Free Tool

AI Model Comparison Table

Compare today's leading AI models side by side — context window, modalities, price per million tokens, and what each is best for. Filter, sort, pin to compare, and estimate your monthly cost.

Pin Model Context Modalities In $/1M Out $/1M Best for Open

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LLM Context Window Comparison: See Context, Price, and Capability Side by Side

Choosing a model usually means opening eight tabs, each presenting its numbers differently, and trying to hold three context windows in your head while comparing prices quoted per different units. By the time you have the picture assembled, another model has launched.

This table collapses that into one view. Every model sits in the same grid, measured on the same columns, so a like-for-like LLM context window comparison takes seconds instead of an afternoon.

What Each Row Tells You

The table shows the attributes that actually drive a decision:

  • Context — how much the model can hold at once, sortable so you can rank by capacity directly.
  • Modalities — what kinds of input it accepts.
  • Input price per million tokens and output price per million tokens, listed separately because the gap between them changes your bill significantly.
  • Best for — the practical positioning, in plain language.
  • Open — whether the weights are open.

Sorting is available on model, context, and both price columns, so you can order the whole table by whichever factor matters most to your project.

Filter Before You Compare

Filters narrow the field before you start reading closely. Set a minimum context — any, 32K+, 128K+, 200K+, or 1M+ — to hide anything that cannot handle your documents. Set a maximum input price to remove options outside budget. Filter by modalities if you need more than text. There is also a search box for jumping straight to a named model.

An open-weights only toggle answers a specific question people arrive with: an open source LLM comparison, filtered to models you can self-host or inspect rather than only call through an API. It is one switch instead of a research project.

Pin the Shortlist, Then Estimate the Bill

Every row has a pin. Pinning two or three models isolates your shortlist so the rest of the table stops competing for attention.

Beneath the table sits an optional cost estimator. Enter your input tokens per request, output tokens per request, and requests per month, and each pinned model returns an estimated monthly cost at your actual volumes. This is the step most comparisons omit — a price per million tokens means very little until it meets your traffic.

A Realistic Example: A Document Summarisation Feature

A product team is adding summarisation for uploaded contracts, often forty pages long. Their real constraint is context length; their second constraint is monthly spend.

They set the minimum context filter to 200K+, which immediately removes most of the table. From what remains, they sort by input price ascending and pin the three cheapest that still fit. Then they enter their volumes — roughly 60,000 input tokens per request, 1,500 output, 4,000 requests a month.

The estimator shows the three monthly figures side by side, and the cheapest model that meets the context requirement becomes obvious. The whole evaluation takes ten minutes.

Answering "Which LLM Is the Best" Honestly

There is no universal answer, and any page claiming one is selling something. The best model for long-document work is rarely the best for high-volume classification, and the cheapest LLM by input price may not be cheapest once output-heavy workloads are counted.

The table takes the opposite approach: it presents the specifications and lets you weigh them against your own constraints. If you are evaluating the best open source LLM for coding in 2026, filter to open weights, check the "best for" column, and compare pricing on the same screen — then judge against your own tasks rather than a leaderboard.

Who It Helps

Developers picking a model for a build. Product managers comparing options before committing. Founders weighing capability against burn rate. Anyone who has to justify a model choice to someone else. Once you have picked one, the LLM Token Cost Calculator and API Cost Calculator refine the budgeting.

Compare Before You Commit

Model choice sets your ceiling on capability and your floor on cost. Run an LLM context window comparison above, filter to what your project actually needs, pin your shortlist, and see the monthly figure before you write a line of integration code.

Use this ai model comparison and llm comparison table to compare ai models side by side, including a dedicated Claude Opus 5 vs GPT-5.6 breakdown, so you can find the best llm 2026 for your use case without reading a dozen separate reviews. Every price is checked against the provider pricing page and dated on the table.

Questions answered

Frequently asked questions

Everything you might be wondering about the AI Model Comparison Table.

Which is better, Claude Opus 5 or GPT-5.6 Sol?
They are close on both capability and price - Opus 5 is $5 per 1M input and $25 per 1M output, GPT-5.6 Sol is $4 and $20, and both take about a million tokens of context. Pin them in the table, then decide on the task rather than the sticker price.
What is the best AI model for coding in 2026?
Compare candidates on coding-relevant rows and published benchmarks like SWE-bench Verified rather than general reputation. Claude Opus 5 and GPT-5.6 Sol are the usual shortlist; Claude Sonnet 5 at $2 / $10 is worth testing before you pay flagship rates.
What does the comparison table show?
Provider, context window, input and output price per million tokens, modalities, open-weight status and what each model is suited to - side by side, with the date the prices were last verified.
Is it free to use?
Yes, the AI model comparison table is free and requires no signup.
Which is the cheapest AI model in 2026?
Among the models listed, GPT-5.6 Luna is cheapest at $0.20 per 1M input and $1.20 per 1M output. Sort by the input or output price column to rank them yourself, and remember output tokens usually dominate a real bill.
See it in action

A worked example

Real input, real output — so you know what to expect before you run it yourself.

Comparing Claude Opus 5 vs GPT-5.6 Sol for coding
Sample input
Task: Building an AI-powered code review system. Need: Strong reasoning and long context. Budget: $1000/month.
Sample output
Filter by the coding tag, then pin Claude Opus 5 and GPT-5.6 Sol. Opus 5 is $5 per 1M input and $25 per 1M output; GPT-5.6 Sol is $4 and $20. At 2M input and 500K output tokens a month that is $22.50 on Opus 5 and $18.00 on GPT-5.6 Sol - close enough that capability, not price, should decide. If budget is the binding constraint, pin Claude Sonnet 5 ($2 / $10, the same run costs $9.00) and test it on your own code before paying flagship rates.