Best value · zero misses
GPT-6 Luna
49/49 correct · 100% accuracy
$0.001297 for all 49 tasks · $0.000026 per correct answer. No misses across all 7 categories.
AI Pricing Guru Labs · Experiment 01
We ran 20 models through the same 49 short, machine-graded tasks. The primary score is cost per correct answer. A model must score at least 95% to qualify, so a low price cannot compensate for too many wrong answers.
Live experiment 01
Question: for routine, short-form API work, which model produces correct answers at the lowest token cost? We divide each model’s total direct-price run cost by the number of correct answers. Lower is better—but only if the observed error rate is acceptable for your workflow.
The verdict
Best value · zero misses
49/49 correct · 100% accuracy
$0.001297 for all 49 tasks · $0.000026 per correct answer. No misses across all 7 categories.
Poor value on these simple tasks
48/49 correct · 98% accuracy
$0.11043 for the run · $0.002301 per correct answer, or 86.9× the best-value score. That does not make it a bad model; it means its premium was not justified by this workload.
Choose GPT-6 Luna for this exact task mix: it delivered the lowest cost per correct answer with zero observed misses. At the same prompt lengths and mix, one million correct answers would cost roughly $26. Do not extrapolate that estimate to longer or more complex workloads.
Same-scale comparison
Every bar uses the same linear $0–$2.4k axis, so the price gap is proportional rather than decorative. This projects each qualified model’s measured cost per correct answer to one million answers; lower is better.
GPT-6 Luna
100% accuracy
GPT-5.6 Luna
98% accuracy
DeepSeek V4.1 Flash
100% accuracy
Gemini 3.5 Flash-Lite
96% accuracy
DeepSeek V4 Pro
100% accuracy
GPT-6 Sol
100% accuracy
GPT-6.1 Sol
100% accuracy
Claude Sonnet 5
100% accuracy
GPT-5.6 Terra
100% accuracy
GPT-5.6 Sol
100% accuracy
GLM-5.2
100% accuracy
Claude Opus 4.8
100% accuracy
Gemini 3.6 Flash
100% accuracy
Claude Opus 5.5
98% accuracy
Grok 4.5
100% accuracy
Claude Opus 5
100% accuracy
Grok 4.6
100% accuracy
Kimi K3
100% accuracy
Claude Fable 5
98% accuracy
This is a straight-line scale-up of the observed 49-task mix, not a volume quote. It excludes models below the 95% quality threshold and should not be extrapolated to longer prompts or different workloads.
Cost-per-task leaderboard
Value rank #1
$0.000026
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #2
$0.000049
per correct
Perfect: Reasoning, Extraction, Classification, Summarization, Instruction, Pricing
Missed: Coding: 1 missed
Value rank #3
$0.000053
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #4
$0.000056
per correct
Perfect: Extraction, Classification, Summarization, Coding, Pricing
Missed: Reasoning: 1 missed · Instruction: 1 missed
Value rank #5
$0.00027
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #6
$0.000319
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #7
$0.000351
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #8
$0.000424
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #9
$0.000434
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #10
$0.000618
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #11
$0.000637
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #12
$0.000861
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #13
$0.001045
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #14
$0.001128
per correct
Perfect: Extraction, Classification, Summarization, Coding, Instruction, Pricing
Missed: Reasoning: 1 missed
Value rank #15
$0.001418
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #16
$0.001682
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #17
$0.002076
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #18
$0.002107
per correct
Perfect across all 7 categories. No misses in this run.
Value rank #19
$0.002301
per correct
Perfect: Reasoning, Extraction, Classification, Summarization, Instruction, Pricing
Missed: Coding: 1 missed
Below 95% threshold
$0.000145
per correct
Perfect: Extraction, Classification, Summarization, Coding, Pricing
Missed: Reasoning: 2 missed · Instruction: 1 missed
| Value rank | Model | Accuracy | What worked / missed | Run cost | Cost / correct | Vs. best |
|---|---|---|---|---|---|---|
| #1 | GPT-6 Luna | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.001297 | $0.000026 | Best |
| #2 | GPT-5.6 Luna | 98% 48/49 | Perfect: Reasoning, Extraction, Classification, Summarization, Instruction, Pricing Missed: Coding: 1 missed | $0.002371 | $0.000049 | 1.9× |
| #3 | DeepSeek V4.1 Flash | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.002589 | $0.000053 | 2× |
| #4 | Gemini 3.5 Flash-Lite | 96% 47/49 | Perfect: Extraction, Classification, Summarization, Coding, Pricing Missed: Reasoning: 1 missed · Instruction: 1 missed | $0.002625 | $0.000056 | 2.1× |
| #5 | DeepSeek V4 Pro | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.013223 | $0.00027 | 10.2× |
| #6 | GPT-6 Sol | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.015642 | $0.000319 | 12.1× |
| #7 | GPT-6.1 Sol | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.017222 | $0.000351 | 13.3× |
| #8 | Claude Sonnet 5 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.020784 | $0.000424 | 16× |
| #9 | GPT-5.6 Terra | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.021276 | $0.000434 | 16.4× |
| #10 | GPT-5.6 Sol | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.030284 | $0.000618 | 23.4× |
| #11 | GLM-5.2 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.031204 | $0.000637 | 24.1× |
| #12 | Claude Opus 4.8 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.04221 | $0.000861 | 32.6× |
| #13 | Gemini 3.6 Flash | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.051211 | $0.001045 | 39.5× |
| #14 | Claude Opus 5.5 | 98% 48/49 | Perfect: Extraction, Classification, Summarization, Coding, Instruction, Pricing Missed: Reasoning: 1 missed | $0.05416 | $0.001128 | 42.6× |
| #15 | Grok 4.5 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.069458 | $0.001418 | 53.6× |
| #16 | Claude Opus 5 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.082435 | $0.001682 | 63.6× |
| #17 | Grok 4.6 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.101702 | $0.002076 | 78.4× |
| #18 | Kimi K3 | 100% 49/49 | Perfect across all 7 categories. No misses in this run. | $0.103251 | $0.002107 | 79.6× |
| #19 | Claude Fable 5 | 98% 48/49 | Perfect: Reasoning, Extraction, Classification, Summarization, Instruction, Pricing Missed: Coding: 1 missed | $0.11043 | $0.002301 | 86.9× |
| Below 95% | Claude Haiku 4.5 | 94% 46/49 | Perfect: Extraction, Classification, Summarization, Coding, Pricing Missed: Reasoning: 2 missed · Instruction: 1 missed | $0.006656 | $0.000145 | 5.5× |
Models need at least 95% accuracy (47/49) to qualify. Qualified models are ranked by run cost ÷ correct answers. “Vs. best” shows how many times more each correct answer cost than the winner. Models below the quality threshold remain visible without receiving a value rank.
Methodology
Every model receives the same system prompt and user tasks through OpenRouter with temperature 0, top_p 1, and a 1024-token output ceiling so reasoning tokens do not truncate the final answer.
Answers are checked with exact, numeric, regex, contains, and JSON-field graders. This avoids paying another model to make subjective scoring decisions.
OpenRouter is the inference route. Cost is calculated from the direct-provider list prices in AI Pricing Guru’s daily pricing dataset, using reported prompt and completion tokens.
This compact suite does not measure long-context reliability, safety, creative writing, production latency, tool use, or every coding workload. Results are a workload signal, not a universal model ranking.
Coverage decisions
We keep availability, scope, and reproducibility decisions in a separate archive so this page stays focused on measured results. Each note links to its supporting evidence and buyer-impact analysis.
Return only the integer answer. A batch job processes 18 files per minute for 7 minutes, then 12 files per minute for 5 minutes. How many files total?
Return only the integer answer. A model costs $0.30 per 1M input tokens and $2.50 per 1M output tokens. What is the cost in cents for 100,000 input tokens and 20,000 output tokens?
Return only the integer answer. If a 64,000-token context window is filled to 75%, how many tokens are used?
Return only the integer answer. A provider raises output price from $4/M to $6/M. What is the percent increase?
Return only the integer answer. A cache hit costs 10% of normal input price. Normal input is $2/M. What is the cache-hit price in cents per 1M tokens?
Return only the integer answer. A crawler finds 9 new models Monday, 14 Tuesday, and removes 5 duplicates. How many unique new models remain?
Return only the integer answer. A benchmark has 50 tasks. A model gets 37 correct. What is the accuracy percentage rounded to the nearest whole number?
Return only the decimal answer with no currency symbol. If a run spends $0.42 and gets 14 correct answers, what is dollars per correct answer?
Return only JSON with numeric values for prices and context, without currency symbols or units. Text: 'Gemini Flash: input $0.30/M, output $2.50/M, context 1,048,576 tokens.' Extract fields model, inputPerM, outputPerM, context.
Return only JSON. Text: 'Z.ai GLM-5.2 charges $1.40 per million input tokens and $4.40 per million output tokens.' Extract provider, model, inputPerM, outputPerM.
Return only the single word immediately before 'plan'. Sentence: 'For heavy coding, the Pro plan is cheaper than per-token API use after roughly 35 million output tokens.'
Return only JSON. Log line: '2026-07-20 model=deepseek-v4-flash prompt=120 completion=38 latency_ms=910 ok=true'. Extract model, prompt, completion, latency_ms, ok.
Return only a comma-separated list of model IDs in their original order. Text: 'Tested: gpt-5.6-luna; claude-sonnet-5; gemini-2.5-flash.'
Return only JSON. Text: 'Anthropic Claude Haiku 4.5: $1 input, $5 output, active.' Extract provider, model, status.
Return only one label: pricing, launch, outage, or tutorial. Headline: 'OpenAI cuts GPT-5.6 Luna output token price by 20%'.
Return only one label: pricing, launch, outage, or tutorial. Headline: 'DeepSeek introduces V4 Flash for low-latency chat apps'.
Return only one label: pricing, launch, outage, or tutorial. Headline: 'Claude API errors spike across us-east for 42 minutes'.
Return only one label: pricing, launch, outage, or tutorial. Headline: 'Step-by-step guide to add streaming responses to a chatbot'.
Return only yes or no. Query: 'cheapest ai api per token'. Is this query primarily cost/comparison intent?
Return only yes or no. Query: 'what is a transformer neural network'. Is this query primarily pricing intent?
Summarize in exactly 8 words and include these exact words: cheaper, extraction, failed, arithmetic. Text: 'The cheaper model answered all extraction tasks correctly but failed most multi-step arithmetic, making it a good fit for structured parsing rather than reasoning.'
Return these three search phrases exactly as written, lowercase and separated by commas with no extra text: ai api pricing, cheapest ai api, token calculator. Text: 'Rising searches include ai api pricing, cheapest ai api, and token calculator, all pointing toward cost-conscious developer intent.'
Return one sentence under 12 words and include the exact words verbosity and cost. Text: 'A model with high accuracy but very high output verbosity can lose on dollars per correct answer because every extra token compounds cost.'
Return only a title under 7 words that includes the exact words cost and correct. Text: 'We compare leading AI models by how much each correct benchmark answer costs, not just accuracy.'
Return exactly one sentence that includes OpenRouter and the exact phrase list prices. Text: 'OpenRouter is used for routing, but costs are computed from AI Pricing Guru's direct-provider list prices so the metric reflects what API buyers see on provider pricing pages.'
Return only the output. JavaScript: const xs=[3,1,4,1,5]; console.log(xs.filter(x=>x>2).reduce((a,b)=>a+b,0));
Return only the output. Python: prices={'in':0.3,'out':2.5}; print(round(prices['in']*2 + prices['out']*0.1, 2))
Return only the missing JavaScript expression. Complete: const cost = (inputTokens * ___ + outputTokens * outputPerM) / 1_000_000;
Return only JSON. For rows [{ok:true,cost:0.02},{ok:false,cost:0.03},{ok:true,cost:0.01}], compute correct, totalCost, costPerCorrect.
Return only the output. JavaScript: console.log(['gpt','claude','glm'].map(s=>s.length).join('-'));
Return only the called function name immediately after await. Snippet: async function call(){ const r = await fetch(url, opts); return r.json(); }
Return only the third item alphabetically: Luna, Flash, Sonnet, Grok.
Return only the word that appears twice: token, price, model, token, output.
Return only the reversed string: glm-5.2
Return only the uppercase acronym from: cost per correct answer.
Return only a comma-separated list of the two providers with names starting with G: OpenAI, Google, Anthropic, Groq, Mistral.
Return only valid or invalid. JSON string: {"model":"gpt","cost":0.01}
Return only valid or invalid. JSON string: {model:"gpt",cost:0.01}
Return only the smallest price: $1.25/M, $0.30/M, $2.00/M, $0.14/M.
Return only the final model ID. Chain: start=gpt-5; replace gpt with glm; append .2; prefix z-ai-.
Return only the count of unique providers: OpenAI, Anthropic, OpenAI, Google, Z.ai, Google.
Return only cheaper or expensive. Model A costs $0.10/M input and $0.30/M output. Model B costs $0.30/M input and $2.50/M output. For any positive input/output workload, Model A is what relative to Model B?
Return only the model name. A got 10 correct for $0.20. B got 8 correct for $0.08. Which has lower dollars per correct answer?
Return only the integer answer. If output tokens are 4x more expensive than input tokens, how many input-token equivalents is 250 output tokens?
Return only JSON. A run used 1000 prompt tokens and 500 completion tokens. Prices are $1/M input and $6/M output. Compute inputCost, outputCost, totalCost in dollars.
Return only yes or no. If a model is free for input but charges for output, can a verbose wrong answer still cost money?
Return only the ratio as N:1. Output costs $15/M and input costs $3/M.
Return only the integer answer. A $5 pilot budget has already spent $1.75. How many cents remain?
Return only the best metric name: cost per prompt, cost per correct answer, or total tokens. We need to compare accuracy and spend together.
Coming next
When does a fixed-price coding subscription beat paying per successful API task?
How does cost per correctly retrieved fact change as context grows?
Does a model’s cost per correct answer worsen over time without a price change?
Allow optional product analytics and session replay to help improve our pricing tools? Anonymous identifiers stored in your browser connect visits across pages. Replay shows page interactions; input values are masked and forms are excluded. Privacy policy