teams usually shortlist text-embedding-3-large when they need a dependable OpenAI option for production chat, agents, retrieval-augmented generation, or coding copilots. Common fits include rag pipelines, semantic search, and clustering. Reviewers consistently call out strong retrieval quality, flexible dimensions, and mature ecosystem as standout strengths. Trade-offs to weigh include proprietary api only and not multimodal. The sections below break down pricing tables, benchmark charts, token limits, input/output modalities, and head-to-head comparisons so long-tail queries — from “text-embedding-3-large API pricing” to “text-embedding-3-large vs text-embedding-3-small” — are answered on this page.

If you are migrating from an older OpenAI model or switching labs entirely, treat this page as a decision brief: skim the overview stats, confirm API pricing fits your volume, check whether the context window covers your longest documents, then validate quality on a golden set of prompts. Benchmarks and charts help shortlist; your own evals decide. We refresh catalog numbers periodically (last update 2026-06) so figures stay useful through the year.

Detailed academic benchmark numbers are not yet published for text-embedding-3-large in our catalog. We still surface intelligence index and qualitative strengths where available — run task-specific evals before migrating production traffic.

text-embedding-3-large offers a 8k-token context window — roughly about 6k words of English text. Everything in a single API call counts against that budget: system instructions, chat history, retrieved documents, tool schemas, and the model’s reply. Exceeding the window truncates or errors depending on the provider.

Large windows help with long PDFs, multi-file code reviews, and multi-hour agent traces, but bigger contexts also cost more tokens and can add latency. Prefer retrieval that stuffs only relevant chunks, summarize old turns, and reserve headroom for the model’s maximum completion length.

Token limits define how much text-embedding-3-large can read and write per request. Total context is capped at 8k tokens. Providers may also enforce organization-level rate limits (RPM/TPM) separate from these per-request caps.

Practical tip: set max_tokens intentionally. Leaving it unbounded wastes budget on verbose answers; setting it too low truncates JSON or code. For structured outputs, prefer schemas/tool calls and keep completions tight.

The charts on this page visualize text-embedding-3-large against catalog peers. Benchmark bars show academic scores versus the current leader; the similar-models comparison table plots intelligence, speed, and blended price so you can see trade-offs at a glance. Use them to answer “is text-embedding-3-large fast enough?” and “is the quality jump worth the premium?” without opening a spreadsheet.

Best use cases for text-embedding-3-large follow from its strengths, price point, and modality support. Match the model to the job: frontier reasoning for hard planning, fast/cheap tiers for classification, image/video/speech specialists for media pipelines.

Based on catalog notes, text-embedding-3-large is a particularly strong fit for rag pipelines, semantic search, and clustering. Validate with a short bake-off on your real prompts before a full cutover.

Anti-patterns: do not use a frontier-priced model like a generic classifier if a smaller model scores within a point on your eval; do not stuff entire corpora into context when retrieval would be cheaper; and do not skip structured outputs if you plan to parse text-embedding-3-large responses in code.