OpenAI's most powerful reasoning model, pushing the frontier across coding, math, science, and visual perception. Trained to think longer before responding and agentically use tools (web search, code execution, image generation) to solve complex problems. Sets new SOTA on benchmarks like Codeforces and MMMU.

A smaller, cost-efficient reasoning model from OpenAI optimized for speed. Achieves remarkable performance for its size, particularly in math, coding, and visual tasks. Supports significantly higher usage limits than o3 and can agentically use tools.

OpenAI o4-mini-high is the same model as o4-mini but defaults to a high reasoning effort setting. It's a compact reasoning model optimized for speed and cost-efficiency, retaining strong multimodal and agentic capabilities, especially in math, coding, and visual tasks.

DALL-E 3 auto-improves user inputs via ChatGPT integration and blocks prohibited content with 99.9% precision using multimodal classifiers.

OpenAI's second-generation image model. Near-perfect typography (including non-Latin scripts), 2x faster than gpt-image-1.5, up to 2K resolution, and 'thinking' over the prompt for dense compositions.

OpenAI's latest image generation model with strong instruction following, optional transparent backgrounds, and quality controls.

GPT Image 1.5 with `quality=low` for faster and cheaper generations.

GPT Image 1.5 with `quality=medium` for balanced cost and quality.

GPT Image 1.5 with `quality=high` for maximum fidelity.

GPT-4o processes text, images, and audio through a unified transformer architecture and offers real-time translation for 154 languages with 89.2% BLEU score on low-resource languages.

OpenAI's GPT-4.1, built around a 1 million token context window and precise code diffs. Scores 54.6% on SWE-bench Verified and 87.4% on IFEval, and beats GPT-4o on coding, instruction compliance and long-document recall.

o3 Mini is a smaller, more efficient version of the o3 model, optimized for faster response times and lower computational costs while maintaining high-quality outputs.

o1 achieves 86% accuracy on Mathematics Olympiad benchmarks (vs. GPT-4o's 13%), offers PhD-level STEM proficiency, and is built with extensive alignment training and safety evaluation.

GPT-4.5 is a step forward in scaling up pre-training and post-training. With broader knowledge, improved intent understanding, and greater 'EQ', it excels at natural conversations, writing, programming, and practical problem solving with reduced hallucinations. GPT-4.5 achieved 62.5% accuracy on SimpleQA and a 37.1% hallucination rate, significantly outperforming GPT-4o and other models.

The March 2025 refresh of GPT-4o in ChatGPT. Follows instructions more closely, handles coding with fewer detours, and writes shorter answers with fewer nested markdown levels than the previous build.

GPT-4o mini is OpenAI's newest model after GPT-4 Omni, supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than GPT-3.5 Turbo. It maintains SOTA intelligence, while being significantly more cost-effective.

The smallest and cheapest model in the GPT-4.1 line, with the same 1 million token context. Scores 80.1% on MMLU, 50.3% on GPQA and 9.8% on Aider polyglot, above GPT-4o mini. Aimed at classification and autocompletion.

The mid tier of the GPT-4.1 line, roughly GPT-4o quality at lower latency and cost, with the same 1 million token context. Scores 84.1% on IFEval, 35.8% on MultiChallenge and 31.6% on Aider's polyglot diff benchmark, and handles vision.

GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.

The original GPT-4: a large multimodal model that handled hard problems more accurately than GPT-3.5 on the strength of broader general knowledge and better reasoning. Training data stops at September 2021.

A direct scale-up of GPT-1 with 1.5 billion parameters, trained on 8 million web pages. Known for its ability to generate coherent text, sometimes indistinguishable from humans, but could be repetitive.

The first large-scale transformer-based language model released by OpenAI, trained on the BooksCorpus dataset. This version is accessed via the Hugging Face model hub (`openai-community/openai-gpt`).

codex-mini-latest is a fine-tuned version of o4-mini specifically for use in Codex CLI. For direct use in the API, we recommend starting with gpt-4.1.

OpenAI's smaller open-weight release, 21B parameters on a mixture-of-experts design. Matches o3-mini on quality while fitting in 16GB of memory, which puts it on consumer hardware and edge devices.

OpenAI's first open-weight model since GPT-2, 117B parameters on a mixture-of-experts design. Reasoning depth is configurable per request and tool use is native, aimed at agent and production workloads.

OpenAI's GPT-5, built around step-by-step reasoning and instruction following on high-stakes work. It routes at test time and reads intent cues like "think hard about this". Hallucination and sycophancy are down against GPT-4o, with better coding, writing and health answers.

The Pro tier of GPT-5, given more test-time compute for complex, high-stakes tasks. Same routing controls and intent cues as GPT-5, with lower hallucination and sycophancy and stronger coding, writing and health answers.

A compact GPT-5 for lighter reasoning, with the same instruction following and safety tuning at lower latency and cost. It is the successor to OpenAI's o4-mini.

The smallest model in OpenAI's GPT-5 line, priced for high-volume classification, extraction and short code edits rather than hard reasoning. Text and image input, training data through May 2024.

GPT-5 retuned for Codex, OpenAI's agentic coding tool. It runs on the Responses API only, not chat completions, and its training data stops in September 2024.

GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning to allocate computation dynamically, responding quickly to simple queries while spending more depth on complex tasks.

GPT-5.1 Chat (AKA Instant) is the fast, lightweight member of the 5.1 family, optimized for low-latency chat while retaining strong general intelligence. It uses adaptive reasoning to selectively think on harder queries, improving accuracy on math, coding, and multi-step tasks without slowing down typical conversations.

GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex, optimized for coding tasks with lower latency while maintaining strong code generation capabilities.

GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks including building projects from scratch, feature development, debugging, large-scale refactoring, and code review.

The largest Codex tier in OpenAI's GPT-5.1 line, built for coding agents that run for hours rather than turns. Training data stops in September 2024.

The fast, lightweight member of the GPT-5.2 family, built for low-latency chat. Adaptive reasoning lets it think only on the harder queries, so mathematics, coding and multi-step questions get more accurate without slowing ordinary conversation. Warmer and more conversational by default than the base model.

The GPT-5.2 base model, with better agentic and long-context behaviour than GPT-5.1. Adaptive reasoning allocates compute per query, answering simple things quickly and spending depth on hard ones. Gains show up across mathematics, coding, science and tool calling, with more coherent long-form answers.

The Pro tier of GPT-5.2, aimed at agentic coding and long-context work beyond what GPT-5 Pro handled. Test-time routing and intent cues like "think hard about this" carry over, with lower hallucination and sycophancy than the previous Pro.

OpenAI's agentic coding model at the 5.3 mark, pairing GPT-5.2-Codex software engineering with GPT-5.2's broader knowledge. Leads SWE-Bench Pro and scores well on Terminal-Bench 2.0 and OSWorld-Verified, reflecting better multi-language coding and terminal work. Built for long tool-using runs, and steerable mid-execution.

OpenAI's high-definition text-to-speech model with six built-in voices. Clear, natural speech optimized for quality over latency.

OpenAI's standard text-to-speech model optimized for low latency. Supports 6 voices and multiple output formats. Offers faster generation than TTS-1-HD with slightly lower quality.

OpenAI's first generally available audio model. Features an upgraded decoder for more natural sounding voices and maintains better voice consistency. Supports both text and audio input/output.

Cost-efficient version of GPT Audio. Features an upgraded decoder for more natural sounding voices and better voice consistency at a fraction of the cost.

GPT-4o with audio input support. Detects nuances within audio recordings and adds depth to generated user experiences. Processes both text and audio prompts.

An update to the model most ChatGPT conversations run on. It answers with better context, refuses less often, and drops the caveats and hedging that used to interrupt a conversation. More willing to engage with nuanced topics, its prose reads cleaner, and reasoning, coding and creative work hold up.

OpenAI's GPT-5.4, folding the Codex and GPT lines into one system. Over 1M tokens of context, 922K in and 128K out, with text and image input. The improvements land in coding, document understanding, tool use and instruction following.

The Pro tier of GPT-5.4, with more reasoning compute for high-stakes work. Same unified Codex and GPT architecture, over 1M tokens of context at 922K in and 128K out, text and image input. Aimed at agentic coding, long-context workflows and multi-step problems.

GPT-5.4 at a smaller size, for high-throughput work. Text and image input, with reasoning, coding and tool use intact at lower latency and cost. The intended fit is chat products, coding assistants and agent workflows running at scale.

The smallest and cheapest model in the GPT-5.4 family, for speed-critical, high-volume work. Text and image input. It trades reasoning depth for responsiveness, which suits classification, data extraction, ranking and sub-agent calls.

GPT-5.5 is OpenAI's frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token context window (922K input, 128K output) with support for text and image inputs, enabling large-scale reasoning, coding, and multimodal workflows within a single system.

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series, suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, with capable reasoning for its price tier.

GPT-5.6 Terra is a balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It suits everyday coding, reasoning, and agentic tasks where capability and cost need to be balanced, at roughly half the cost of Sol.

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series, suited for complex reasoning, coding, and agentic workflows. It is particularly strong at command-line and multi-step coding tasks and long-horizon problem solving.

GPT-5.6 Luna Pro is the same underlying model as GPT-5.6 Luna, served with reasoning mode set to pro for higher-quality responses on complex tasks.

Google's Nano Banana (Gemini 2.5 Flash Image) is an image generation and editing model, optimized for fast, high-quality results. Tested on LMArena as 'nano-banana', it quickly outperformed Midjourney and Flux in blind tests.

Google's Gemini 3 Pro Image model, sold as Nano Banana Pro. Generates and edits at up to 4K and routes prompts through Gemini's thinking mode for text rendering and multi-step instructions.

Gemini 3.1 Flash Image Preview, sold as Nano Banana 2. Google's image generation and editing model at Flash speed and Flash pricing, aimed at iterative edits rather than one-shot renders.

Gemini 3.1 Flash Lite Image, the cheapest and fastest model in Google's Nano Banana line. Text-to-image in roughly 4 seconds, about 2.7x faster than Gemini 3.1 Flash Image, with the same character consistency and editing behaviour. One API covers text-to-image, editing and multi-image composition, at 1K resolution across 14 aspect ratios with an invisible SynthID watermark.

Google's high-quality text-to-image model focused on lighting, detail, and strong visual composition.

A faster and cheaper Imagen 3 variant for when speed and cost matter more than maximum quality.

Imagen 4 Ultra prioritizes maximum quality over speed and cost for best-in-class generations.

Imagen 4 Fast trades a bit of quality for significantly improved speed and cost.

Gemini 2.5 Flash variant tuned for fast text-to-image generation and simple image edits.

Gemini 1.5 Pro handles infinite context with 99% retrieval accuracy at 750k tokens via Mixture-of-Experts and generates chapter summaries for 2-hour videos with 92% accuracy.

Gemini 2.5 Pro Experimental is Google's advanced model with improved multimodal reasoning, long context understanding with 1 million tokens, and specialized video comprehension.

Gemini 2.0 Pro builds interactive 3D environments from text descriptions and offers hypothetical reasoning for scientific simulations.

An experimental Gemini 2.0 Flash variant that reasons through a problem before answering. Trades some latency for accuracy on multi-step questions while staying at Flash speed.

Google's Flash tier workhorse, with reasoning that can be switched on or off and a configurable thinking budget. The point is trading quality against cost and latency per request rather than per model.

Gemini 2.5 Flash with thinking left on, so it reasons through a request before answering. The same workhorse model as the non-thinking variant, priced higher on output because thinking tokens are billed.

PaLM 2 by Google features improved multilingual, reasoning, and coding capabilities. Optimized for chat-based interactions.

Google's flagship multimodal model (as of release). Designed for natural language tasks, multi-turn chat, code generation, and understanding image inputs.

Our most advanced reasoning model, capable of solving complex problems. Best for multimodal understanding, reasoning over complex problems, complex prompts, tackling multi-step code, math and STEM problems, coding (especially web development), and analyzing large datasets/codebases/documents with long context. Knowledge cutoff Jan 2025.

The May 20 checkpoint of Gemini 2.5 Flash in its non-thinking form: it will not emit reasoning tokens, and is billed at the lower output rate. The paired :thinking variant is the one that reasons before answering.

The May 20 checkpoint of Gemini 2.5 Flash with thinking enabled. It reasons before answering, is billed at the higher output rate, and takes a max tokens for reasoning parameter to cap how long it deliberates.

Google's Gemma 3n at the E4B size, built to run on phones, laptops and tablets. Takes text, image and audio input for tasks like transcription, translation and image analysis. Per-Layer Embedding caching and the MatFormer architecture let it activate only part of the model per request, which is what keeps memory use low. 32K context, trained across more than 140 languages.

Google's Gemini 2.5 Pro, a reasoning model that thinks through a request before answering. Aimed at coding, mathematics and scientific work, and it held first place on the LMArena leaderboard at release.

The lightweight tier of Gemini 2.5, tuned for low latency and cost rather than depth. Thinking is off by default to keep it fast; developers can switch it on through the Reasoning API parameter and pay for the extra intelligence.

Google DeepMind's Gemma 3n at an effective 2B parameters inside a 6B architecture. The MatFormer design lets it nest submodels and recombine them through Mix-and-Match, which is how it runs on low-resource hardware. 32K context, multilingual.

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 96000 tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 12B is the second largest in the family after Gemma 3 27B.

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 131072 tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2.

The September 2025 checkpoint of Gemini 2.5 Flash, Google's workhorse tier for reasoning, coding, mathematics and science. Thinking is built in, and the max tokens for reasoning parameter caps how long it deliberates.

The September 2025 checkpoint of Gemini 2.5 Flash-Lite, the cheapest and lowest latency tier in the family. Thinking is off by default; turning it on through the Reasoning API parameter trades cost for intelligence.

Google's Flash tier of Gemini 3, aimed at agent loops, multi-turn chat and coding assistance at much lower latency than Pro. 1M token context, with text, image, audio, video and PDF input and text output. Thinking level is selectable across minimal, low, medium and high, alongside structured output, tool use and automatic context caching.

Gemini 3 Pro Preview with high reasoning effort enabled. Exposes full chain-of-thought process for enhanced transparency in complex problem-solving across text, code, and multimodal tasks.

The 3.1 update to Gemini Pro, with better software engineering scores, more reliable agent behaviour and fewer tokens spent getting there. Reasoning spans text, image, video, audio and code across a 1M token context. Adds a medium thinking level between the existing settings, and improves on structured work like finance and spreadsheets.

Google's Imagen 4 flagship text-to-image model with excellent quality, detail, and prompt adherence.

Google DeepMind's production music generation model available via Vertex AI. Generates 32.8-second instrumental clips at 48kHz from text prompts. Supports negative prompts to exclude unwanted elements and deterministic generation via seed parameter. All output is SynthID-watermarked.

Google DeepMind's most advanced music generation model, launched in the Gemini app. First Lyria model with full vocal generation, automatic lyric creation, and multimodal inputs (text, image, video). Produces 30-second tracks with distinct verses, choruses, and bridges. Supports 8 languages for vocal generation. No public API yet, available through the Gemini app and YouTube Dream Track.

Google's high-efficiency tier at the 3.1 mark, for high-volume work. Beats Gemini 2.5 Flash Lite on quality and gets close to 2.5 Flash, with the gains concentrated in audio and speech recognition, RAG snippet ranking, translation, data extraction and code completion. 2.5x faster to first token and 45% faster output than 2.5 Flash, at half the price of Gemini 3 Flash.

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function calling, and multilingual support across 140+ languages. Strong on coding, reasoning, and document understanding tasks. Apache 2.0 license.

An instruction-tuned mixture-of-experts model from Google DeepMind: 25.2B total parameters but only 3.8B active per token, which is how it reaches near-31B quality on a fraction of the compute. Takes text, images and video up to 60 seconds at 1fps, with a 256K context window, native function calling, a configurable thinking mode and structured output. Apache 2.0.

The general availability release of Gemini 3.1 Flash Lite, Google's cheapest multimodal tier. Text, image, video, audio and PDF input, for light agent work, simple extraction, and anything where latency and API cost decide the design. Thinking levels run from minimal to high. Half the price of Gemini 3 Flash.

Google's Flash tier at the 3.5 mark, close to Pro on coding and reasoning at Flash cost and speed. Tuned for coding and parallel agent loops, taking text, image, video, audio and PDF input. Thinking effort defaults to medium and runs from minimal to high.

Google's Flash workhorse at the 3.6 mark, tuned for coding, agent loops and web and app development. Built on 3.5 Flash, it aims at cleaner output with fewer unnecessary edits and less hedging, spending roughly 17% fewer output tokens and fewer tool calls on the same task. Text, images, audio, video and PDFs across 1M tokens, up to 64K out.

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving, and accepts text, images, audio, video, and files across a 1M-token context window.

Claude 3.7 Sonnet offers Extended Thinking Scaffolds that boost SWE-bench coding accuracy from 62.3% to 70.3%, with 81.2% accuracy in retail automation tasks, outperforming Claude Sonnet 3.6 (2022-10-22) by 13.6%.

Claude 3.7 Thinking Sonnet exposes the full chain-of-thought process during problem-solving, including error backtracking and alternative solution exploration. Scores 86.1% on GPQA Diamond benchmark for expert-level Q&A.

Claude 3.5 Sonnet offers a cost-efficient API ($3/million input tokens vs. $5 for GPT-4o) and uses embedded alignment techniques that reduce harmful outputs by 34% compared to Claude 2.1.

Claude 3 Haiku is Anthropic's fastest and most compact model, designed for near-instant responses in real-time applications with strong multilingual support.

Claude 3 Opus is Anthropic's most powerful model with versatile capabilities ranging from complex reasoning to advanced problem-solving.

Anthropic's Claude 2 model, featuring a large 100K token context window and strong performance on various benchmarks. Known for helpful, honest, and harmless AI conversations.

A temporary research demo version of Claude 3 Sonnet (active for 24 hours on May 23, 2024) specifically engineered by Anthropic to demonstrate feature steering. The model was manipulated to obsessively focus on the Golden Gate Bridge in its responses, showcasing research into model interpretability and safety.

Claude Opus 4 is Anthropic's most powerful model, setting new standards for coding, advanced reasoning, and AI agents. It excels at long-running tasks and complex problem-solving, with capabilities like extended thinking with tool use and improved memory.

Claude Sonnet 4 is a significant upgrade to Sonnet 3.7, delivering superior coding and reasoning while responding more precisely to instructions. It balances performance and efficiency for various use cases.

Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments. Features a 200,000 token context window and excels at conversation, reasoning, code generation, and analysis tasks.

An update to Anthropic's Opus tier, sharpened on coding, reasoning and agent work. Scores 74.5% on SWE-bench Verified, with the clearest gains in multi-file refactors and debugging precision. Extended thinking runs to 64K tokens.

Claude Sonnet 4.5 is Anthropic's most advanced Sonnet model to date, optimized for real-world agents and coding workflows. Stronger agentic capabilities, speculative parallel execution, efficient memory and tool orchestration.

Claude Haiku 4.5 matches Claude Sonnet 4's reasoning and coding strength while staying ultra-responsive for real-time and high-volume workloads. Extended thinking, controllable reasoning depth, and full tool-use support keep sub-agents fast without sacrificing quality.

Anthropic's Opus tier at the 4.5 mark, aimed at long coding sessions and agent work. Text and image input, training data through August 2025, available on the Claude API, Bedrock and Vertex.

Anthropic's strongest model for coding and long-running professional work, built for agents that operate across a whole workflow rather than a single prompt. Handles large codebases, complex refactors and multi-step debugging, and produces near-final documents, plans and analyses in one pass.

Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.

Anthropic's Opus at the 4.7 mark, built for long-running asynchronous agents. Stronger than Opus 4.6 on complex multi-step tasks: large codebases, multi-stage debugging, end-to-end project orchestration. Beyond coding it drafts documents, builds presentations and analyses data, and it holds coherence across very long outputs and extended sessions.

Anthropic's most capable generally available Opus. Text, image and file input, reasoning support, 1M token context. Built for highly autonomous agents and memory-driven work where coherence across a long session is the hard part: multi-step reasoning, complex coding, orchestration across large codebases and multi-stage debugging. It also drafts documents, builds presentations and analyses data.

Anthropic's first publicly available Mythos-class model, built for autonomous knowledge work and coding. Text, image and file input, reasoning support, 1M token context. Aimed at long-running, ambiguous, multi-step tasks that used to need frequent human check-ins, self-correcting through verification loops, with safeguards that fall back to Claude Opus 4.8 on a small fraction of high-risk queries.

Anthropic's most capable Sonnet, with adaptive thinking across selectable effort levels from low to x-high, a 1M token context, and text, image and file input. It ships an updated tokenizer and real-time cyber safeguards that block certain high-risk dual-use requests.

Anthropic's largest model, for demanding reasoning, coding and long-horizon agent work. Strongest on end-to-end software tasks, code review and bug finding, visual analysis of charts and documents, and coordinating parallel subagents. Instruction following and tool use hold steady across long tasks, including at lower effort settings.