Frontier AI Models Compared: Official Context, Cost, Output, and Tool Data

Snapshot checked: August 5, 2026. This comparison uses developer-published specifications, not a third-party leaderboard. The numbers describe API limits and list prices; they do not predict the quality of your particular prompt.

Model comparison pages often place a single “score” at the top and hide the conditions underneath. That is backwards. Before asking which model is best, a buyer needs to know how much context it accepts, how much output it can produce, what the request costs, which modalities and tools it supports, and how stable the model identifier is.

At a glance

Prices above are USD per one million tokens for the standard API tier where the provider publishes that number. Google lists a higher price for Gemini requests above 200K input tokens. Meta's model card describes the model and license but does not provide a first-party API token price, so that cell is intentionally marked “not published.”

Chart 1: official context window

What the context number does—and does not—tell you

GPT-5.6 Sol is listed with a 1,050,000-token context window and 128,000-token maximum output. Anthropic lists Claude Opus 5 at 1M context and 128K maximum output. Google's Gemini 3.1 Pro lists 1M input and 64K output. Meta's Llama 4 Maverick model card lists a 1M context length.

These are not identical products. The context window includes the prompt, tool definitions, retrieved documents, conversation history, and generated output according to each API's accounting rules. Long context also has a quality cost: irrelevant material can dilute the useful evidence. For a production system, test recall and answer quality at the context sizes you actually plan to send instead of selecting a model solely because its headline number is larger.

Chart 2: maximum output

Chart 3: standard input and output price

Why “cheapest” is not a model recommendation

Input and output tokens are not consumed symmetrically. A retrieval-heavy application may spend most of its budget on input; a writing or coding workflow may generate more output. A model with a low input price can still be expensive if it needs repeated retries, produces long answers, or requires an external reranker and tool layer.

Gemini's published price also changes above 200K input tokens. OpenAI lists a higher price for GPT-5.6 Sol prompts above 272K input tokens. These thresholds make a cost estimate based on a short prompt misleading for long-document workloads. Measure representative requests, include retries, and separate model cost from storage, search, and application infrastructure.

Modalities, tools, and deployment shape

OpenAI lists text and image input with text output, plus functions, web search, file search, and computer use on the GPT-5.6 Sol page. Anthropic's model overview says current Claude models support text and image input, text output, multilingual capability, and vision. Google's Gemini 3 documentation emphasizes multimodal input, thinking controls, built-in tools, structured outputs, and computer use. Meta's Llama 4 model card describes native text-and-image input with multilingual text and code output, plus a community license and self-hosting path.

Those capabilities change the architecture. A hosted model with built-in web search may reduce integration work but add tool charges and provider coupling. An open-weight model may offer deployment control but move GPU, safety, observability, and license work onto the buyer. “Best model” is therefore partly a question about operating model, not just answer quality.

Benchmarks: what we deliberately did not rank

Each vendor publishes benchmark or evaluation material, but the test sets, prompt formats, tool settings, sampling, and reporting windows differ. A score from one vendor's model card is not a fair cross-vendor ranking by itself. We leave benchmark cells out of the chart until the same task, data, harness, and cost budget are held constant.

For a real buying decision, run a small private evaluation: 20–50 representative tasks, a fixed rubric, blind judging where possible, repeated runs, and a cost/latency log. Report the task mix and failure categories. A lower average score can still be the right choice if it handles your highest-risk task more reliably.

Practical recommendations

• Complex coding and long agent traces: start with GPT-5.6 Sol or Claude Opus 5, then test tool reliability and cost at your actual context size.
• Large multimodal documents with a lower API price: test Gemini 3.1 Pro, especially if its built-in tools fit your stack.
• Self-hosting, customization, or deployment control: evaluate Llama 4 Maverick, but include infrastructure cost, license review, and safety tuning.
• High-volume production: compare smaller tiers such as GPT-5.6 Terra/Luna, Claude Sonnet 5, or Gemini Flash against a fixed quality threshold rather than assuming the flagship is economical.

Bottom line

The official data shows a crowded frontier: all four models claim roughly million-token context, while output limits, price curves, knowledge cutoffs, tool surfaces, and deployment choices differ materially. The useful comparison is not “which logo wins.” It is “which model clears our quality bar for this workload at a cost and operating model we can sustain?”

We will update this page when a provider changes a model ID, pricing tier, context limit, or knowledge-cutoff statement. Treat this snapshot as a decision aid, not a permanent leaderboard.