One context. Hundreds of questions, answered.
See every document from hundreds of perspectives, at the price of reading it once. spinf answers hundreds or thousands of questions about your content in a single pass, so every extra question costs almost nothing. Built for trading-grade analysis and already running in production.
$0.09 per million input tokens · first 50M tokens free · only input is billed · public beta
The right direction on 9 in 10 big crude moves
Thinking Text scored five years of news on spinf to build daily oil and equity indexes. Across the big 20-day moves of crude since 2023, the move implied by the news pointed the right way 92% of the time. A read that explains the market, not a forecast.
Read the storynews articles scored
tokens processed
of big crude moves
Look at the same content from every angle — and afford it
Most LLM pipelines ask one question per prompt. When you need a hundred perspectives on a million documents, that multiplies cost and latency. spinf is an inference engine built for the opposite shape: few documents in, many questions out.
Many questions, one read
Send the content once with as many questions as you need. Our engine reads the content a single time and answers every question from that read — no re-sending the article for each prompt.
Near-zero cost per extra question
You pay for the content plus the words of each question, nothing else. Going from 10 to 500 questions adds only the question tokens, not 490 more copies of the article.
Trading-grade rigour
Designed from the ground up for quantitative research: deterministic scoring, per-question probabilities and calibration signals. Already used in production for daily market indexes.
Scores you can calibrate
Every question returns a probability, a residual and an empty-content floor, so scores can be normalized and compared across questions, sources and time.
One article, many questions, one call
Write a question as a template that ends where the answer goes, and list the answers to score. spinf returns the probability of each answer for this content — plus two numbers that make the scores comparable.
OPEC+ agreed on Sunday to extend its voluntary output cuts through the first quarter, while Saudi Arabia signaled it could deepen reductions if prices keep sliding. Analysts noted that US crude inventories fell for a third straight week as refinery runs reached a seasonal high.
Live scores from spinf-12b (Gemma 4 12B). The tick marks the empty floor: the same question scored with no content. The distance between the bar and the tick is what the content says. A template with placeholders asks many questions at once (crude oil, natural gas). The residual is the probability left to other words, mostly set by the template: compare it across contents, not across questions.
The article is read once, not once per question
You are billed for the content plus the words of your questions — nothing else: no system prompt, no repeated context, no output tokens. The more questions you ask, the larger the saving.
Questions per article
100
Articles
1M
$9,918
98% less than one prompt per question
37× fewer tokens
65.4B tokens not sent
600-token articles, 12-token questions, 60-token instruction per conventional prompt; spinf bills at least 1,000 tokens per call. Compared with a 12B model at an average $0.15 per million input tokens; its output tokens are not counted, so the real saving is larger.
Numbers built for calibration
Scores are free: you only pay for input tokens. Each question comes back with three numbers, designed to be aggregated over thousands of documents and calibrated against a baseline.
pProbability
The probability of each answer you listed, read from the model’s next-word distribution at the end of your template — not a generated sentence to parse.
residualResidual
The probability that went to none of your answers. Mostly set by the template and the words you chose, so compare it across contents under the same question: a residual that jumps for one content flags a question that does not fit it.
floor_pEmpty floor
The same answer scored with empty content: the question’s built-in bias. Measure the content against it (p − floor_p), and questions become comparable.
{
"id": "req_01M3B14ABQ0S81CBK2WQ5HPNX9",
"object": "score",
"created": 1790297909,
"model": "spinf-12b",
"system_fingerprint": "fp_87dd99dbf6",
"results": [
{
"input_id": "0",
"queries": [
{
"id": "direction",
"combinations": [
{
"values": {
"commodity": "crude oil"
},
"options": [
{
"text": " rise",
"logprob": -2.813903,
"p": 0.654929,
"floor_p": 0.473377
},
{
"text": " fall",
"logprob": -3.454679,
"p": 0.345071,
"floor_p": 0.526623
}
],
"residual": 0.908432,
"floor_residual": 0.936882
},
{
"values": {
"commodity": "natural gas"
},
"options": [
{
"text": " rise",
"logprob": -2.390459,
"p": 0.444475,
"floor_p": 0.605271
},
{
"text": " fall",
"logprob": -2.167439,
"p": 0.555525,
"floor_p": 0.394729
}
],
"residual": 0.793942,
"floor_residual": 0.911571
}
]
},
{
"id": "supply_cut",
"combinations": [
{
"values": {},
"options": [
{
"text": " yes",
"logprob": -0.552571,
"p": 0.834359,
"floor_p": 0.435885
},
{
"text": " no",
"logprob": -2.169414,
"p": 0.165641,
"floor_p": 0.564115
}
],
"residual": 0.310287,
"floor_residual": 0.323976
}
]
},
{
"id": "topic",
"combinations": [
{
"values": {},
"options": [
{
"text": " oil",
"logprob": -2.843978,
"p": 0.990817,
"floor_p": 0.815715
},
{
"text": " natural gas",
"logprob": -7.552238,
"p": 0.008938,
"floor_p": 0.064336
},
{
"text": " coal",
"logprob": -11.14758,
"p": 0.000245,
"floor_p": 0.119949
}
],
"residual": 0.941267,
"floor_residual": 0.999882
}
]
}
]
}
],
"warnings": [],
"usage": {
"prompt_tokens": 148,
"completion_tokens": 0,
"total_tokens": 148,
"prompt_tokens_details": {
"content_tokens": 50,
"query_tokens": 49,
"floor_tokens": 49
},
"billed_tokens": 1000
}
}Built for many questions over many documents
Analyse archives in bulk on demand, or make decisions in live flows on a reserved endpoint, and combine them: one read answers all your questions.
15M news articles, turned into a market signal
Our partner Thinking Text built its oil and equity news indexes on spinf. Every article is scored against hundreds of prompts, and the answers are aggregated into daily indexes with their own hit rates.
15 million news articles over 5 years, read under thousands of perspectives
Close to 1 trillion tokens processed across the research iterations
A daily oil read that tracks the crude price level: correlation up to 0.85 in a year, 0.80 over the whole period
In production today: published every day, with a public score card
Oil news read vs WTI crude
Since mid-2022 · correlation 0.80 on 1064 market days
The spinf read (six fundamental questions, averaged over ~1,000 articles a day) against the log price minus its trailing 60-day median. Both series scaled by their own standard deviation. Data: Thinking Text OIL-F6, live.
One price, per million input tokens
No seats, no tiers, no output charge. Pay for what you send, keep the savings of reading once. Large projects and on-prem deployments are priced separately.
$0.09
/ 1M input tokens
- $4.50 of free credit to start: 50M tokens
- Only the tokens processed are billed: content, questions and answers
- Scores (the output) are free
- Up to 10,000 questions per content
- Content up to 4,096 tokens, questions up to 256 tokens
Questions, answered
Every question, at the price of one read
Our mission is to put the full power of the leading open-weights language models to work at hyperscale: we optimize them for your use cases, so analyses that used to be too slow or too expensive to run become routine. spinf is in beta: we are still tuning models and configuration to support wider use cases, and we would love to hear what you want to measure.