TL;DR: LLM API pricing is metered per token, not by subscription. As of July 10, 2026, input runs from $0.05 to $5.00 per million tokens and output from $0.18 to $30.00 across the current flagships. No single provider is cheapest for every job: GPT-5-nano wins on input price, DeepSeek V4 Flash on output, Gemini 2.5 Flash-Lite on context per dollar. Every figure below is pulled live and dated.
Search "llm price comparison" and you land on raw calculators. costgoat.com, pricepertoken.com, llmpricecheck.com, artificialanalysis.ai, all ranking a grid of numbers with no verdict attached. We checked the July 9, 2026 results for that query and for "llm api pricing calculator": no AI Overview on either, a thin field, and not one page that connects a per-token rate to a real bill or says which model to actually pick. This hub is the LLM price comparison those tools skip. Every price comes from the OpenRouter models API snapshot fetched July 10, 2026 (347 models), cross-checked against each vendor's published rates. Then it does the math a raw table skips, and routes you to the deep page for your provider.
Disclosure: we have no affiliate or paid relationship with OpenAI, Anthropic, Google, xAI, DeepSeek , or OpenRouter as of publication. We read prices from OpenRouter 's public model feed; how that feed works, and where its markup sits, is in our OpenRouter review . If any relationship changes, this paragraph will say so.
LLM API pricing compared (live table)
LLM API pricing is metered per token, not by subscription. As of July 10, 2026, input runs from $0.05 to $5.00 per million tokens and output from $0.18 to $30.00 across the current flagships. Input and output bill separately, and output costs 4 to 8 times the input rate. Here is the cross-provider grid, sorted flagship to floor.
| Tier | Model | Input / 1M | Output / 1M | Context |
|---|---|---|---|---|
| Flagship | Claude Opus 4.8 | $5.00 | $25.00 | 1M |
| Flagship | GPT-5.6 Sol | $5.00 | $30.00 | 1.05M |
| Flagship | Gemini 3.1 Pro | $2.00 | $12.00 | 1.05M |
| Flagship | Grok 4.5 | $2.00 | $6.00 | 500K |
| Workhorse | Claude Sonnet 5* | $2.00 | $10.00 | 1M |
| Workhorse | GPT-5.1 | $1.25 | $10.00 | 400K |
| Workhorse | Gemini 2.5 Pro | $1.25 | $10.00 | 1.05M |
| Budget | Claude Haiku 4.5 | $1.00 | $5.00 | 200K |
| Budget | GPT-5-mini | $0.25 | $2.00 | 400K |
| Budget | Gemini 2.5 Flash | $0.30 | $2.50 | 1M |
| Budget | DeepSeek V4 Pro | $0.44 | $0.87 | 1.05M |
| Floor | GPT-5-nano | $0.05 | $0.40 | 400K |
| Floor | Gemini 2.5 Flash-Lite | $0.10 | $0.40 | 1M |
| Floor | DeepSeek V4 Flash | $0.09 | $0.18 | 1M |
| Floor | Llama 4 Scout (open) | $0.10 | $0.30 | 10M |
*Claude Sonnet 5 is on introductory pricing through August 31, 2026. It reverts to $3.00 / $15.00 after that.
Prices last verified July 10, 2026, from the OpenRouter models API snapshot, cross-checked against vendor pricing pages.
Four price tiers, flagship to floor, with representative models.
Two things the grid rewards reading twice. First, the span. The cheapest input rate ($0.05, GPT-5-nano) and the dearest ($5.00, the top flagships) are 100x apart, and providers do not rank the same on input as on output. Second, context is not billed the same way as tokens. Grok 4.5 carries a 500K window, most flagships sit at 1M, and Llama 4 Scout reaches 10M, yet the context window cost is folded into the standard rate, not billed as a surcharge. Pin the exact model version before you budget. Claude Opus pricing holds at $5 / $25 across the 4.5 to 4.8 builds, but legacy Opus 4.1 still lists at $15 / $75, three times the current rate.
Price per token, explained in one paragraph
A token is a chunk of text, roughly four characters, and 1M tokens is about 750,000 words. You pay for the tokens you send and the tokens the model writes back (input output tokens), at separate rates, with output always the pricier side. Every sticker on this page is a cost per million tokens. That is the whole model, and most buyers arrive not knowing it. One r/ClaudeAI post that reached 1,809 upvotes put it plainly: coming from ChatGPT , "we have ZERO knowledge about 'tokens'." The number that matters is not the sticker rate. It is the rate times your real input and output volume, which the next section computes.
LLM API pricing calculator
The formula is short. A raw calculator hides it behind a form; here it is in the open, so you can plan without a tool.
Priced on one identical job, a support chatbot at 3,000 input and 500 output tokens per turn, here is the cost per 1,000 requests across the lineup. Same work, every model.
| Model | Cost per 1,000 requests |
|---|---|
| GPT-5-nano | $0.35 |
| DeepSeek V4 Flash | $0.36 |
| Gemini 2.5 Flash-Lite | $0.50 |
| GPT-5-mini | $1.75 |
| Gemini 2.5 Flash | $2.15 |
| Claude Haiku 4.5 | $5.50 |
| GPT-5.1 | $8.75 |
| Claude Sonnet 5 | $11.00 |
| Claude Opus 4.8 | $27.50 |
| GPT-5.6 Sol | $30.00 |
The same chatbot job costs about 85x more on the top flagship ($30.00) than on the budget floor ($0.35). On this mix the GPT-5 API cost lands mid-table at $8.75, while Gemini Flash pricing ($0.30 / $2.50) comes in at $2.15. That spread is the whole argument for routing cheap work to a cheap model. When a job does need a flagship, our Claude vs ChatGPT comparison settles that pick task by task.
One caveat the token calculator cannot price for you: agent loops . A single user action in an agent workflow fans out into many model calls. An Ask HN thread on forecasting these costs (2026-03-11) described it exactly: "a single user action can trigger anywhere from a few to dozens of LLM calls (tool use, retries, reasoning steps)." So multiply the per-call cost by 3 to 30, not by 1, and pad the forecast for the tail. Per-model workload math sits on the OpenAI and Claude pages.
Cheapest model for production right now
There is no single cheapest model, and any page that names one is hiding the split. Cheapest depends on your token mix. For input-heavy work (classification, extraction, tagging), GPT-5-nano is the floor at $0.05 per million input tokens. For output-heavy work (generation, drafting, long replies), DeepSeek V4 Flash wins at $0.18 output, less than half GPT-5-nano's $0.40. For long-context jobs on a budget, Gemini 2.5 Flash-Lite pairs a $0.10 / $0.40 rate with a 1M window.
That is the honest read across providers. GPT-5-nano owns the input rate but charges more than twice as much per output token as DeepSeek V4 Flash . DeepSeek is cheapest per output token but runs on a non-US ecosystem with different latency and data terms. Llama 4 Scout carries a 10M context window at $0.10 / $0.30, but open-weight quality trails the closed flagships on hard reasoning. Price is one axis. Rate limits, API tiers, and throughput gate the others, and quality decides whether the cheap answer is the right one. For the score-per-dollar view on real tasks, our best LLM for coding breakdown joins live price to coding benchmarks.
There is no single cheapest model; it depends on your token mix.
Batch and prompt-caching discounts compared
Two levers cut the sticker bill, and raw tables omit both. Prompt caching discounts are the bigger lever. Reusing a cached prefix costs about one-tenth of the input rate across the big three providers. On DeepSeek V4 Pro it drops under 1%.
| Model | Input / 1M | Cached read / 1M | Read as % of input |
|---|---|---|---|
| Claude Opus 4.8 | $5.00 | $0.50 | 10% |
| GPT-5.1 | $1.25 | $0.13 | ~10% |
| Gemini 2.5 Pro | $1.25 | $0.13 | ~10% |
| DeepSeek V4 Pro | $0.44 | $0.004 | <1% |
The catch is on the write side. Anthropic charges 1.25x the input rate to write a prompt into cache, so caching pays off from the second read, not the first. The more you reuse a fixed prefix (a system prompt, a document, a codebase), the closer the saving climbs to the full 90%.
The second lever is batch API discounts. OpenAI and Anthropic both document a roughly 50% cut on asynchronous jobs that tolerate a delay, with no caching setup. For overnight bulk work, that is the easier discount to capture. Verify the current caching and batch terms on the vendor page before you budget a large run; these mechanics change more often than the sticker rates.
How we keep this data fresh
Every number on this page comes from our OpenRouter live pricing feed, the OpenRouter models API, which aggregates per-token rates across 347 models and providers, cross-checked against each vendor's published pricing. The snapshot behind this update was fetched July 10, 2026, which is the dateModified on our pricing Dataset. When a provider ships a new model, or the Claude Sonnet 5 introductory rate expires on August 31, 2026, this LLM price comparison gets re-pulled and re-dated. How that live feed works, what it costs per token, and where the markup sits is documented in our OpenRouter review , which also discloses that this hub reads from it.
Start here: the /llm/ pricing cluster
This hub frames how pricing works and who is cheapest for what. Each member below goes deep on one provider or decision, all verified against the same July 10, 2026 snapshot.
All guides in this topic
- Best LLM for Coding (July 2026): Verified Prices, Benchmark Reality, and Which Model Wins Each Job — Live OpenRouter prices (verified July 10, 2026) joined to real coding benchmarks. Which model wins for agents, autocomplete, budget, and self-hosting, with honest limits.
- Claude API Pricing (2026): Live Cost Per Token for Opus, Sonnet, and Haiku — Claude API pricing, verified July 10, 2026 from live OpenRouter data: Haiku 4.5 $1/$5, Sonnet 5 $2/$10, Opus 4.8 $5/$25 per million tokens, plus real-workload cost math.
- Claude vs ChatGPT (2026): The Task-by-Task Verdict on Verified Live Pricing — Claude wins writing and code reasoning; ChatGPT wins images, voice, and web research. Flagship APIs price within 10%. The real deal-breaker is Claude 's usage caps. Verified July 2026.
- OpenAI API Pricing (July 2026): Live Per-Token Rates, Real Workload Costs, and Cheaper Swaps — Live OpenAI API pricing from the July 10, 2026 OpenRouter snapshot: per-token rates, cost on three real workloads, and cheaper same-job swaps.
- OpenRouter Review (2026): 347 Models, One API, and the Markup Nobody Explains — OpenRouter puts 347 models behind one API key. We use its data feed daily. Here is the per-token math, the real fees, the dependency risk, and a full disclosure. Verified July 10, 2026.