LLM API pricing in 2026, compared across Claude, GPT, and Gemini

tldr: Output tokens cost five to eight times input tokens at every vendor, so your output-to-input ratio decides your bill more than any headline rate does. Current verified rates are below. Two rules move real bills more than the rate card: Anthropic's newer models produce about 30% more tokens for the same text, and both OpenAI and Google surcharge long-context requests while Anthropic does not.

What does each model cost per million tokens?

Rates per million tokens, verified against each vendor's own pricing page on 25 July 2026, sorted by output price.

Anthropic and OpenAI both discount batch processing by 50% on input and output. Anthropic's cache reads cost 10% of base input, with writes at 1.25x for a five-minute cache and 2x for an hour. OpenAI's cache writes run 1.25x standard input. Gemini charges $1.00 per million cached tokens per hour of storage on top of the read rate.

On context windows, the three GPT-5.6 tiers all carry a 1.05 million token window with a 128,000 token output ceiling, and every Claude model from 4.6 onward carries a one-million-token window. The difference is what you pay to fill them, which is the second bullet below.

Which models actually compete with each other?

Vendor pricing pages list models by vendor, which invites the mistake of comparing across weight classes and concluding the cheap one is better value. Line them up by output price instead and the real fights appear.

Read that table top to bottom and the first thing that stands out is who is missing. Google does not field a model in either of the top two tiers. Its most expensive model, 3.1 Pro, tops out at $12 output, which lands it in the mid tier against Sonnet 5 and Terra rather than against Fable 5 or Sol. Anyone shopping the frontier is choosing between Anthropic and OpenAI, and Google is competing on price per unit of adequate work instead.

The near-frontier row is the interesting one, because Opus 5 and GPT-5.6 Sol charge the identical $5 for input and differ only on output, $25 against $30. On a coding-shaped request of 30,000 input and 5,000 output tokens, Opus 5 costs $0.275 and Sol costs $0.30. Ten thousand of those a month is $2,750 against $3,000, a 9% gap that no benchmark table will tell you about.

The practical rule: choose the tier from the task, then compare prices inside the tier. A comparison that crosses tiers tells you nothing except that cheaper models are cheaper.

Why the output rate decides your bill

Output costs five to eight times input everywhere on that table, so the shape of your workload matters more than the vendor you pick. An app that reads 6,000 tokens of retrieved context and writes a 300-token answer barely notices an output price cut. A coding agent that reads 3,000 and writes 2,000 lives or dies by it.

We worked the full derivation through in our Gemini API pricing breakdown, including the formula for what share of your bill is output at a given ratio. The short version: measure your own ratio before you compare vendors, because a model with cheaper output and pricier input can win or lose on workload shape alone.

What the pricing page does not tell you

These are the multipliers that turn a quoted rate into an actual invoice. Two of them are large enough to reverse a vendor decision.

The tokenizer changed, and nobody put it on the rate card. Anthropic's Claude 4.7 and later models use a newer tokenizer that produces roughly 30% more tokens for the same text than Claude Sonnet 4.6 and earlier. Opus 4.6 and Opus 5 both list at $5 and $25. Run identical text through both and the newer one bills about 30% more, because it counts more tokens, not because it charges more per token. Our coding-shaped request at $0.275 becomes $0.3575 once the same text tokenizes 30% heavier. Anthropic is explicit that the increase varies by content, so measure yours rather than assuming the headline figure.

Long context is surcharged everywhere except Anthropic. Claude 4.6 and later include the full one-million-token window at standard rates, so a 900,000-token request bills at the same per-token rate as a 9,000-token one. Both competitors step the price up, and step it up identically: 2x input and 1.5x output on the whole request, OpenAI once input passes 272,000 tokens and Google once it passes 200,000 on 3.1 Pro. Feed Sol and Opus 5 the same 300,000-token repository with a 2,000-token answer and Sol costs $3.09 against Opus 5's $1.55, almost exactly double. The 9% gap from the near-frontier comparison inverts completely at large context, and Google's threshold arrives even earlier than OpenAI's.

Reasoning tokens bill as output. Gemini states this outright for Flash and Flash-Lite, and it holds wherever a model thinks before answering. A harder prompt costs more than an easy one even when the visible answers are the same length, which makes response length a poor proxy for cost. Log output token counts per request instead. Anthropic's effort parameter makes this controllable, and we worked through what each level does to a bill separately.

Server-side tools bill outside tokens entirely. Anthropic charges $10 per 1,000 web searches on top of the tokens the results consume, and code execution runs 1,550 free hours per organisation per month before charging $0.05 per container-hour. Neither line appears anywhere on a per-million-token table, and neither shows up in a token-based cost estimate.

Tools add tokens before the model does anything. Simply having tools present on a request injects a system prompt: 286 tokens on Opus 5, or 406 when tool choice is forced, plus the schemas themselves and another 325 for the bash tool. An agent making many small requests pays that overhead on every one.

Anthropic's Fast mode is exactly double. Opus 5 and Opus 4.8 run at $10 and $50 under Fast mode, matching the Fable 5 rate card. It is in research preview, first-party API only, and cannot be combined with the Batch API, so the 50% batch discount and the speed premium are mutually exclusive choices.

Tool calls and retries multiply request counts, not rates. One agentic request is several billed model calls, since every tool result comes back through the model with the full context attached. A 3% failure rate on structured output is a 3% surcharge on everything above it. Anything you re-run, you re-pay for, which is why Passmark caches AI test steps rather than re-executing them on every run.

Data residency costs 10%. Anthropic applies a 1.1x multiplier across input, output, and cache when you pin inference to the US, and OpenAI adds a 10% uplift on regional endpoints for models released from 5 March 2026.

How do you compare vendors without getting fooled?

Compare dollars per task, not dollars per million tokens. The rate card is the one number every vendor publishes and the one number that cannot answer your question on its own, because it says nothing about how many tokens a model spends reaching the same answer.

A quoted rate becomes useful only once you attach a workload to it: your token ratio, your context size, your tool-call count, your retry rate. Run a day of your own traffic through two candidates, compare the invoices, and ignore the rate cards entirely.

That comparison is far easier when your agent layer can switch models per task rather than per deployment. FactoryKit, the AI software factory we build, picks between Claude Code, Codex, and Grok Build per task for this reason, which turns a vendor decision into a routing setting you can change without redeploying.

Key takeaways

• Output costs five to eight times input at every vendor. Your output-to-input ratio matters more than your choice of vendor.
• Opus 5 and GPT-5.6 Sol are the same weight class at the same input price. Sol's output is 20% dearer, which works out to roughly 9% on a coding-shaped request.
• Anthropic's models from 4.7 onward tokenize about 30% heavier. Same rate card, bigger bill, and it is invisible unless you measure tokens.
• Long context is surcharged by OpenAI above 272,000 input tokens and by Google above 200,000, both at 2x input and 1.5x output. Anthropic charges its normal rate, so large-context work reverses the ranking.
• Google fields nothing in the top two tiers. Its most expensive model sits in the mid tier against Sonnet 5 and Terra.
• Batch processing is a flat 50% off at Anthropic and OpenAI. Anthropic's Fast mode is a flat 100% on, and the two cannot be combined.

FAQ

What is the cheapest LLM API?

Gemini 3.5 Flash-Lite has the lowest rates on the table at $0.30 input and $2.50 output per million tokens. Cheapest per token is not the same as cheapest per task, though: a model that needs three attempts costs more than one that succeeds once at twice the rate. For mechanical work such as classification, extraction, and routing, the floor tier really is cheapest. For anything requiring reasoning, compare cost per completed task.

How much does Claude cost per million tokens?

Claude Opus 5 and Opus 4.8 cost $5 input and $25 output. Fable 5 and Mythos 5 cost $10 and $50. Sonnet 5 is $2 and $10 through 31 August 2026, then $3 and $15. Haiku 4.5 is $1 and $5. Cache reads cost 10% of base input on all of them, and the Batch API halves everything.

Is GPT-5.6 cheaper than Claude Opus 5?

No. They charge the same $5 for input, and Sol's output is $30 against Opus 5's $25. On a request reading 30,000 tokens and writing 5,000, Sol costs about 9% more. Past 272,000 input tokens the gap widens sharply, because Sol's long-context surcharge doubles input pricing while Opus 5 stays flat.

Should you use Claude Opus or Claude Sonnet?

Sonnet for most production work, Opus for the requests that measurably fail on Sonnet. The price gap is the argument: Opus 5 costs $5 and $25 against Sonnet 5's introductory $2 and $10, so Opus is two and a half times dearer per token before you account for it spending more of them on reasoning. Anthropic's own guidance points the same way, Sonnet for most workloads and Opus for the most complex reasoning. Route rather than default, and measure your escalation rate before committing either way.

Do thinking tokens cost extra?

They bill at the output rate, so yes in effect. Google states this explicitly for Gemini. The consequence is that two prompts producing similarly sized answers can cost very differently, so track output token counts per request rather than trusting response length.

Which model should I use for coding?

Start in the volume tier and escalate only the requests that measurably fail there. Most coding work is well-specified enough that a mid-tier model handles it, and reserving the near-frontier tier for genuinely hard requests beats defaulting everything to it on quality per dollar. If your repository context runs past 272,000 tokens, Anthropic's flat long-context pricing changes the arithmetic in its favour. Which agent you drive that model through is a separate decision, and we compared Codex against Claude Code on exactly that.

Does the Batch API stack with caching?

Yes. Anthropic's batch discount and prompt caching multipliers combine, so a cached read inside a batch request gets both reductions. Fast mode is the exception, since it cannot be used with the Batch API at all.