Frontier LLM APIs compared — price, context, tool use and deprecation policy Pricing Compared

Headline $/M is the least useful number in this category. Four things distort it.

First, output tokens dominate. A reasoning model can burn tens of thousands of thinking tokens before it writes a word you see, and those bill at the output rate. A provider with a cheap input rate and an expensive output rate is expensive for reasoning work and cheap for retrieval-augmented answering. Model your actual input:output ratio before comparing.

Second, cached input is where the real money is. Cache reads bill at roughly a tenth of the input rate at Anthropic and OpenAI, and an agent loop resending its history every turn is mostly cache reads. But caching is a prefix match — a timestamp in the system prompt, a non-deterministic JSON serialisation, or a tool list that varies per user destroys it silently. Watch the cache-read token counter, not the invoice.

Third, long-context surcharges. Google charges a higher rate above 200K tokens on its flagship; others quietly meter cache storage per hour. A million-token window advertised at the short-context rate is not what you will pay.

Fourth, batch — but check the column before you budget for it. Where an asynchronous endpoint exists the discount is usually a flat 50%, and Anthropic, OpenAI, Google, Mistral and Qwen all publish that rate, as do Bedrock and Together. It is not universal. DeepSeek offers no batch endpoint at all — its list price already sits below most competitors' batch rates — and neither does OpenRouter, which is a real cost if half your workload could tolerate async. xAI, Moonshot, Z.ai, Cohere, Fireworks and Cerebras publish nothing we could confirm, and Groq's batch discount is documented as existing without a percentage we could stand behind. Where the halving is there and part of your workload tolerates a few hours of latency, it is the one saving no negotiation will beat; where it is not, no amount of committed spend conjures it.

Bulk text processing — classification, extraction, tagging, translation, summarising a firehose. Price dominates and capability differences barely register. DeepSeek, Qwen, Moonshot and Z.ai are the rational choices, or Groq and Together if you want open weights with a Western contract. Add the batch endpoint if latency is negotiable. Do not put sensitive data through a provider whose retention terms you haven't read.

Agent harnesses — a loop that plans, calls tools, reads results and iterates for minutes at a time. This is where the frontier labs earn their price. You need tool calls that stay well-formed at depth, structured output that is enforced rather than requested, an effort or thinking control so you can trade cost against quality per route, and prompt caching that survives the loop. Anthropic and OpenAI are the credible options; Google is close and cheaper on long context. Budget for the fact that a long agentic turn can now run for minutes on a single request.

Interactive, latency-critical UI — voice, live search, inline completion. Time to first token is the product. Cerebras and Groq are in a different class here, an order of magnitude ahead of first-party endpoints on tokens per second, and the constraint is that you take whichever open-weight models they have chosen to host. Design the feature around the model menu, not the other way around.

Pricing on this page is stale within a fortnight. That is not hedging — this category re-prices faster than any other on toolweight, so the page carries a seven-day verification cadence and every price cell is dated. Treat any cell whose date is more than a month old as indicative and click through to the vendor's pricing page before you build a forecast on it. Figures are pay-as-you-go list rates in USD per million tokens, before batch, cache or committed-spend discounts, and before any provider-specific long-context surcharge. One convention worth stating: where a vendor is running an unexpired introductory rate, the column carries the list price and the cell note carries the introductory rate and its expiry date. An introductory rate is not a discount you negotiate — it is what you actually pay today — so read the note before modelling a bill.

Entries here are providers, not models. Because a provider ships a dozen models at a dozen prices, every price, context and capability figure on a row describes that provider's current flagship model, named explicitly in the Flagship model column so no number is orphaned from the thing it measures. Compare rows knowing that a cheaper flagship often sits beside a cheaper mid-tier that would serve your workload better. The killer column, deprecation and continuity policy, records two things: how much notice you get before a model is retired, and whether you can pin a dated snapshot that keeps serving after the alias moves on. Continuity is judged from published deprecation pages and observed retirements, not from marketing claims, and where a provider publishes no policy at all we say so rather than guessing a number. Latency figures are community-reported from third-party benchmarks; toolweight has not run its own TTFT harness yet, so most of that column is honestly blank.