Classifier.dev: outperforms Jev and is free

Classifier.dev launched a free, keyless zero-shot text classification API that it says outperforms the Jev model it runs on, scoring 90.0% on AG News versus 87.5% for Jev alone and 62.7% on emotion versus 61.8%, measured live on 2026-09-18 across 400 items per public test set. The service's smart tier re-asks only the items Jev was unsure about, gaining +2.5 points on AG News (+18.4 on the 49 unsure items) and +1.0 point on emotion (+4.9 on the 122 unsure items), with no API key or account required. Classifier.dev also ships an npm CLI, classifier-dev, and machine-readable descriptions at openapi.json and llms.txt.

classifier.dev zero-shot text classification over plain HTTP — no API key, no account Give your agent this prompt Paste it into any coding agent. It installs the skill and teaches the agent to classify text through this API — no key, no setup. Set up the classifier.dev skill: run npx skills add https://classifier.dev , then read https://classifier.dev/skill.md and follow it all the way through. or open it in \ Claude Code\ claude-cli://open?q=Set%20up%20the%20classifier.dev%20skill%3A%20run%20%60npx%20skills%20add%20https%3A%2F%2Fclassifier.dev%60%2C%20then%20read%20https%3A%2F%2Fclassifier.dev%2Fskill.md%20and%20follow%20it%20all%20the%20way%20through. \ Codex\ codex://threads/new?prompt=Set%20up%20the%20classifier.dev%20skill%3A%20run%20%60npx%20skills%20add%20https%3A%2F%2Fclassifier.dev%60%2C%20then%20read%20https%3A%2F%2Fclassifier.dev%2Fskill.md%20and%20follow%20it%20all%20the%20way%20through. \ Cursor\ cursor://anysphere.cursor-deeplink/prompt?text=Set%20up%20the%20classifier.dev%20skill%3A%20run%20%60npx%20skills%20add%20https%3A%2F%2Fclassifier.dev%60%2C%20then%20read%20https%3A%2F%2Fclassifier.dev%2Fskill.md%20and%20follow%20it%20all%20the%20way%20through. \ Grok\ https://grok.com/?q=Set%20up%20the%20classifier.dev%20skill%3A%20run%20%60npx%20skills%20add%20https%3A%2F%2Fclassifier.dev%60%2C%20then%20read%20https%3A%2F%2Fclassifier.dev%2Fskill.md%20and%20follow%20it%20all%20the%20way%20through. Better than calling its own model directly The model behind this service is Jev. The smart tier re-asks only what Jev was unsure about, and comes out +2.5 points on AG News +18.4 on the unsure items , +1.0 points on emotion +4.9 on the unsure items . On 400 items a gap under about 5 points is noise; AG News clears it. Same public test sets, measured live over this API on 2026-09-18. No key, no cost. | | AG News | | emotion | | |---|---|---|---|---| | | all | unsure 49 | all | unsure 122 | | jev alone = classifier.dev fast | 87.5% | 65.3% | 61.8% | 36.9% | | classifier.dev smart | 90.0% | 83.7% | 62.7% | 41.8% | Try it curl https://classifier.dev/spam,not+spam/Win+a+free+iPhone spam Install the CLI npm i -g classifier-dev Then sort a file, one label ⇥ confidence ⇥ text line per input, in input order — a thousand lines a request, and rows appear as they land: classify bug,feature,praise < feedback.txt classify relevant,"not relevant" --review 0.7 < snippets.txt only the unsure ones Zero-shot text classification over plain HTTP. You send text and a list of labels, you get back the label that fits and how sure the model is. There is no API key to obtain and no account to create, so the example below works if you paste it right now. If you are an agent or a code generator, the machine-readable description of this API lives at https://classifier.dev/openapi.json https://classifier.dev/openapi.json OpenAPI 3.1 , with a short index at https://classifier.dev/llms.txt https://classifier.dev/llms.txt Against the model it runs on The model behind this service is Jev; the smart tier re-asks what Jev was unsure about. Same public test sets, 400 items each, measured live on 2026-09-18: AG News emotion all unsure 49 all unsure 122 ------------------------------------------------------------------------- jev alone = classifier.dev fast 87.5% 65.3% 61.8% 36.9% classifier.dev smart 90.0% 83.7% 62.7% 41.8% The fast tier is Jev, so one row serves both. Smart re-asked 43 / 126 of 400 items. Gaps under about 5 points are noise. The full table, with latency and cost, is at https://classifier.dev/benchmark https://classifier.dev/benchmark When this is worth a network call If you are a language model, you can already classify any text you can see, for free. So the question is never "can I classify this" — it is whether you want the text in your context at all. This is worth calling when reading the input is the expensive part: Filtering before reading. You have forty search results and want the six worth opening. Judging them yourself means pulling all forty into context first, which is the cost you were trying to avoid. One call returns forty labels and you read only the survivors. Cascade pre-filtering. Drop the obvious no's cheaply, then spend real reasoning on what is left. Streams nobody reads line by line. Log lines, error buckets, inbound tickets, the changed files in a large diff. Deterministic routing. A pipeline branch that must take the same path for the same input on every run, instead of drifting with a model's reasoning. The thread joining those: classify ten thousand things without reading them yourself. A thousand inputs go in one request and come back in about a second. Below about five items, skip it — you have already paid the context cost, so just decide. CLI The same API from the shell, one line per input, in input order: npm i -g classifier-dev classify bug,feature,praise < feedback.txt classify relevant,"not relevant" --review 0.7 < snippets.txt the unsure ones classify db,web,ml --count < titles.txt a histogram Plain lines, JSON or NDJSON in; label, confidence and text out. Batches of a thousand per request, four at a time, and rows stream as they land, so piping to head returns at once on a large file. Retries rate limits and upstream failures on its own. classify --help has the rest. Source in cli/ at https://github.com/mrmps/classifier-dev https://github.com/mrmps/classifier-dev MCP The same tools inside Claude, ChatGPT, Codex, Cursor or any MCP client, over Streamable HTTP with no key: https://classifier.dev/mcp classify texts, classify multi label, count labels, review uncertain https://classifier.dev/mcp/docs list docs, read doc, search docs claude mcp add --transport http classifier https://classifier.dev/mcp codex mcp add classifier --url https://classifier.dev/mcp Listed in the official MCP registry as dev.classifier/classifier and dev.classifier/docs: https://registry.modelcontextprotocol.io/v0/servers?search=dev.classifier https://registry.modelcontextprotocol.io/v0/servers?search=dev.classifier Claude.ai: Customize Connectors Add custom connector paste the URL. ChatGPT: Settings Security and login Developer mode, then create an app with the URL and "No Authentication". Step by step for every client, plus what each tool does: https://classifier.dev/mcp-setup https://classifier.dev/mcp-setup Agent skill Install this as a skill and your agent will remember to reach for it: npx skills add https://classifier.dev It is served straight from this domain over RFC 8615 well-known discovery, so there is no repository in the middle: /.well-known/agent-skills/index.json the discovery document /skill.md the skill itself, readable as-is Agents without a skills runtime can simply fetch /skill.md and follow it. Usage GET https://classifier.dev/{labels}/{text} GET https://classifier.dev/?labels={a,b}&text={text} POST https://classifier.dev {"input":"...","labels": "...","..." } POST https://classifier.dev {"inputs": "...", ...up to 1000 ,"labels": ... } Examples curl https://classifier.dev/spam,not+spam/Win+a+free+iPhone+now spam curl classifier.dev -d '{"input":"the checkout button does nothing","labels": "bug","feature","praise" }' {"label":"bug","confidence":1,"scores":{"bug":1,"feature":0,"praise":0},"model":"jev-1.13.0","ms":260} curl "classifier.dev/entailment,neutral,contradiction/Only+12+of+40+sites+were+inspected.+Every+site+was+inspected." contradiction Spaces can be written as + or %20, and labels are separated by commas. The same request as query parameters, for code that builds URLs: curl "https://classifier.dev/?labels=spam,not+spam&text=Win+a+free+iPhone+now"; spam input, q, classes and categories are read as text and labels too, and the two forms mix: /spam,not+spam?text=... is the same call. Every option below works on both. A malformed GET answers with a URL that would have worked. Parameters labels Two to one hundred categories. Required. input The text to classify, up to 32,000 characters. inputs Up to one thousand strings classified in a single call. tier Either fast the default or smart. See TIERS. instructions Extra criteria, such as "judge the reviewer's overall verdict". verbose On GET requests, ?verbose=1 returns JSON instead of a bare label. multi Return every category that applies instead of just one. max labels Cap how many multi-label answers come back. Results come back in input order. Each carries the label, a confidence from 0 to 1, a score for every label, and the model that answered. Batch responses also carry modelsUsed. The top-level model is "mixed" when different results were answered by different models, such as a smart-tier batch where only some inputs were escalated. Confidence The model behind this is not a language model prompted to classify. It is a decision model that returns a calibrated probability for every label, so the confidence is a real forecast of whether the label is right, measured: six-way emotion, 400 items confidence = 0.9 right 82% of the time confidence < 0.5 right 29% of the time four-way news topic, 400 items confidence = 0.9 right 92% of the time confidence < 0.5 right 64% of the time Use it. Act on high-confidence answers, and route the rest to a person, a reasoning model, or the smart tier, which does exactly that for you. Two things confidence does not measure. It is not out-of-distribution detection. It says which of your labels fits best, not whether any of them fit. "The weather is nice today" against bug / feature / praise is "praise" at 0.97. If none-of-the-above is a real outcome, add it as a label — the same text against those three plus "none of these" picks "none of these" at 0.78. That works; hoping for a low score does not. It is withheld for input that is not language. A forced choice on "asdkjfhaskdjfh" still lands somewhere, so the label ships with confidence and scores null and an unscored field explaining why. Multi-label One article, fifty tags, the ones that fit: curl classifier.dev -d '{"input":"...","labels": "ml","databases",... , "multi":true,"max labels":10}' {"results": {"labels": "databases","serverless","rust","caching", ... , "scores":{"databases":0.98,"serverless":0.98,...,"gaming":0.01}} } On GET, add ?multi=1 and the labels come back one per line. Every label is judged independently as a yes/no probability, and the answer lists those at or above 0.7, most likely first. The full score map is returned so you can set your own threshold: on a seven-task set, 0.7 gave recall 0.99 and precision 0.81 F1 0.887 ; 0.5 gave recall 1.00 and precision 0.74. max labels keeps the top N. One request, about 200ms. Tiers fast Every answer comes from the decision model, in one round trip. Measured: four-way news topic 87.7%, six-way emotion 60.5%, which is the same accuracy as a 3.4-second reasoning model on the news topics at two milliseconds per item. smart Same first pass, then every single-label answer below 0.7 confidence is re-asked of a fast reasoning model and replaced. Measured: emotion 61.8% to 63.7%, news topics 87.5% to 90.0%, by re-asking 30% and 12% of the items. Those results carry escalated: true and the reasoning model's name; the confidence and scores shown are still the decision model's, since they are why it was escalated. usage.escalated counts them. A few seconds per escalated item, so a batch on smart is slower in proportion to how uncertain it is. Multi-label answers ignore the tier: the reasoning model was measured re-judging them and made them worse. The models are not fixed. They are benchmarked as candidates appear and swapped when a measurement, not a launch post, says to. If the decision model is unavailable, requests of up to twenty inputs fall back to a chain of language models on different providers; JSON responses always report which model actually answered. Limits Limits are counted per IP address in classifications, not requests, so a batch of a thousand inputs spends a thousand of them. The fast tier allows 3,000 per minute and 20,000 per day; the smart tier 200 per minute and 2,000 per day. Each input is capped at 32,000 characters, and a request may carry up to a thousand inputs. Every response carries an X-RateLimit-Limit header and, where it can be determined, X-RateLimit-Remaining. Exceeding a limit returns 429 with a Retry-After header rather than a slow or silently dropped request. If you need more than this, or you want a classifier tuned to your own data, the fastest path is a short call: https://cal.com/michaelsf/coffee https://cal.com/michaelsf/coffee Privacy The text you send is never stored or logged here. It is forwarded to the model provider for the classification and nothing else. What gets recorded is the label names, which tier ran, which model answered, the latency, the response status and a coarse country, which is what makes the usage counts on this service possible. Built by @michael chomsky — https://x.com/michael chomsky https://x.com/michael chomsky