Compare AI Models, Context Windows and Pricing
Compare leading AI models by provider, context window, input price and output price, with links to verify current pricing.
| Model | Provider | Context | Input $/1M | Output $/1M | Official pricing |
|---|---|---|---|---|---|
| AILAMA | AILAMA.LIVE | 1,048,576 | — | — | Verify ↗ |
| Claude Opus 5 | Anthropic | 200,000 | $5.00 | $25.00 | Verify ↗ |
| Claude Sonnet 5 | Anthropic | 200,000 | $2.00 | $10.00 | Verify ↗ |
| Claude Haiku 4.5 | Anthropic | 200,000 | $1.00 | $5.00 | Verify ↗ |
| GPT-5.6 Sol | OpenAI | 400,000 | $5.00 | $30.00 | Verify ↗ |
| GPT-5.6 Luna | OpenAI | 400,000 | $0.20 | $1.20 | Verify ↗ |
| Gemini 3.1 Pro Preview | 1,000,000 | $2.00 | $12.00 | Verify ↗ | |
| Gemini 3.6 Flash | 1,000,000 | $1.50 | $7.50 | Verify ↗ | |
| Llama 3.1 70B | Meta (open-source, self-hosted) | 128,000 | — | — | Verify ↗ |
⚠ Verify current pricing before making decisions — model prices and availability change frequently.
Prices are approximate, per 1M tokens, in USD, and may be out of date. Last updated: August 2026. Always verify current pricing on each provider's official page before making decisions based on these numbers.
AI Model Comparison lays out specs like context window and per-token pricing for major models side by side, so you can pick one for a specific project instead of researching each provider separately.
How do you actually compare AI models, and is there a single 'best' one?
Start with the specific task you need done — writing, coding, reasoning, images — then compare price, speed, context length and capabilities among models that fit that task; the right model for coding a codebase migration and the right model for drafting marketing copy are rarely the same one. There's genuinely no single best model for every task: it depends on your budget, the performance and speed you actually need, and the specific type of work, whether that's coding, writing or a broader business use case like customer support or analysis.
What is context length, and does a more expensive model actually give better results?
Context length is the amount of text, measured in tokens, a model can process in one interaction — a larger context window helps when you're feeding it a long document, an entire codebase, or an extended conversation history. A more expensive model doesn't automatically mean better results for your specific task, either: pricier models can offer real advantages for complex or demanding work, but a cheaper model is often perfectly sufficient for simpler tasks, so cost alone isn't a reliable proxy for quality.
What's the actual difference between an AI model and an AI chatbot, and how often should pricing be rechecked?
A model is the underlying technology that generates responses; a chatbot is an application or interface built on top of one or more models — ChatGPT, for instance, is a chatbot product, while GPT is the model powering it. Models and pricing both change frequently in this industry, so it's worth rechecking current numbers before an important decision rather than relying on a comparison you did months earlier, since a model that was the best value last quarter may not be anymore.
Should you choose a model based on price or performance — and which is actually cheapest or fastest?
Usually a balance of both: a cheaper model may be entirely sufficient for simple tasks, while genuinely complex work can justify paying more for a stronger model. There's no fixed answer for which model is cheapest or fastest, since pricing varies by provider and plan, and speed depends on the model, provider, request size and current system load — always weigh both alongside quality for your specific workload rather than picking on price or speed alone.
When did comparing a machine's intelligence to a human's actually become a serious question?
Long before any of these models existed — 1950, to be precise. British mathematician Alan Turing published a paper called "Computing Machinery and Intelligence" that year, opening with the deceptively simple question "Can a machine think?" and proposing what he called the imitation game as a practical way to test it: an evaluator holds a text conversation with both a human and a machine, without knowing which is which, and the machine "passes" if the evaluator can't reliably tell them apart. Turing based the format on an older Victorian parlor game, and predicted that by the year 2000, a machine would fool an average judge roughly 30% of the time in a five-minute conversation. Every model comparison table since — weighing one AI's conversational ability, reasoning or writing against another's — is really still circling the same question Turing first formalized 75 years ago, just with dramatically better contestants.
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