AI Model Comparison
Compare 30+ leading AI models side by side. A sortable, filterable table of current models from OpenAI, Anthropic, Google, xAI, Meta, Mistral, DeepSeek, Alibaba (Qwen), Moonshot (Kimi), Cohere, Amazon, and Zhipu (GLM) — with input/output pricing, context window, max output, speed tier, and capability flags (vision, function calling, JSON mode). Expand any row for strengths and weaknesses, and export the comparison as CSV. Specs verified June 2026.
How to Use This Tool
- Browse all models — scan the table of current models with pricing, context window, max output, speed tier, and capability flags.
- Sort by what matters — click any column header to sort by input or output price, context, max output, or speed (click again to reverse).
- Filter by capability — use the checkboxes to show only models with vision, function calling, JSON mode, or open weights.
- Read the details — hover a column for an explanation, and click Details on any row to see that model's strengths and weaknesses.
- Export for your team — click Export CSV to download the current (filtered, sorted) comparison and share it.
About Comparing AI Models
Choosing an AI model is a trade-off between intelligence, speed, context size, capabilities, and cost — and the right answer changes with every task. A frontier reasoning model is worth its price for hard agentic work, but it is slow and expensive overkill for classifying support tickets. This table puts the current leaders from a dozen providers side by side so you can see those trade-offs at a glance instead of stitching together a dozen pricing pages and model cards.
Read the table in two passes. First, price and capacity: input and output prices are quoted per million tokens, and output almost always costs several times more than input, so a chatty model can dominate your bill. The context window is how much the model can read at once; max output is how much it can write in a single response — a model can have a million-token context yet only return 64K tokens per turn. Second, capabilities: the Vision, Tools, and JSON columns tell you whether a model accepts images, can call your functions, and can be pinned to structured output. The Speed tier is a relative guide to latency, where Slow usually signals a heavier reasoning model that thinks longer for higher quality.
Match the model to the job. For complex reasoning, long-horizon agents, and the hardest coding, the frontier tier — GPT-5.5, Claude Opus 4.8, Gemini 3.1 Pro, and Grok 4.3 — leads. For high-volume, latency-sensitive, or budget-bound work, efficiency models like GPT-5.4 mini, Claude Haiku 4.5, Gemini 3.1 Flash-Lite, and DeepSeek V4-Flash deliver most of the quality at a fraction of the cost. When you need to self-host, fine-tune, or avoid per-token fees entirely, the open-weight options — Llama 4 Scout, Mistral Medium 3.5, DeepSeek, Qwen, Kimi K2, and Zhipu's GLM — are the place to start. A common production pattern is to route the easy 80% of traffic to a cheap model and reserve a frontier model for the hard 20%.
Treat every number here as a snapshot, not gospel. Pricing in particular moves fast: providers cut rates, add tiers, retire models, and offer batch and prompt-caching discounts that can halve the list price. The figures were verified in June 2026 and align with our LLM Cost Calculator, but you should always confirm the live rate on the provider's page before budgeting. And remember that benchmarks and spec sheets only approximate real performance — the only test that counts is your own workload, so shortlist two or three models here, then evaluate them on a handful of your real tasks.
Picking the model is the easy part; running it well in production — multi-model routing, fallback chains, rate-limit and retry architecture, prompt caching, and cost controls — is where reliability and margin are won. Our AI-Powered Marketing team advises on model selection and the architecture around it. Pair this comparison with the LLM Cost Calculator to project spend, the AI Token Counter to size requests, the AI Prompt Builder to craft prompts, and the ChatGPT Prompt Library for 200+ ready-to-use prompts.
Frequently Asked Questions
Which AI model is best for coding?
It depends on the trade-off you want. For the hardest agentic coding and large refactors, frontier models like GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro lead on reasoning and long-horizon tasks. For fast, high-volume coding help at lower cost, GPT-5.4 mini, Claude Haiku 4.5, and DeepSeek V4-Flash are strong value. Open-weight options like Llama 3.3 70B and Mistral Medium 3.5 are good when you need to self-host or avoid per-token fees. Sort the table by input or output price, filter by the capabilities you need, then read each model's strengths to match it to your task.
What is the difference between context window and max output?
The context window is the total number of tokens the model can read at once, including your system prompt, the conversation history, any documents you paste, and the answer it generates. Max output is the cap on how many tokens it can produce in a single response. A model with a 1M context window but a 64K max output can read a very large document but will only write up to about 64K tokens back per turn. For long generations you may need to stream the output or split the work across several calls. Both numbers are shown in the table and in each model's expanded detail.
What does the speed tier mean?
Speed is a relative tier (Fast, Medium, or Slow) describing typical latency and throughput for a model's class, not an exact benchmark. Fast models return tokens quickly and suit chat, autocomplete, and high-volume jobs. Slow usually means a heavier reasoning model that thinks longer before answering, trading latency for quality on hard problems. Medium sits in between. Actual speed varies with prompt length, output length, server load, and whether extended reasoning is enabled, so treat the tier as a guide rather than a guarantee.
Should I use an open-source or a proprietary model?
Proprietary models from OpenAI, Anthropic, Google, and xAI are the simplest to use: you call a hosted API and get frontier quality with no infrastructure to manage. Open-weight models such as Llama 3.3 70B, Mistral Medium 3.5, and DeepSeek V4-Flash let you self-host for data control, predictable cost at scale, fine-tuning, and no vendor lock-in, at the cost of running and maintaining the serving stack. Many teams use both: a hosted frontier model for the hardest work and an open model for high-volume or sensitive workloads. Use the Open source filter to see only open-weight models.
What is function calling and which models support it?
Function calling, also called tool calling, lets a model return a structured request to run one of your functions, such as looking up an order, querying a database, or calling an external API. The model decides when a tool is needed and supplies the arguments; you run the function and feed the result back. It is the foundation of agents and assistants that take actions rather than just chat. Almost every current model in this table supports it, so use the Function calling filter to confirm, and check each model's detail since tool-calling reliability varies between models.
Which models support vision (image input)?
Vision models can accept images as input alongside text, so you can ask questions about screenshots, photos, charts, diagrams, and scanned documents. Most current frontier models are multimodal and accept images, while some efficiency-focused or open text-only models are not. Note that vision here means image input; generating images is a separate capability handled by dedicated image models. Use the Vision filter to show only models that accept image input, and see each model's detail row for any practical limits.
How often does AI model pricing change?
Pricing changes more often than any other spec. Providers regularly cut prices as efficiency improves, launch new tiers, retire older models, and offer discounts through batch processing and prompt caching. The figures here are standard on-demand list prices verified in June 2026 and kept in sync with our LLM Cost Calculator, but they can change at any time. Always confirm the current rate on the provider's official pricing page before you budget, and remember that batch and cached pricing can be substantially lower than the list rate.
Do benchmarks reflect real-world performance?
Not perfectly. Benchmarks measure narrow, standardized tasks and are useful for rough comparison, but they can be gamed, can leak into training data, and rarely match your exact workload. A model that tops a coding benchmark may still feel worse on your codebase, your prompts, and your latency budget. Treat published scores as a starting point, then run your own evaluation on a handful of real tasks before committing. The strengths and weaknesses in each model's detail row summarize practical behavior beyond the raw numbers.