Claude vs ChatGPT API: Which One Cheaper?

Di wan wey short

Claude vs ChatGPT API pricing no get one clear winner, because di two vendors dey charge input and output differently for every tier. As of August 2026, di top two Claude tiers dey charge di same amount per input token like their OpenAI counterparts, and dem charge less per output token. So Claude come slightly cheaper for work wey output plenty. For di small tier, Claude cost several times more for di same job. Any answer wey no mention tier and token mix na guess.

So dis page go first show you di arithmetic, then three workloads wey get their token mixes and costs, then di multipliers wey fit increase real bill pass di headline rate.

Na formula wey you need

Both APIs dey bill text for the same way. You pay for the tokens wey you send, and you pay higher rate for the tokens wey the model write back.

cost = (input tokens / 1,000,000) * input rate + (output tokens / 1,000,000) * output rate

One token na about 4 characters for English, near 0.75 of one word. Use this only for your first estimate. For correct bill, use the counts wey each API return inside the usage object for every response.

# Rates are US dollars per million tokens.
def cost(in_tok, out_tok, in_rate, out_rate):
    return in_tok / 1e6 * in_rate + out_tok / 1e6 * out_rate

# One support-chat turn: 1,400 tokens in, 220 tokens out, on a $2 / $10 model.
print(round(cost(1400, 220, 2.0, 10.0), 6))

The script dey print 0.005, wey be half a cent for each turn. Multiply am by the number of turns wey you expect for one month, and you get your budget. Learn this one formula, and every price change after today go be one-line edit instead of new analysis.

Rates wey dem publish, August 2026

Na these be the list prices wey both vendors publish on 1 August 2026. Na this block only for the whole post wey price dey written down. Check am against claude.com/pricing and OpenAI pricing page before you commit budget.

[
  {
    "label": "Frontier",
    "claude_input": 5,
    "claude_output": 25,
    "openai_input": 5,
    "openai_output": 30
  },
  {
    "label": "Workhorse",
    "claude_input": 2,
    "claude_output": 10,
    "openai_input": 2,
    "openai_output": 12
  },
  {
    "label": "Workhorse from September",
    "claude_input": 3,
    "claude_output": 15,
    "openai_input": 2,
    "openai_output": 12
  },
  {
    "label": "Small",
    "claude_input": 1,
    "claude_output": 5,
    "openai_input": 0.2,
    "openai_output": 1.2
  }
]

The pairings follow position for each lineup, no be popularity. Frontier na Claude Opus 5 against gpt-5.6-sol. Workhorse na Claude Sonnet 5 against gpt-5.6-terra. Small na Claude Haiku 4.5 against gpt-5.6-luna. Both vendors still sell higher tiers above this block, and OpenAI still lists older generations for their original rates. If you compare flagship for one side with budget model for the other side, the number no get meaning. Na so most published comparisons take get their headline.

Two things for that block need clear explanation. Sonnet 5 2 and 10 na introductory rates, and Anthropic document say dem go end on 31 August 2026. After that, Sonnet 5 go charge 3 and 15. That one change turns the workhorse tier from small Claude advantage to clear OpenAI advantage, and e go happen within the period wey this post still dey current.

The second thing na pattern wey worth memorising: Claude prices output at exactly 5 times its own input rate for every row above, while OpenAI prices output at 6 times. Na that ratio, no be the absolute rate, dey make the winner change based on your token mix.

Why rate per million tokens no dey compare across vendors

Rate na dollars per token. Bill na dollars per token multiplied by tokens, and token count for one text na property of the model, no be property of the text.

Anthropic document this directly: Claude 4.7 and later models, wey include Claude Opus 5 and Claude Sonnet 5, dey use newer tokenizer wey dey produce roughly 30% more tokens for the same text than Claude Sonnet 4.6 and earlier. Rate no change. Bill change.

So two rates wey look the same no be two identical bills. Count tokens for both sides, using your own text, before you trust any comparison, including this one.

curl https://api.anthropic.com/v1/messages/count_tokens \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [{"role": "user", "content": "Summarise the attached contract."}]
  }'

Healthy response na small JSON object wey hold input_tokens. A 401 mean say the key variable empty for your shell. Run the same text through the other vendor tokenizer, divide one count by the other, then multiply that ratio by the published rate before you compare. If the ratio favour one vendor by more than about 1.2, e outweigh every rate difference for the block above.

Scenario one: one chat feature

One support assistant dey inside web application. E dey handle 120,000 turns every month. Each turn dey send one system prompt, tool schemas, two retrieved help-desk snippets, and some earlier messages. Na about 1,400 input tokens. The model dey answer with about 220 tokens. E dey interactive, so batch discount no fit apply here. Run am for workhorse tier.

[
  {
    "label": "List price",
    "claude_usd": "600.00",
    "openai_usd": "652.80"
  },
  {
    "label": "Cached prefix",
    "claude_usd": "427.20",
    "openai_usd": "480.00"
  },
  {
    "label": "Small tier, cached",
    "claude_usd": "213.60",
    "openai_usd": "48.00"
  }
]

For list price, Claude bill na 600.00, while OpenAI bill na 652.80. The input costs na the same because both input rates na the same. The whole difference come from output, and Claude save only small amount. This difference no big enough to choose one vendor over the other.

Now mark 800 of the 1,400 input tokens as stable prefix: the system prompt and tool schemas wey no dey change between turns. Both vendors charge 10% of their base input rate when cache hit happen. Claude bill go drop to 427.20, and OpenAI own go drop to 480.00. Caching save more money than the vendor choice, and both platforms support am.

Na here Claude lose. Most support answers no need workhorse model. Move the same traffic to small tier, and Claude bill go be 213.60, while OpenAI bill go be 48.00. Divide one by the other, and Claude cost about four and a half times more for the same work. Claude Haiku 4.5 charge 1 per million input tokens, while gpt-5.6-luna charge 0.2. No amount of caching fit close a fivefold difference. If your workload fit small model, OpenAI cheaper, and the difference big.

Scenario two: pipeline wey dey handle document with long context

25,000 contracts every month. Every request get the same 9,000-token instruction and JSON schema, then 12,000 tokens of contract text, and the model dey return about 700 tokens of structured fields. Output na less than 4% of all the tokens for this job, and na this one fact decide the comparison.

[
  {
    "label": "Chat feature",
    "claude_usd": "427.20",
    "openai_usd": "480.00"
  },
  {
    "label": "Document pipeline",
    "claude_usd": "820.00",
    "openai_usd": "855.00"
  },
  {
    "label": "Coding agent",
    "claude_usd": "116.10",
    "openai_usd": "124.20"
  }
]

With the 9,000-token prefix cached for both sides, Claude charge 820.00 and OpenAI charge 855.00. The input costs match exactly, because both input rates match for August 2026. The whole difference dey from the output cost, wey Claude price at 5 times input and OpenAI price at 6 times input.

This job fit run as batch too. Both vendors reduce input and output cost by 50% for asynchronous work, so both totals reduce by half and the difference keep the same shape. Batch na the biggest single discount for either platform, and nightly document pipeline fit always use am.

Claude lose for the same tier logic as before. Field extraction against fixed schema na exactly the kind work wey small model dey do well, and input make up 97% of the tokens here. Moving down one tier multiplies the OpenAI input rate by 0.1 and the Claude input rate by 0.5. Test the small model on 200 documents before you assume say you need the bigger one, using the same evaluation wey you go run for choosing the right Claude tier for a job.

Scenario three: one coding agent wey dey always on

One developer dey run an agent for VPS, about 900 model turns every month. Each turn get about 60,000 input tokens of repository context, and 80% of am na cache hit. E dey produce about 1,800 tokens of edits and explanation. Use frontier tier price, because capability gap for coding fit cost real money.

Claude dey bill 116.10, compared with OpenAI's 124.20. Input rate na the same, cache read rate na the same, and Claude lower output rate dey win by about 7%.

That 7% dey inside the tokenizer error bar wey we mention before, so treat this scenario as tie for rates. But billing model no be tie. For one developer at this volume, subscription seat usually cheaper pass metered API calls, and the whole comparison go change once you include one. Work through the comparison between API and subscription cost before you put agent on metered billing. If you use metering, first learn where the tokens dey go, because coding agent token usage mostly come from context wey dey resend for every turn, rather than from the code wey e write.

Di multipliers wey dey decide di bill

Di headline rate na di smallest item for dis list. Each one wey dey below fit change real bill pass di difference between di two vendors for di same tier.

Cached input. Both vendors dey charge 10% of base input for cache hit. Anthropic still dey charge to write di cache: 1.25 times base input for five-minute entry and 2 times for one-hour entry. Later hit go refresh dat entry for di read price. So, five-minute entry don pay for itself after one read. Workload wey get long gaps between requests dey write more often than e dey read, and caching dey cost more than e dey save for dat case. Steady traffic dey cache well. Bursty traffic no dey.

Batch discounts. 50% off input and output for both platforms, but na for asynchronous work only. If di job fit wait, dis one go cut di bill for either vendor by half, and e still dey combine with caching.

Output share. Claude dey charge 5 times its input rate for output. OpenAI dey charge 6 times. Di more tokens wey your system dey write instead of read, di better Claude go look when input rate dey equal. Di opposite dey happen for input-dominated work.

Retries. Retry dey pay for di whole input again and e dey return nothing wey you fit use. If 6% of your calls fail schema validation and dem retry am, your input leg go big pass your plan by 6%. Log retries as cost line, no be error line. Teams dey discover dis multiplier last, and e often dey bigger than di vendor gap wey dem spend one week arguing about.

Na which side go lose, and for where

Claude lose the small tier clear. gpt-5.6-luna dey charge 0.2 per million input tokens, while Claude Haiku 4.5 dey charge 1. For output, e dey charge 1.2 against 5. High-volume classification and short extraction dey cost about four times more for Claude.

Claude lose the workhorse tier from 1 September 2026, when the introductory rate go end and Sonnet 5 go move to 3 and 15 against gpt-5.6-terra wey no change. Claude win the frontier tier for output rate, but your own token counts fit wipe out that margin. No side win input-dominated work for August 2026, because at 2 per million for both sides, the input costs na the same.

How to measure this on your own traffic

Read the usage object for every response and store four numbers for each request: input tokens, output tokens, cache read tokens, and cache write tokens. For Claude API, dem dey come as input_tokens, output_tokens, cache_read_input_tokens and cache_creation_input_tokens. Add dem together for each day, multiply am by the current rates, and compare the total with the invoice. Difference between the two usually na retries or code path wey you forget say you release.

Run the comparison with one week of real traffic instead of one sample prompt, and compare cost per completed task instead of cost per token. Model wey answers with one attempt at twice the rate fit cheaper pass model wey needs three attempts at half the rate. Cost per token na rate. Cost per completed task na your bill.

Set a hard spend ceiling before you leave anything running. Both platforms get spend limits for their consoles, and agent wey stick for retry loop fit spend one month budget overnight. Connect am the same way you go connect cost control for an always-on agent. If you dey build the first version on server wey you own, your first Claude API app on a VPS explains the key handling and request loop wey this calculation dey use.

FAQ

Claude API dey cheaper pass ChatGPT API?

E no dey always be so. For August 2026, frontier and workhorse tiers dey charge the same amount per input token for both sides, and Claude dey charge less per output token. So, Claude dey run a few percent cheaper for work wey get plenty output. For the small tier, OpenAI's gpt-5.6-luna costs one fifth of Claude Haiku 4.5 per input token. So, high-volume classification dey far cheaper for OpenAI. Calculate your own figure from your own token mix, because na your workload, not the price list, dey decide when the difference small like this.

Why the same words dey cost different number of tokens for each API?

Na because every model get its own tokenizer, and the model dey determine the token count. Anthropic document say Claude 4.7 and later models dey produce about 30% more tokens for the same text than Claude Sonnet 4.6 and earlier. Send your own text to each vendor's token counting endpoint. Divide one count by the other. Then multiply that ratio by the published rate before you compare. A 20% token difference dey outweigh most published rate differences.

Prompt caching fit ever make the bill bigger?

Yes, for Anthropic. A cache write costs 1.25 times the base input rate for a five-minute entry and 2 times for a one-hour entry, while a cache hit costs 0.1 times. If your traffic dey come in bursts so most entries expire before anything reads dem, you go pay the write premium and get no reads. Compare cache_creation_input_tokens with cache_read_input_tokens for your logs. A ratio near 1 means caching dey cost you money, so make the entry last longer or remove am.

I suppose run coding agent for the API or use subscription?

For one developer wey dey use am at ordinary volume, subscription seat usually costs less than metered API billing. This na because coding agent dey send large context again for every turn, and dem charge you for that context every time. Meter am for one week first. Then compare the API total with the seat price. API dey win when usage dey come in bursts, or when you need programmatic access wey a seat no give you.