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text-davinci-002

text-davinci-002

text-davinci-002 · Family: GPT-3.5
OpenAI GPT-3.5-series language model, instruction-tuned via supervised fine-tuning (SFT). Launched 2022, shut down on 4 January 2024.
✕ DiscontinuedLLM📁 GPT-3.5
Context window
4K (4 097 tokenów)
tokens
Release date
15 March 2022
Access:APIDeployment:☁ Cloud

Overview

text-davinci-002 is an OpenAI language model belonging to the GPT-3.5 series. It is an instruction-tuned variant from the GPT-3/InstructGPT family, built on top of code-davinci-002 and trained with supervised fine-tuning (the so-called FeedME method) rather than reinforcement learning from human feedback (RLHF).

The model was released on 15 March 2022 alongside code-davinci-002, introducing text edit and insert capabilities. It supported a 4,097-token context window, and its training data extended up to June 2021.

text-davinci-002 became well known in research on reasoning in language models — Kojima et al. (2022) used it to demonstrate the Zero-shot Chain-of-Thought technique, raising accuracy on the GSM8K benchmark from 10.4% to 40.7% simply by appending the phrase "Let's think step by step" to the prompt.

OpenAI announced the retirement of older GPT-3 and GPT-3.5 models on 6 July 2023; text-davinci-002 was finally shut down on 4 January 2024. The recommended successor is gpt-3.5-turbo-instruct.

Classification
LLM
Family: GPT-3.5
Access & deployment
API
Cloud
Weights: Closed
Key parameters
📏 Context: 4K (4 097 tokenów)
📥 Input: text

Technical specification

Context window
4K (4 097 tokenów)
tokens
Knowledge cutoff
1 Jun 2021
Knowledge boundary
License
Proprietary (OpenAI API, zamknięty / closed)
Hardware requirements
Available only via the OpenAI API (cloud). Closed weights — no local/self-hosted deployment.
Modalities
⬇ Input
text
⬆ Output
textcode

Capabilities and applications

Native model capabilities
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Zero-shot learning
The model's ability to perform a new task without dataset-specific training or hyperparameter tuning — prediction is produced in a single pass from context.
Category: other
Few-shot learning
The model's ability to perform a new task from a handful of examples provided directly in the prompt, without any weight updates or fine-tuning.
Category: other
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning

Benchmark results

1 benchmark
GSM8K
accuracy · zero-shot chain-of-thought (Zero-shot-CoT)
40.7%%
📅 24 May 2022📄 paper
Zero-shot-CoT reached 40.7% vs 10.4% for plain zero-shot (Kojima et al., 2022).

Pricing

Technical architecture

Core Architecture
Model Form
Training Techniques