MorphMorph
Morph Reflex

A decision, not a paragraph

Send a state and typed questions. Get a probability for every answer and zero output tokens. Text, JSON or images in, 64K context, $0.042/M input and $0.01/M cached.
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One POST, every answer typed

Three question types in one request: choice, score and noul (yes or no). No parsing, no retries on malformed output.

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curl https://api.morphllm.com/v1/systemone \
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  -H "Authorization: Bearer YOUR_API_KEY" \
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  -H "Content-Type: application/json" \
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  -d '{
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    "model": "reflex",
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    "state": "I have been trying to connect my Stripe account for 3 days and it keeps failing. I am losing sales. Please help ASAP.",
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    "questions": {
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      "department": {
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        "type": "choice",
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        "instructions": "Which team should handle this?",
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        "criteria": {
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          "billing": "Payment or subscription issues",
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          "technical": "Bugs or integration problems",
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          "sales": "Pricing or account questions"
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        }
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      },
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      "frustration": {
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        "type": "score",
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        "instructions": "How frustrated does the customer appear?",
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        "criteria": ["Calm, just stating facts", "Frustrated but civil", "Very angry, strong language"]
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      },
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      "is_urgent": {
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        "type": "noul",
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        "instructions": "The message conveys urgency or time sensitivity."
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      }
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    }
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  }'
Morph Reflex

Probabilities your code can act on

Three question types

choice picks an option. score returns the expected level across 2 to 10 ordered descriptions. noul returns the probability of yes. Name as many questions as you need; they share one state.

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"questions": {
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  "department": {
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    "type": "choice",
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    "instructions": "Which team should handle this?",
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    "criteria": {
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      "billing": "Payment or subscription issues",
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      "technical": "Bugs or integration problems",
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      "sales": "Pricing or account questions"
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    }
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  },
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  "frustration": {
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    "type": "score",
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    "instructions": "How frustrated does the customer appear?",
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    "criteria": [
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      "Calm, just stating facts",
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      "Frustrated but civil",
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      "Very angry, strong language"
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    ]
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  },
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  "is_urgent": {
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    "type": "noul",
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    "instructions": "The message conveys urgency."
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  }
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}

Probabilities, not prose

Every answer comes back as a distribution that sums to 1, plus a confidence measured against the model's full vocabulary. Output tokens: 0.

Docs
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"answers": {
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  "department": {
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    "type": "choice",
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    "choice": "technical",
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    "probabilities": {
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      "billing": 0.21,
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      "technical": 0.78,
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      "sales": 0.01
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    },
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    "confidence": 0.77
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  },
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  "is_urgent": {
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    "type": "noul",
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    "noul": 0.97,
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    "confidence": 0.96
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  }
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},
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"usage": {
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  "input_tokens": 312,
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  "output_tokens": 0
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}

Text, JSON or images

Put a ticket, a transcript or a JSON record in state. Add up to 16 PNG, JPEG or WebP images as base64 data URLs, 8 MB each. Same questions, same answer shape.

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{
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  "model": "reflex",
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  "state": "Satellite frame of an industrial building.",
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  "images": ["data:image/jpeg;base64,/9j/4AAQ..."],
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  "questions": {
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    "equipment": {
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      "type": "choice",
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      "instructions": "Is outdoor mechanical equipment visible?",
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      "criteria": {
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        "visible": "Equipment is visible.",
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        "not_visible": "No equipment is visible.",
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        "unclear": "Too unclear to tell."
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      }
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    }
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  }
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}

Cached state

Caching is automatic. Repeat a state over about 1,000 tokens and most of its input is reused at $0.01/M. Five measured production calls on one satellite frame, not a latency guarantee.

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request                      cached/input   time
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satellite image, first call      0 / 1328   890 ms
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same image again              1216 / 1328   220 ms
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switch to a blank image          0 / 1328   226 ms
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same blank image again        1216 / 1328   131 ms
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back to the satellite image   1216 / 1328   208 ms
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repeat calls reused 91.6% of input tokens

Act on the number

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Free tier available. Pay per input token only.