TypeLLM is live · pricing from $0.05/mo · last verified 5 Nov 2026 · viability healthy
TypeLLM
TypeLLM turns text and images into typed, schema-conformant values in one workflow API call — no JSON repair loops.
If your pipeline ends in a Pydantic model or a JSON parser with a retry loop, TypeLLM is worth a serious look — the per-field thinking switch and confidence-aware when conditions delete real orchestration code. Read the benchmark honestly: 98.70% on JevBench is the thinking-on number across 231 tasks, and getting there costs $0.50/M thinking tokens against $0.05/M for input.
Verdict generatedVerified 9d ago · liveness 82/100 · cite: rightaichoice.com/tools/typellm
- Backend and ML engineers extracting typed fields from receipts, invoices and forms
- Teams self-hosting open LLMs on SGLang that need schema-conformant output
- Classification pipelines that branch on probabilities and confidence thresholds
- Vision workflows needing bounding boxes plus typed labels from one call
- Non-technical users who want a point-and-click hosted extraction UI
- Teams with no GPU infrastructure and no appetite for self-hosting SGLang
- Projects whose output schema is invented fresh on every single call
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Skip TypeLLM if your output schema is different on every call or you have no way to serve a model — the typed-field design pays off only when the questions are known up front and you can point it at a hosted key or your own SGLang deployment.
Thinking-billed at $0.50/M versus $0.05/M for input, so a pipeline where every field reasons costs ten times the context rate for those tokens.
At $0.05/M input tokens with typed outputs free, TypeLLM is cheap per call but meter-shaped: a solo engineer prototyping receipts spends the $5 credit for weeks, while a production classification pipeline that thinks on every field pays ten times the rate on those tokens. Compare against per-seat structured-output tiers in hosted LLM platforms, where you pay a flat seat price but own the retry logic.
In short
TypeLLM — TypeLLM turns text and images into typed, schema-conformant values in one workflow API call — no JSON repair loops. Best for Backend and ML engineers extracting typed fields from receipts, invoices and forms, Teams self-hosting open LLMs on SGLang that need schema-conformant output, Classification pipelines that branch on probabilities and confidence thresholds. Plans from $0.05/mo.
What is TypeLLM?
TypeLLM — TypeLLM turns text and images into typed, schema-conformant values in one workflow API call — no JSON repair loops. Best for Backend and ML engineers extracting typed fields from receipts, invoices and forms, Teams self-hosting open LLMs on SGLang that need schema-conformant output, Classification pipelines that branch on probabilities and confidence thresholds. Plans from $0.05/mo.
TypeLLM is a Python client plus hosted HTTP API that turns open autoregressive models into a typed extraction engine. You send one block of context — text, images, or both — alongside questions written as JSON Schema fields, each with a type, an optional enum, and a plain-English instruction. Generation is constrained so answers arrive as values your code consumes directly, not strings you parse and retry. Arrays now hold up to 200 items per call, and enums inside array items accept up to 512 values, so big multi-label passes stay in a single request. Output types run from primitives (string, integer, number, boolean, enum) through composition (object, array, array[object]) plus null for absence. Box fields return an object's edges in an image normalized 0 to 1, and arrays of boxes find every match or return [] when none exists — both shipped in 0.7.1 and 0.7.2. Version 0.7.3 sped up image calls that share context prefixes, like repeated instructions across document pages. Thinking is controlled per field: true, or auto so hard questions reason first while easy ones answer immediately. Version 0.7.0 lets a user-sourced question pause for a database, API or person: the hosted API returns needs_input until client.resume() continues. Fields can return decision probabilities you branch on, and when conditions test a confidence threshold before later fields run. Parallel fields with depends_on ordering keep independent questions moving together. TypeLLM bills context once instead of once per step, and its own support-ticket comparison puts one call at 149 input tokens and 1.38s against 4 calls, 320 tokens and 2.60s for a chat-plus-decision-model stack. It targets backend and ML engineers wiring extraction, classification and tool-call pipelines, and sits apart from chat-first LLM APIs by making the workflow, not the message, the unit of inference.
Key features
- JSON Schema field definitions with plain-English per-field instructions
- Guaranteed output types: string, integer, number, boolean, enum
- Arrays up to 200 items per call, up from 50 (0.7.3 era)
- Enums and choices accept up to 512 values with probabilities and confidence
- Enum arrays take 512 values and array items may repeat
- Per-field thinking mode: thinking true or auto
- Text and image input read by every field in one call
- Box fields returning image edges normalized 0 to 1
- Arrays of boxes finding every match, returning [] for none
- Object groups and array[object] for lists of typed records
- Permutation-invariant decision probabilities, averaged over 8 orderings past 8 options
- when conditions gating fields on a confidence threshold (0.6.7)
- User-sourced questions pause for a database, API or person, resumed with client.resume() (0.7.0)
- Shared context-prefix optimization for multi-page image calls (0.7.3)
- Python client plus hosted HTTP API, with self-hosted generate() on SGLang
Behind the verdict
The pitch is narrow and that's the point: one call, typed out, no repair loop. Where it earns its place is any pipeline you've already built around validation and retry — invoice fields, receipt line items, classification with a confidence gate. Rewriting a three-call chain into one declarative schema usually pays off in both latency and tokens, and the support-ticket numbers (149 tokens, 1.38s versus 320 and 2.60s) are the kind of gap you notice on a dashboard. Pick this when your output shape is known in advance and stable. A fixed Pydantic model plus per-field instructions is exactly the input TypeLLM wants, and the newer limits matter if you were sizing around 50-item arrays or 26-value enums — those caps moved to 200 items and 512 values. Image-heavy workflows got two useful additions in 0.7.1 and 0.7.2: normalized box edges and arrays of boxes that return every match, or [] when the page has none. 0.7.3 then shaves time on repeated instruction prefixes across document pages. Pass when the schema is genuinely invented on every call. TypeLLM's advantage comes from constraining generation to a declared shape; if a human rewrites the questions each run, you're paying schema overhead for nothing. Same for conversational products — this is not a chat interface, and it isn't aimed at one. Also think about where the work actually runs. Self-hosting on SGLang means GPUs and the ops that come with them; the hosted API means per-token billing where thinking is ten times the input rate. If most fields think, the $0.05 versus $0.50 split stops looking cheap, so set thinking per field rather than globally and let auto handle the easy ones. The closest alternative is just calling a general chat model with JSON mode bolted on. That works until you need a confidence threshold
Models under the hood
as of 2026-11-14
Limitations
- TypeLLM is consumed via a Python client (pip install typellm) or the hosted HTTP API at api.typellm.ai, with self-hosted deployment available on the API platform tier.
- Pricing is usage-based: input tokens at $0.05/M, thinking tokens at $0.50/M (only for fields that think or where auto elects to), and typed outputs are free; new users get $5 to start.
- Accuracy figures (84.42% without thinking, 98.70% with thinking) are self-reported on the vendor's own JevBench benchmark of 231 public tasks.
- The headline 53%-cheaper and 1.9x-faster claims are measured only on a single support-ticket workflow.
as of 2026-10-18
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Is TypeLLM still active?
TypeLLM is live · pricing from $0.05/mo · last verified 5 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is TypeLLM? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in TypeLLM
Checked 6 days agoAcross the latest 10 updates: 7 feature updates, 2 changelog entries and 1 community discussion.
TypeLLM 0.7.3 speeds image calls with shared context prefixes
Version 0.7.3 speeds image calls that share context prefixes, such as repeated instructions across document pages, and multi-question calls on a single image.
TypeLLM arrays expand to 200 items per call
Arrays of strings, numbers, enums and booleans now accept up to 200 items per call, up from 50. String arrays keep repeated items.
Enum arrays take 512 values; array items may repeat
Enums inside array items now accept up to 512 values, up from 26. Array items may repeat, so boolean arrays return every answer. Number, enum and boolean arrays write faster.
The workflow model
TypeLLM published an Insights post on its workflow model, outlining how structured extraction and tool steps compose in production pipelines.
TypeLLM 0.7.1 adds box fields with normalized edges
A box field locates an object in an image and returns its edges as top, bottom, left and right coordinates scaled from 0 to 1.
TypeLLM 0.7.2 adds arrays of boxes for image matching
Array of box schemas finds every match in an image and returns an empty array when none is found.
TypeLLM 0.7.0 adds user-source questions and resume
Questions with source user are answered outside the model. The hosted API returns status needs_input and client.resume() continues with the answer; self-hosted generate() accepts given and skipped fields.
Enums and choices take 512 values on hosted API
Hosted API enums and choices accept up to 512 values with probabilities and confidence. Past 8 options, auto permutations average 8 orderings instead of one per option.
TypeLLM 0.6.8 raises enum cap to 26 values
Enums, choices and score levels accept up to 26 values, up from 24. A single-value enum or choice now raises SchemaError.
TypeLLM 0.6.7 gates fields on answer confidence
A when clause can test a field's confidence, running subsequent fields only when the answer passes a threshold. Requires return_probabilities.
What do users say about TypeLLM?
What people actually say about TypeLLM — is it worth it?
We scanned public community sources for TypeLLM on Sep 28, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 1 of the posts we fetched could be positively tied to TypeLLM. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about TypeLLM — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 5 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified TypeLLM 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed limitations
- — changed description, pricing tiers, features, summary, our verdict, who should skip it
- — changed pricing page
- — changed features, description, who should skip it, pricing tiers, our verdict, who it suits, summary
- — changed our verdict, limitations, pricing tiers, switching in, description, summary, who should skip it, setup time, features, who it suits, pricing fit, switching out, hidden costs, use cases, models, pricing page
- — changed pricing tiers, who should skip it, summary, description, our verdict, who it suits, features, integrations
Free to cite with attribution — this page re-verifies continuously.
How much does TypeLLM cost in 2026?
TypeLLM: Plans from $0.05/mo. Pricing last verified 5 Nov 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where TypeLLM's pricing actually pencils out — and where peers do it cheaper.
At $0.05/M input tokens with typed outputs free, TypeLLM is cheap per call but meter-shaped: a solo engineer prototyping receipts spends the $5 credit for weeks, while a production classification pipeline that thinks on every field pays ten times the rate on those tokens. Compare against per-seat structured-output tiers in hosted LLM platforms, where you pay a flat seat price but own the retry logic.
Has TypeLLM's pricing changed?
No plan price for TypeLLM has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick TypeLLM?
Pick TypeLLM if you are Backend and ML engineers extracting typed fields from receipts, invoices and forms, Teams self-hosting open LLMs on SGLang that need schema-conformant output, Classification pipelines that branch on probabilities and confidence thresholds, Vision workflows needing bounding boxes plus typed labels from one call.
Look elsewhere if you are Non-technical users who want a point-and-click hosted extraction UI, Teams with no GPU infrastructure and no appetite for self-hosting SGLang, Projects whose output schema is invented fresh on every single call, Workflows where most fields think, making the $0.50/M thinking rate eat the input savings.
Skip it if Skip TypeLLM if your output schema is different on every call or you have no way to serve a model — the typed-field design pays off only when the questions are known up front and you can point it at a hosted key or your own SGLang deployment.
GeneratedReal-world workflow fit
Concrete scenarios for the personas TypeLLM actually fits — and what changes day-one when you adopt it.
You send receipt text and an image into one generate() call with merchant, total, expense_type (enum of meal/travel/equipment) and reimbursable as typed fields.
Outcome: The response.result is already a dict with a float total and a Python bool, so you write it straight to the database and skip the parse-retry loop entirely.
A 50-class taxonomy is declared as an enum field with return_probabilities, and a when condition tests the confidence threshold to route uncertain documents to review.
Outcome: Permutation averaging past 8 options keeps the class order from skewing probabilities, and the confidence score decides the route in the same call.
Tool-call arguments are schema fields with depends_on ordering, and a field with source user pauses for a database lookup mid-generation.
Outcome: The hosted API returns needs_input instead of a guessed argument, and client.resume() continues the same generation once the lookup returns.
Use cases
- Extract merchant, total, expense type and reimbursability from receipt text into typed fields
- Classify expenses into a fixed enum taxonomy with a calibrated confidence value
- Generate strictly typed tool-call arguments for an agent without JSON repair loops
- Parse invoices into integer quantities, float totals and boolean paid flags
- Find every matching object in an image with an array of boxes, [] when none
- Pause a generation mid-flow so a database, API or person can answer before continuing
- Build extraction graphs where later fields depend on earlier constrained choices
- Score multiple-choice decisions using permutation-invariant probabilities
Setup time & first value
How long it actually takes to get something useful out of TypeLLM — broken out by persona, not the marketing-page minute.
With an API key from the dashboard, pip install -U typellm gets a Python client running in under 15 minutes — the docs include a receipt example you can paste. Self-hosting on SGLang takes longer: plan an afternoon for model serving, then point the client at your endpoint. The docs invite you to paste the SKILL.md URL into Claude Code or Cursor to scaffold the setup.
Switching to or from TypeLLM
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a Pydantic-plus-OpenAI retry harness: replace the parse-and-retry loop with JSON Schema fields in one generate() call, then delete the validator.
- →From raw SGLang structured output: keep serving the same model, send your grammar or schema as TypeLLM fields and pick up typed results plus probabilities.
- →From a chat-model extraction prompt: collapse the multi-step prompt chain into parallel fields with depends_on where ordering actually matters.
- ↗To a self-hosted SGLang endpoint: the docs link self-hosting on GitHub, and the same schema fields run against your own model.
- ↗To a proprietary model API: call it directly and reintroduce JSON validation, since TypeLLM's constraint layer is what you would be leaving behind.
Integrations
Resources & guides
- Documentationtypellm.ai
Docs · TypeLLM
Full product docs from typellm.ai
- Examplestypellm.ai
Examples · TypeLLM
Working sample projects from typellm.ai
- Resourcetypellm.ai
Cookbook · TypeLLM
Helpful link from typellm.ai
- Resourcetypellm.ai
Blog · TypeLLM
Helpful link from typellm.ai
- Resourcetypellm.ai
SKILL · TypeLLM
Helpful link from typellm.ai
Tutorials & Learning
YouTube returned 6 videos for “TypeLLM”, and we withheld 6: 6 could not be judged, because “TypeLLM” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about TypeLLM.
Official links
What are the best TypeLLM alternatives?
The closest alternatives to TypeLLM we list are DiscuroAI, LLMTornado and Openai Scala Client. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| DiscuroAI | Freemium | Pricing not published | — |
| LLMTornado | Freemium | Pricing not published | — |
| Openai Scala Client | Free | Free | — |
Alternatives to TypeLLM
View all
DiscuroAI
DiscuroAI chains GPT-3 prompt templates and DALL-E 2 image calls into multi-step workflows you run with one API request that returns JSON.
LLMTornado
One strongly-typed C# .NET client for calling 15+ LLM providers through a unified TornadoApi.
Openai Scala Client
OpenAI Scala Client: typed, effect-friendly JVM calls to OpenAI's chat, image, embedding and audio APIs
Tools that pair well with TypeLLM
Common stack mates teams adopt alongside TypeLLM, with the specific reason each pairing earns its keep.
DiscuroAI
DiscuroAI chains GPT-3 prompt templates and DALL-E 2 image calls into multi-step workflows you run with one API request that returns JSON.
LLMTornado
One strongly-typed C# .NET client for calling 15+ LLM providers through a unified TornadoApi.
Openai Scala Client
OpenAI Scala Client: typed, effect-friendly JVM calls to OpenAI's chat, image, embedding and audio APIs
TypeLLM vs its closest rival
TypeLLM's closest head-to-head is with Unsloth — These two live at opposite ends of the self-hosted LLM stack, and you shouldn't treat them as substitutes. Unsloth is the thing you reach for when you want to train and serve a model on your own GPU — its 2x-faster/60%-less-VRAM pitch, Dynamic 3.0 quants, and no-code Desktop app target anyone with a consumer card and a privacy budget. TypeLLM is what you reach for after that, or beside it, when the model's output has to be a boolean, integer, or enum rather than free text — it constrains generation instead of parsing it. If your problem is 'I need a fine-tuned local model,' pick Unsloth. If it's 'my model returns a stringly-typed mess in a pipeline,' pick TypeLLM. Buyers shortlisting one rarely shortlist the other. Verdict generated
Head-to-head comparisons
Typellm vs Unsloth
These two live at opposite ends of the self-hosted LLM stack, and you shouldn't treat them as substitutes. Unsloth is the thing you reach for when you want to train and serve a model on your own GPU — its 2x-faster/60%-less-VRAM pitch, Dynamic 3.0 quants, and no-code Desktop app target anyone with a consumer card and a privacy budget. TypeLLM is what you reach for after that, or beside it, when the model's output has to be a boolean, integer, or enum rather than free text — it constrains generation instead of parsing it. If your problem is 'I need a fine-tuned local model,' pick Unsloth. If it's 'my model returns a stringly-typed mess in a pipeline,' pick TypeLLM. Buyers shortlisting one rarely shortlist the other.
GeneratedTypellm vs Predibase
These are not competing products and you should not choose between them. Predibase is a managed platform where you pay to fine-tune and serve open models — its value is infrastructure removal and cheap LoRAX multi-adapter inference. TypeLLM is a self-hosted library you bolt onto an SGLang-served open model to guarantee typed outputs per field, paying with your own GPUs. A team could use both (TypeLLM on a Predibase-served model, if the endpoint is OpenAI-compatible), but that would be a stack decision, not a comparison. Buy based on the problem: training and serving at scale → Predibase; schema-guaranteed extraction on hardware you already run → TypeLLM.
GeneratedTypellm vs Resistant Ai
These are not competitors and nobody should be choosing between them. Resistant AI sells a fraud-decision system to risk teams at regulated financial institutions — document forgery checks, KYB/claims/tenant vetting, and 80+ transaction-monitoring models layered on existing rules. TypeLLM is a developer tool for engineers who already run open autoregressive models on SGLang or vLLM and want typed values instead of parsed free text. If you have a fraud problem, buy Resistant AI; if you have a stringly-typed extraction pipeline, use TypeLLM. The only surface where they touch is if a fraud team builds its own extraction stack — and even then Resistant AI is a purchase, TypeLLM is a component.
GeneratedTypellm vs Mirascope
These are not rival frameworks so much as different layers of the same Python stack, and the honest pick depends on one question: do you control the model? If you're calling OpenAI, Anthropic, or Google and need agents, tool loops, prompt versioning, and per-call cost tracking, pick Mirascope — it gets you to production without owning GPUs. If you self-host open autoregressive models on SGLang and your pain is stringly-typed extraction from receipts, invoices, or forms, pick TypeLLM — its whole reason to exist is guaranteeing boolean, integer, number, and enum outputs instead of parseable text. Teams with GPUs and structured-extraction workloads should seriously consider running both: Mirascope for orchestration and vendor routing, TypeLLM for the fields that must come back typed. If you have no GPU appetite and no SGLang deployment, TypeLLM isn't a realistic starting point today.
GeneratedTypellm vs Klippa
These two should not be on the same shortlist. If you are a finance, HR, logistics, legal, or fintech team that needs invoices, receipts, IDs and shipping documents turned into structured data with verification, fraud detection, liveness/NFC checks and ERP push into Exact, AFAS, NetSuite, SAP or Visma, Klippa (Doxis) is purpose-built for you and TypeLLM offers you nothing. If you are a backend or ML engineer already serving open autoregressive models on SGLang and you want enums, booleans and integers back instead of parseable text — with parallel fields, depends_on ordering and per-field thinking — TypeLLM solves a problem Klippa does not touch. Choose by which problem you actually have, not by which product looks cheaper, because neither has public list pricing to compare.
GeneratedTypellm vs Marvin
Pick Marvin if you want to bolt LLM intelligence onto an existing Python codebase this week — it's free, uses the OpenAI/Anthropic keys you already have, and Pydantic-style typed outputs plus agent loops, streaming and retries cover most product work. Pick TypeLLM only if you're already serving open models on SGLang and your bottleneck is guaranteed schema conformance, probabilities and compute cost — it's the more specialized tool with no list price and no recent Marvin-side news to match its rapid 0.1.x/0.2.x cadence. If you don't run GPUs or can't get a TypeLLM quote, that decision is already made for you.
GeneratedFrequently asked questions
Best-of guides
For makers of TypeLLM
- Get a Verified Listing for TypeLLM ($39): a written decision within 1 working day.
- Free listing for another tool: Free reviews are decided in order, within 3 weeks of the review starting.
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