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Products that call Jev, including routers, CLIs, warehouses, and SQL functions. 127 stories.
OR-decision-4B prints JevBench 193 of 231, and Choice 79.7 against Jev 79.6 on alphaXiv's chart
alphaXiv posted OR-decision-4B on October 30, 2026, at 16:06 UTC. The Hugging Face card prints JevBench 193 of 231, 83.55%. The launch chart prints Choice 79.7 against Jev 79.6, with sarcasm at 53.6 against Jev 70.7. Apache-2.0 LoRA on Qwen3.5-4B. This desk did not run the weights.
BranchOne returns Choice probabilities from Qwen3.5-0.8B-Base, median 0.305 s on ten short text questions
Necati Demir posted BranchOne on October 29, 2026. The open checkpoint returns one probability per supplied candidate from Qwen3.5-0.8B-Base plus LoRA. His A100 Colab run prints median 0.305 s on ten short text questions. Apache-2.0. This desk did not run the weights.
MiniCPM5-2B-Jev prints 78.79% on JevBench, first among open models at or under 2B
OpenBMB posted MiniCPM5-2B-Jev on October 28, 2026, at 13:01 UTC. The project prints 78.79% on JevBench, first among open models at or under 2B, and 86.41% accuracy with ECE 0.0307 on decision-v7. Apache-2.0 on MiniCPM5-2B. This desk did not run the weights.
Opened October 26, Decision Index 0.4.1 Full score for d3-ultra is 70.18, second of 229
Xunzhuo Liu posted d3-ultra on October 27, 2026, at 03:01 UTC. Opened October 26, Decision Index 0.4.1 Full score is 70.18, second of 229, +3.03 over d3 at 67.15. The card's private SR-Text mean is 87.2. Apache-2.0. This desk did not run the weights.
hunch runs EmbeddingGemma2 as a CPU System One endpoint at 20 ms on text and 100 ms on images
Matt Mastracci's hunch turns EmbeddingGemma2 into a CPU System One endpoint at 20 ms on text and 100 ms on images. Cloudflare Clef teaches a local student in real time. Open-source launch was planned for later the same week. This desk has no public repo or hosted call yet.
Abe's 1,774 Snowflake posts cost $0.055 on Jev Noul against $1.87 on AI_FILTER
ABE Masanori posted on October 24, 2026, at 11:20 UTC. On 1,774 of his 2025 X posts, Jev Noul cost about $0.055 against AI_FILTER at about $1.87, about 1/34. AUC against AI_FILTER was 0.9685. Of 70 splits where Jev scored under 0.2 and AI_FILTER was TRUE, he judged Jev right on 54. Date headers 08:49:12 GMT to 08:49:40 GMT October 26, this desk's jev-1.14.0 noul on his Lightdash example was 0.04.
Base Labs posts mammoth-1 at Decision Index 0.4 Full 71.24; live 0.4.1 prints 68.4
Base Labs posted mammoth-1 on October 21, 2026, at 17:14 UTC, and the card prints Decision Index 0.4 Full 71.24, first of 208. Opened October 23, the live space is edition 0.4.1, 229 models, mammoth-1 Full 68.4 rank 3. Date headers 02:23:21 GMT to 02:23:23 GMT, GET /v1/models with no key returned HTTP 401.
Sthānika's Sieve-31B card prints Decision Index 0.3.2 Full score 63.8
Cuth posted Sieve 31B on October 20, 2026. The Hugging Face card prints Decision Index 0.3.2 Full score 63.8, board data of October 14, and a public index of 62.52 on the authors' own 0.3 run. Apache-2.0 LoRA on Gemma 4 31B. This desk did not run the weights.
Opened October 26, Decision Index 0.4.1 Full score for d3-turbo is 67.31, and vision full is 72.46
Xunzhuo Liu posted d3-turbo on October 20, 2026, at 00:42 UTC. Opened October 26, Decision Index 0.4.1 Full score is 67.31 and vision full is 72.46, second of 56. The card's own 0.3.2 estimate is 70.2. Apache-2.0. This desk did not run the weights.
Cloudflare posts Clairic, a System One extraction model at $0.10 per million input
Cloudflare posted Clairic on October 19, 2026. The blog's cascade ran in 19.3 s against 61.7 s for GLM-5.3 at every step, $6,032 against $11,740 per million claims. Docs price input at $0.10 per million tokens. This desk did not call the API.
Datadog prints Jev Noul recall 82.90% to 3.88% under a counterfeit policy, and this desk's is_risky fell from 0.94 to 0.13
Datadog Security Labs' October 15 write-up, posted October 19, prints Jev Noul recall 82.90% on raw attacks and 3.88% with a counterfeit policy prepended. Date headers 08:50:46 GMT and 08:50:47 GMT October 20, this desk's jev-1.13.0 is_risky was 0.94 then 0.13.
Nace posts Drex Max at Decision Index 0.3.2 public 67.59, $0.06 per million input
Nace posted Drex Max on October 19, 2026. The product page prints Decision Index 0.3.2 public 67.59 against Jev 1.14 at 60.70. Docs price input at $0.06 per million tokens. Date headers 08:50:01 GMT and 08:50:02 GMT October 20, GET /v1/models with no key returned HTTP 401.
ToddlerJev prints 68.70% on MMAU-mini, with weights under the Qwen Research License
ToddlerJev, posted October 18, 2026, prints 68.70% on MMAU-mini, 61.30% on MMSU, and 56.90% on MMAR. Weights are TeenageWorks/Toddler-Jev under the Qwen Research License. This desk did not run them.
Opened October 19, JevBench ranks basal-omni-1.0-ultra first of 184 open systems on Capability at 82.5
Opened October 19, 2026, JevBench ranks basal-omni-1.0-ultra first of 184 open systems on Capability at 82.5. Composite among open weights is 71.8, rank 3. The card prints Werdykt v1 at 0.879. This desk did not run the weights.
Opened October 17, JevBench API board v1.7.46 ranks kettei-fat first of 39 at composite 77.8
Opened October 17, 2026, JevBench API board v1.7.46 ranks kettei-fat first of 39 at composite 77.8. ImageJevBench v0.3.0 capability is 75.9. Docs price kettei fat at $0.06 per million input tokens. GET /v1/models with no key returned HTTP 401.
Opened October 16, Artificial Analysis Decision Index prints d1-ultra at 65 and Jev 1.14 at 57
Vals AI, October 22, 2026, 23:57 UTC, printed d1-ultra as the only decision model to get all 1,960 ClaimBench claims right. Opened October 16, Artificial Analysis Decision Index prints 65 against Jev 1.14 at 57. Liquid's blog prices input at $0.042 per million tokens, output free. This desk did not call the API.
HiddenLayer prints Jev at 91% Confirm Price on a checkout ad, and this desk's jev-1.13.0 still chose place_order at 0.89
HiddenLayer's October 14, 2026 write-up prints TypeSafe Jev at 91% Confirm Price and 9% Place order on a checkout ad. Date headers 07:24:57 GMT and 07:24:58 GMT October 19, this desk's jev-1.13.0 still chose place_order, 1.0 then 0.89.
On October 26 TypeSafe's jev-latest named jev-1.14.0, and both aliases point at 1.14.0
Date headers 08:49:11 GMT to 08:49:40 GMT October 26, POSTs with jev-latest and jev-preview named jev-1.14.0. Docs say both aliases point at jev-1.14.0, and that there is no preview build. A named jev-1.13.0 POST still named 1.13.0. At 00:28 UTC October 16, jev-latest still returned jev-1.13.0.
TypeLLM said Jev 1.14 cannot return every vehicle box, including Tesla's flying Roadster; this desk's every-vehicle Box was one rectangle, n = 3, and type array returned HTTP 400
On October 16, 2026, at 08:19 UTC, Jev 1.14 returned one box for every vehicle, n = 3, confidence 0.19, 0.17, 0.13, and type array returned HTTP 400. TypeLLM's cookbook at 14.00 s prints six vehicle boxes, including Tesla's flying Roadster.
A green Continue button scored noul 0.99 on jev-1.14.0, n = 3, and an empty field scored 0.02
On October 16, 2026, at 00:28 UTC, a green Continue button scored noul 0.99 on jev-1.14.0, n = 3, and an empty field scored 0.02. Aviz Maeir scored Jev 1.14 Box at 82% on 180 scenes. Jev4Mellea's 12 scenes covered 5 of 12 at 0.10 / 0.90.
TypeSafe-Danger-Zone-Long-Context raises both Jev context caps to 262k tokens
The header TypeSafe-Danger-Zone-Long-Context: true raises both context caps to 262k tokens on Jev 1.14 and Jev 1.13. Default limits stay 64k per request and 32k per question. On October 15, 2026, at 01:22 UTC, this desk's header calls returned HTTP 200 on 2,122 input tokens.
Jeva-4B prints 0 flips on 600 Choice permutations, and BANKING77 at K=16 is 91.62 against Jev 82.50
Hugging Face DailyPapers posted Jeva on October 14, 2026, at 20:10 UTC. The paper prints 0 flips on 600 Choice items and BANKING77 at 16 candidates 91.62 against Jev-1.13.0 at 82.50. Apache-2.0 on Qwen3.5-4B. This desk did not run the weights.
SubQ Snap 1.1 prints 65 on Artificial Analysis Decision Index, $0.04 per million input
On October 23, 2026, Date headers 02:23:17 GMT to 02:23:20 GMT, three POSTs from this desk to SubQ Snap returned HTTP 200. Docs name subq-snap-1.1 at $0.04 per million input tokens. GET /v1/models listed subq-snap-1.1 and subq-snap-preview. Artificial Analysis Decision Index prints Snap 1.1 at 65, tied with d1-ultra and Elephant 9B. The October 12 blog prints Decision Index 0.2.1 at 61.7 against a Jev cell of 57.9.
PurpleMIST-Flash-1.0 scores 0.668 on 15,050 translated African-language decisions
PurpleMIST-Flash-1.0 scores 0.668 accuracy on 15,050 translated African-language decisions, against DeepSeek-V4-Pro at 0.669. KL on that set is 0.214 against 1.125. The card says the checkpoint is Apache-2.0, about 7.9B parameters, from Qwen3.5-9B-Base, and text only. A self-run of Decision Index edition 0.3 prints a public index of 46.7. We did not run the weights.
JevBench ranks Sage 1.3.0 first of 27 hosted systems at composite 74.0
Opened October 11, 2026, the JevBench API board ranks Sage 1.3.0 first of 27 hosted systems at composite 74.0. Levanto's pricing page lists Pro input at $0.042 per million tokens, and output at $10 per million for reasoning. Answers on that page are free. SageCraft's October 9 log is 4,573 decisions and level 5 at 2 cents a kill. We did not call the API.
TypeLLM scores Qwen3.8-27B at 228 of 231 public JevBench tasks when thinking is on
On October 16, 2026, at 05:44 UTC, TypeLLM posted that Jev 1.14 cannot return an array of vehicle boxes, including Tesla's flying Roadster. The cookbook at 14.00 s prints six boxes. The September 23 file still scores Qwen3.8-27B at 228 of 231 public JevBench tasks with thinking. This desk did not call the API.
Vega, the vegaml package, scores 75.4% on 800 phishing emails against Jev at 61.9%
Nandha Kishor M's Vega, the vegaml package, scores 75.4% on 800 phishing emails against Jev 1.13.0 at 61.9%. The default checkpoint is 0.8B on frozen Qwen3.5-0.8B, Apache 2.0, with a 4B sibling. Decision 2.0's Vega is a different checkpoint. We did not run the weights.
Clef-omni scores a 21-second video in about 1.5 seconds, at $0.15 per million input tokens
Cloudflare's October 9, 2026 blog prices Clef-omni at $0.15 per million input tokens. The same post cuts hosted Clef-flash from $0.09 to $0.038 and from a 64k window to 24k. A 21-second video with sound scores in about 1.5 seconds. On that post's ten-benchmark table, Clef-omni's BANKING77 macro-F1 is 94.8. The weights are Apache 2.0, from Qwen3-Omni-30B-A3B-Instruct. We did not call the API.
Microsoft-Decision-1 lists $0.042 per million input tokens and calls itself 4.5 times quicker than Quyet-1.0-Large
Opened October 11, 2026, the JevBench API board ranks Microsoft-Decision-1 sixth of 27 hosted systems at composite 69.1. On October 9, 2026 Microsoft posted the model at $0.042 per million input tokens, with output free. The post says it was 4.5 times quicker than Quyet-1.0-Large and 35 times quicker than GPT-6 Sol, and it does not print an accuracy. The Foundry catalog, opened October 10, lists a 32,768-token window and says the weights are not distributed. A search of the October 10 API page found no Microsoft-Decision-1. We did not call the API.
OpenBles Firewall 1 catches 119 of 120 agent attacks and flags no benign look-alike
OpenBles Firewall 1 caught 119 of 120 agent attacks and flagged none of 120 benign look-alikes on its held-out test. The README prints classic-set recall at 55 of 60, with 2 of 56 benign prompts flagged. The adapter is MIT, 31.8 MB, on fastino/GLiNER2.5-Decide. We did not run the weights.
Celeris-1 Decision prints 80.0% on 3,210 jev-bench items at a 67 ms median, and Artificial Analysis lists it at 52
Celeris-1 Decision, posted October 8, 2026, prints 80.0% on 3,210 jev-bench items at a 67 ms median, and $0.04 per million input tokens. Opened October 16, Artificial Analysis Decision Index lists it at 52. GET /v1/models with no key returned HTTP 401. This desk did not send a billed POST.
H2O-Lightning-4B v1.1 prints JevBench composite 72.5 against Jev at 71.5
H2O's October 7, 2026 blog prints H2O-Lightning-4B v1.1 at JevBench composite 72.5, against Jev 1.13.0 at 71.5. That reading is board revision v1.7.17 and 104 open-weight systems. The blog says the weights are Apache 2.0, from Qwen3.5-4B. We did not open the card, and we did not run the weights.
Arena scores typesafe/jev-router at +3.6% and $0.155 a task
Arena's October 7, 2026 thread scores typesafe/jev-router at +3.6% net and $0.155 a task. The same thread says similar performance to DeepSeek V4.1 Flash (Max) costs 38% more, with median latency 1.7 times higher, on more than 4,700 agent sessions. The model page FAQ says the router has no price of its own. We did not call it.
PolicyLM-1.7B scores each policy category from 0 to 1
PolicyLM-1.7B prints custom-policy accuracy 0.842 at a 0.5 cutoff, and it returns a score from 0 to 1 for each category, up to 16 in one pass. The weights are Apache-2.0. The table has no Jev column.
Nace posts Drex 1.1, and the card titles the weights Drex DLM
Nace posted Drex 1.1 on October 6, 2026, and the card scores Decision Index 0.2 at 52.31 against the kit's Jev cell of 51.67. Weights are CC BY-NC 4.0. Nace's code is MIT. The call is POST /v1/systemone. The hosted Drex 1.5 row on Decision Index 0.2.1 is 58.28.
Bud Decision Studio serves eleven local decision models on port 8420
Bud Decision Studio runs eleven open decision models on a local TypeSafe-shaped API at port 8420, and its README prints a GB10 training table.
PageIndex walks long filings with Jev Choice calls and checks two answers
VectifyAI's jev-doc-search, Apache 2.0, asks jev-1.13.0 to walk a PageIndex tree, and on two annual reports the kept pages include the income statement.
OpenAI Decisions API is generally available; this desk's October 23 POSTs returned HTTP 200
On October 23, 2026, Date headers 02:23:12 GMT and 02:23:13 GMT, two POSTs from this desk to the Decisions API returned HTTP 200 from gpt-6-luna. OpenAI Developers posted general availability on October 22, 19:07 UTC. The guide prices uncached input at $0.10 per million tokens and cached input at $0.01, with no output charge. Requests now hold up to 400 questions. On October 8, 2026 ten urgency calls had a median clock of 668 ms on Jev and 387 ms on Decisions.
Cloudflare posts Clef and Clef-flash, Apache 2.0 decision models on Workers AI
On October 9, 2026 Cloudflare's blog prices Clef-flash at $0.038 per million input tokens, down from the $0.09 on the October 1 card. Clef stays at $0.24. Clef-omni, posted the same day, is a separate model at $0.15. Cloudflare posted Clef and Clef-flash on October 1, 2026, two Apache 2.0 decision models hosted on Workers AI and released on Hugging Face. The October 1 blog's median latency row is 209.3 ms and 38.8 ms against a printed Jev cell of 524.1 ms. On October 3 a Fastino chart prints Clef at 61.2 and Clef-flash at 57.1, and captions those Cloudflare scores as self-reported. llama.cpp v0.6.0, published October 5, serves Clef on /v1/systemone, including images. The October 6 note leaves Clef's speed cell blank.
Databricks posts ai_decide, a Beta function that returns a noul, a choice, or a score
Databricks posted on October 16, 2026 that TypeSafe's Jev-1.14 is available through ai_classify and ai_decide on the Lakehouse. The September 30 blog still calls ai_decide a Beta AI Function and does not name the checkpoint. The docs do not print a price.
DSPy 3.4.0 calls Jev from Predict, with a gate at 0.2 and 0.6
DSPy 3.4.0 adds experimental Noul, Choice, and Score types plus a TypeSafe client, and a patterns repo pins jev-1.13.0.
GLiDE v1.2 prints Decision Index 0.4.1 Full 71.5, first of 229, at $0.04
Fastino posted GLiDE v1.2 on October 22, 2026, at 16:36 UTC. Opened October 26, Decision Index 0.4.1 Full score is 71.5, rank 1 of 229. Docs price input at $0.04 per million tokens, output free. Date headers 08:49:09 GMT to 08:49:11 GMT, GET /v1/models returned HTTP 200 and omitted GLiDE, POST /v1/systemone returned HTTP 401. JevBench lists Fastino GLiDE (hosted API, 22 Oct 2026) at composite 76.4, rank 3 of 48. The October 1 self-run of 64.81 stays.
Perplexity posted pplx-decider-v1.2 on October 16, with Decision Index 0.3 overall 68.09 on joint requests against v1.1 at 62.30
Perplexity posted pplx-decider-v1.2 on October 16, 2026, with Decision Index 0.3 overall 68.09 on joint requests against v1.1 at 62.30, at $0.02 per million input tokens. Flash is $0.01. The October 1 panel still prints 85.71% for v1. Opened October 8, Decision Index 0.3 ranked v1.1 first at 62.8.
You.com's risk sweeps cost $0.14 each, and the report means tie
Edward Irby's three escalated sweeps cost $0.43 in all, and a blind read of nine Jev reports and six Qwen reports ties at a mean of 4.25.
Strands Decider 2B scores 167 of 231 public JevBench tasks
Strands Labs posted Decider 2B on October 1, 2026, scoring 167 of 231 public JevBench tasks at a median 115 ms on an RTX 3090.
Nace posts Drex 1.5 at $0.04, and its chart shows 199 of 231 against Jev's 201
The October 9, 2026 weights card prints Drex v1.5 at Decision Index 0.3.1 public index 58.08, against Jev 1.13.0 at 57.96. Nace posted on September 30 that Drex 1.5 is live, with a 128k context window, and called it first on Decision Index 0.2.1 and on JevBench. The chart on the next post prints Jev 1.13.0 at 201 of 231 and Drex 1.5 at 199 of 231. Hard tier on that chart is 82 of 111 for both. The post's price is $0.04 per million input tokens. October 1 Benchmark Heaven text has no Drex 1. On October 6, 2026 Nace posted open weights and called them Drex 1.1. The card title is Drex DLM, and that card's Decision Index 0.2 cell is 52.31 against the kit's Jev cell of 51.67.
TechCrunch prices one Jev monitor at $2.94, and the demo page prints another estimate
Tim Fernholz, TechCrunch, September 30, describes Shapor Naghibzadeh's Jev Sentinel demo and writes that this kind of monitoring costs $2.94 with Jev against $372 with a frontier LLM, in theory. The demo page does not print those two figures. It prints about $55 per million actions with Jev against about $6,900 for a frontier judge, a 0.23 second median, and 0 refusals on 53,870 attack payloads. We did not open the GitHub repo.
jgrep's test bench prints mean recall 0.926 across 60 commits, at $0.1112
kyu1204/jgrep, MIT, npm name jevgrep, command jgrep, is a different project from dzhng/jevgrep. A September 28 re-run of 60 commits prints mean recall 0.926, a selection ratio of 0.119, and a total cost of $0.1112. The post's 12%, 90%+, and $0.11 are those three figures rounded. The model field sent is jev-latest. We did not run it.
Inception calls Mercury Decide first on OpenRouter's JevBench v1.4, and prints no score
Opened October 8, 2026, the JevBench API board ranks Mercury Decide third at composite 72.4. The free model page returned the same day, priced Free, with up to 14 decisions a second and no JevBench score. Inception posted on September 30 that Mercury Decide is the most intelligent decision model on OpenRouter, citing JevBench v1.4, at up to 14 decisions a second. The post does not print a score. The free model URL returned Not Found. OpenRouter's Mercury 2.5 page describes Mercury Decide as a System One endpoint with free output tokens and the same schema as Jev. An October 1 search of Benchmark Heaven found no Mercury Decide.
Ollaya serves local decision models on port 11435, and prints winnow:e4b at 0.722
ollaya-dev/ollaya, Apache-2.0, calls itself Ollama for decision models and says it is independent of Ollama and of TypeSafe. The README prints winnow:e4b at 0.722 against TypeSafe Jev at 0.738, and 89 ms for five questions on an RTX 4090. The item count behind that pair is not defined on the page we read. We did not install it.
JevScout ranks oversized MCP text and prints 38% less agent time on its own check
Tim Lin posted JevScout on September 29. The MIT hook leaves Claude Code's MCP result alone under 100 KB, and otherwise asks Jev which segments to keep verbatim. The README's check is 38% less agent time and 26% less agent cost, with TypeSafe's bill separate. One printed row is 5 of 5 against 1 of 5. The evaluation note does not list all seven tasks. On September 30, 2026 this desk scored seven stand-in tasks, three times each, on jev-1.13.0. The critical line stayed in all 21 filtered runs. Claude Code was not installed, so the 38% and 26% were not measured.
A Pi extension asks Jev which skills to load, and the 20-skill demo drops about 1,000 tokens
Arnav Gupta posted a Pi skill picker on September 29. The MIT repo, pi-skill-picker-jev 0.1.0, asks a decision model which skill names belong in the prompt. The README's 20-skill storefront demo cuts the initial system prompt by about 1,000 tokens. The post's 250-skill work repo, about 10,000 tokens and under a second, is not in the README. We did not boot Pi. On September 30, 2026 we scored the 20 published skill names, and a 250-name catalog of those names plus 230 fillers written here. Necessary-skill recall was 16 of 19 on both catalogs. Five of eight tasks had every required skill selected. Median latency was 951 ms and 2,322 ms. The scoring note, opened September 30, uses a five-level Score and a default threshold of 0.625.
Upstage's Solar Decide chart prints 87.0% on the public 231, and a p95 of 15.7 seconds
Sung Kim posted Solar Decide on OpenRouter on September 29. The chart says the comparison is the public JevBench 231, easy 48, standard 72, hard 111. Solar Decide is 87.0% at a p50 of 0.14 seconds. Jev 1.13, measured through OpenRouter, is 86.1% at 0.30 seconds. The footnote puts p95 at 15.7 seconds against Jev at 0.40 seconds. We did not rerun the 231.
Violoop ran the same desktop harness with Jev and with Opus 5.5 on one QuickBooks bill
Violoop posted four live desktop runs on September 29. On one prepared QuickBooks bill, the same harness made 123 decisions with Jev in 127.3 seconds and with Claude Opus 5.5 in 978.9 seconds. Estimated model cost was $0.0027 against $1.38. The Tetris snapshot used a faster gravity setting for Jev. We did not rerun the tasks.
Liquid's chart puts d1 at 58.9 on an internal Decision Index 0.2.1
Liquid AI posted a chart on September 29 captioned as an internal reproduction of Decision Index 0.2.1. The index line is d1 58.9, Jev 1.13 57.9, difference +1.0. Arts, language, and retrieval are ahead on that chart. Tools and knowledge are behind. The docs name a hosted endpoint and the model id d1:free. We did not call it. On October 7, 2026 the JevBench API board ranks d1 second at composite 73.0, with capability 74.4. Opened October 8, 2026, d1 is still second at 73.0, and Jev 1.13.0 on that board is fourth at 71.5. The same day's open weights, d1-3B at 48.57, are a separate page.
OpenAI's Decisions API preview says results come back in 150 milliseconds
On October 22, 2026 OpenAI Developers posted the Decisions API generally available, and this desk's October 23 POSTs returned HTTP 200. The September 29 post had called it a limited preview. On October 6 the Developers post said public beta. The guide prices uncached gpt-6-luna input at $0.10 per million tokens and cached input at $0.01.
Glean replayed four internal decisions on Jev, and 40 expert transfers were 8.1 times faster
Tony Gentilcore posted Glean's write-up on September 28. On 4,855 captured queries, Jev Choice cost about $0.00044 and took 0.195 seconds at p50, and tied about 37 of 41 candidates. Production order as a tie-break raised Recall@6 from 40.2% to 44.3%. A 40-entry transfer sample had a median speedup of 8.1 times. Several chart cells in the article did not come through as text. We did not rerun the sets.
One day of grant posts, 100 of them, hid 75 of 78 rejects after the questions changed
Deji labeled 100 funding posts himself, then scored Jev against that key. Seven were a real yes for the founders he serves. After he switched to full pages, an explicit 'doesn't say,' and one post at a time, the thread prints 75 of 78 nos hidden, 5 of 5 yeses shown, 6 of 6 duplicate programs caught, and deadlines on 33 of 41. The day of testing cost about 2 cents. There is no repository. We did not relabel the posts.
jevbench.app replays recorded Jev answers on labeled examples, and one test is 20 of 21
Yusuke posted jevbench.app, a playground of unit tests for questions you have already labeled. The lead-qualification set is 21 examples, 10 yes and 11 no, recorded September 27. The index says Jev matched 20 of 21, and the miss is a yes at 0.51 on a post a human marked no. Opening a test makes no new model call. This is not Florian S's JevBench. We did not rescore the file.
Polylane moved five on-call decisions to Jev and prints a 39.1% lower bill per 1,000 calls
Vi Tran posted Polylane's write-up on September 28. The page, dated September 25, replaces five fixed-answer steps with jev-1.13.0. The rollup is p90 latency 4,752 ms to 508 ms, and $0.76199 to $0.46369 per 1,000 calls. The page does not print accuracy or a call count. We did not rerun the traffic.
Rox's chart keeps 96% of relevance at 200 chunks, against 84% in production
Rox posted on September 28 a chart titled Jev vs. our production reranker, a mean over 75 queries. At 200 chunks the printed callouts are Jev-Score 96% and production 84%. The post, and a TypeSafe quote on September 30, say 20 times faster, 10 times cheaper, and 12% more accurate. Those multiples are not on the chart. We did not open a methods note.
A DTap chart puts a Jev 1.13 tool selector at 70.1% attack success, and a second Jev call at 36.1%
Zhaorun Chen posted a DTap chart on September 27 for Jev 1.13 used as a tool selector. Direct misuse attack success is 70.1%. A second Jev check on the filled tool call brings that to 36.1%, and indirect prompt injection from 43.5% to 21.6%. Benign success moves from 60.9% to 55.8%. The chart does not print a task count. We did not rerun it. The linked repository, opened September 30, is x402check, a pre-payment risk check. Its sample model field is jev-wallet-risk/v6.
A registered attack on jev-1.13.0 moves validated success from 1.8% to 3.5%
Tiantong Wu and Wei Yang Bryan Lim, NTU, posted arXiv:2609.28613 on September 23. On 510 reconstructed InjecAgent cases, an adaptive attack with score feedback lifts validated attack success on jev-1.13.0 from 1.8% to 3.5%. The mean highest attacker-target probability found during search goes from 0.043 to 0.084. Several registered phrasings do not help the attacker. The paper has no code repository in the text we read. We did not rerun it.
Deel's charts show 6 of 8 quality checks at parity, and two losses
TypeSafe posted four charts on September 25 and attributed them to Deel. Six of eight quality checks are at parity or better. Messy metric questions fall 16.6 points. Three-level ticket tags fall 3.6. The live-shadow speed bar is 4 times. The tweet's 50% to 86% and 70% to 97% are not printed on the chart. We did not rerun it.
postgres-search can sink a bad hit, and the README has not measured that step
Alex posted an Algolia-style search in Postgres with an optional Jev pass. On 50 handwritten queries the SQL ranking puts a correct product first 82% of the time. The Jev step asks one Noul per candidate in the top 10 and sinks anything under 0.30. The docs say that pass was not measured, because no API key was available.
JevGate turns one function at a time into a finding at 0.80
Nodehaven Online posted jevgate, a Rust review gate on crates.io as v0.5.1. Tree-sitter finds a function, a copy, or a test locally. Jev answers a short question. Code maps the probability to review, consider, note, clear, or uncertain at 0.80. The README says those probabilities are judgments, not a measured accuracy.
Jevonian asks Jev which model should take the next coding turn
Chen posted xinyao27/jevonian, an AGPL-3.0 local proxy on port 8787. The virtual model jevonian/auto makes one Jev call for the route and the thinking depth. A named phase, a real model id, or routing turned off skips that call. The README prints no scored routing table.
JEV Search moves NFCorpus nDCG@10 from 0.322 to 0.377
andre posted assadiandre/jev-search, a macOS app that ranks files with no saved index. On 623 queries the README's nDCG@10 goes from BM25 0.322 to JEV 0.377 on NFCorpus, and from 0.687 to 0.745 on SciFact. The repo root has no LICENSE file. The post's cost line is under $0.001 a query.
jevmod scores sexual text at 0.984 AUROC, and ZeroNSFW keeps the pictures local
jevmod's sexual AUROC on OpenAI's moderation eval is 0.984. ZeroNSFW scores pictures on the host and asks Jev only about text.
Together prices Tev1 input at $0.04, and Ollama serves Choice, Score, and Noul
Opened October 7, 2026, Together's Tev1 page lists input at $0.04 per million tokens and free output. The September 23 post listed $0.042. The chat call returns one letter. Ollama serves tev1 on /v1/systemone.
AgentOS posts a 121-turn routing table for an opt-in Jev strategy
AgentOS added Jev as a third Pilot Router strategy, off by default. On the project's 121-turn labeled set the chart shows Jev at accuracy 0.901 against the local MiniLM classifier at 0.355. The same chart prices 121 cases at about $0.005 and a call at about 0.9 seconds. A follow-up pull request the same day says it sharpens the R2 and R3 criteria. Opened September 30, that pull's file list has no results JSON. Its changelog prints accuracy from 0.760 to 0.901.
ASO Workbench ranks App Store terms with a model trained on Jev's answers
Mustafa Kendigüzel posted ASO Workbench, a Mac app that estimates Apple search popularity. The method note trains on 44,658 rows. On 578 held-out terms that have autocomplete clues, Spearman rank agreement with Apple is 0.74 and the mean error is 10.4 points. Jev, through OpenRouter, supplies the brand, demand, and language-fit features. Without those answers the app does not guess.
meraGPT posts Decider 1 ahead of Jev on a teacher-labelled set
Latent Node posted Decider 1 (sd-1), a hosted model that answers noul, choice, and score questions in one call. The blog's table is accuracy 0.768 against Jev 1.13.0 at 0.727. The labels are an average of teacher samples. A September 23 thread runs the same model through a harness called jev-eval and prints one task in full: yes/no sentiment, Jev 0.970 at $0.021 per 1,000 calls, Decider 1 0.943 at $0.0078. The dataset card, read September 30, says that table is the test split, 400 cases and 2,000 decisions. The viewer's 1,600 rows count train as well.
A Cloud Run recipe serves DiffusionGemma-Jev at 35 to 60 ms
Google's Gemma account posted a one-command Cloud Run deploy for DiffusionGemma-Jev. The README puts single-request latency at 35 to 60 ms, batch throughput at 100 to 123 requests a second, cold start at about 47.5 seconds, and the GPU at $3.19 an hour while it is up. The repository lists no license. It does not print an accuracy table.
Exa posts a demo that puts web results into Jev's state
Ishan Goswami posted a demo at demos.exa.ai/jev-web-context. The post says Jev without web search is confidently wrong, and Jev with Exa results is more accurate. The page we loaded lists seven yes/no prompts and does not print a before-and-after. Diogo Almeida quoted the post and asked if it was real.
jev-gateway asks Jev which tool a coding agent should call
Vini Lana posted jev-gateway, an MIT local proxy for Codex, Claude Code, OpenCode, and Gemini CLI. Jev picks the tool. Closed arguments can skip the language model. On the project's chess-engine bench, GPT-6 Astra and GPT-5.6 Sol cut bug-fix output tokens by 57%. The feature task made Opus 5 and Sonnet 5 use more.
jevelry turns a Jev call into a folder, a verdict, and a log
Pavel Hegler posted backant-io/jevelry, an MIT npm runtime. A jevel is a folder with one JEVEL.md. The CLI asks Jev once, maps each answer to act, mark, or fall_back, and appends a log. The live sample in the README is one state on jev-1.13.0. Three on-demand API calls took 2.49 seconds.
jym asks Jev which real subcommand a mistyped one was aiming at
syumai posted jevyoumean, an MIT Go wrapper. When a subcommand is not in the CLI's help, it sends one Jev Choice over the documented names and descriptions, then offers the top matches. Valid commands never call the API. The README ships an eval harness and does not print a scored corpus.
jsort ranks a file by asking Jev which of two texts is more of something
Khaled Eltokhy published jsort, an MIT command that fits a Bradley-Terry scale from pairwise Jev judgments. On 300 CommonLit excerpts it tracked teacher easiness at Pearson r 0.824 for about five cents. Joe Weisenthal then said he had rescored 4,005 FOMC speeches and statements with it. The repo's own Fed check is 95 opening statements.
llamacpp-jev answers /v1/systemone from an unmodified llama-server, including images
Chirag posted llamacpp-jev, an MIT wrapper that puts TypeSafe's decision API in front of stock llama-server. On an M4 Pro, four questions about a fresh 448 by 448 image took 526 ms median, 32 of 32 on synthetic shapes. The probabilities are a raw label softmax. Qwen3.5-2B picks option 16 on every 64-way question the notes tried. A reply the same hour as the cited 800 ms post links kishida/llama.cpp branch jev, which builds the endpoint into llama-server. On October 2, upstream llama.cpp merged the same path for five decision-model GGUFs. Release v0.6.0, published October 5, also serves Nimble and Clef. The Nimble pull names Bespoke-Nimble-9B-v3, whose card prints 56.88 on Decision Index 0.2.1. Clef can take an image. The October 2 medians are unchanged.
pg-jev puts prompt_jev() in PostgreSQL and returns jsonb
Jose Mejias posted mejiasd3v/pg-jev, an MIT extension. prompt_jev sends text to Jev from SQL and returns a probability, a choice, or a score as jsonb. The README we read has no accuracy or latency table. Calls are not batched across rows. Zachi's realZachi/pg-jev is a different extension.
A Hold'em table asks Jev about the opponent and lets code pick the bet
A September 22 post links Jinzhengxu/poker-table and says each action averaged 545 ms and a hand cost $0.002. The README's Jev path is one round trip about the opponent, after a local equity table. A 46-spot bank, three repeats, is described at about one second median. Those two clocks are different measurements. Opened September 30, the live table was waiting for a hand and the Jev panel was not configured. The page does not print 545 or 0.002.
swift-jev types Choice, Noul, and Score, and routes at 0.60 and 0.85
Daiki Matsudate posted a Swift client after a trial that paired Jev with another model did not go well. The library is MIT, has no dependencies, and returns enum cases, probabilities, and scores from one request. The sample policy escalates under 0.60 and acts at 0.85 or above. The README prints no scored set.
TypeSafe reopens Jev signups and drops the free credit for new accounts
On October 8, 2026 the same signed-in TypeSafe console still showed $0.00 in available credits, with no payment method and no billing history. Usage for the last 7 days was $0.00, 0 tokens, and 0 requests. On October 6, 2026, at 17:05 UTC, this desk signed in to a new TypeSafe console account that showed $0.00 in available credits. Billing history was empty. TypeSafe had paused new signups on September 22 and said on September 24 that they were still closed. On September 27 at 22:30 UTC the same account posted that signups were open, and that new signups no longer get free credits.
djev reads DiffusionGemma as a decision API, and sits third on JevBench
Florian S's JevBench table puts djev at 74.3, 1.1 behind Jev. Maisa's djev.dev hosts the API at an announced $0.035 per million input, with native image questions. Matt Mastracci posted phone-camera clips of DiffusionGemma-as-Jev. Davipar/djev-dev is the Apache 2.0 local stack.
A native DuckDB extension batches Jev over SQL rows at about 2,300 rows per second on a live run
Prasanth J posted duckdb-jev, an Apache 2.0 C++ extension that asks TypeSafe's Jev from SQL. A live Choice run classified 2,049 synthetic rows in 0.887 seconds. A cached replay of the same query finished in 28.6 ms with zero API calls. Row text leaves DuckDB.
jevsearch re-ranks keyword hits with Jev and scored 83% Hit@1 on the author's TypeSafe docs set
Kyle McLaren posted jevsearch, an MIT shadcn registry block that streams keyword hits and then re-ranks them with Jev. On 41 labelled queries over TypeSafe's documentation, Hit@1 rose from 41% on the keyword pass to 83% after Jev. Median uncached ranking was 278 ms. About $0.26 per thousand searches on that corpus.
An n8n community node routes Jev answers onto category branches, including Needs Review
khmuhtadin posted n8n-nodes-jev-classification, an MIT community node that wraps TypeSafe's Jev as classify, score, check, and ask. One output per category, plus a Needs Review branch under a confidence threshold. n8n declined to verify it because it is building a built-in classifier. Self-hosted only.
MotherDuck's prompt_jev() labels 100,000 AG News rows in 40 seconds for $0.50
MotherDuck shipped prompt_jev(), a SQL function that calls TypeSafe's Jev. On 100,000 articles drawn from the AG News training split, the published table is 2,484 rows per second, 89% accuracy, $0.50, and 40 seconds. GPT-5.6 Terra on the same sample is 52 rows per second, 88%, $37.58, and 31 minutes 59 seconds.
A 100-case security triage demo put Jev at 65.3% balanced accuracy against Terra 75.1% and Opus 91.2%
RZ posted a recorded comparison of Jev, OpenAI Terra in Codex, and Claude Opus in Claude Code on 100 synthetic TypeScript security cases, five passes each. Jev's balanced accuracy was 65.3% at 0.57 s median. Terra 75.1% at 10.23 s. Opus 91.2% at 13.83 s. Jev repeated the same answer on 95 of 100 cases. Cost about $0.015 versus $15.02 and $13.94 for 500 decisions.
SimpleJev turns an open model into a classifier by reading the next-token logits
Featherless posted SimpleJev, an Apache 2.0 server that scores Choice, Score, and Noul from a Hugging Face model's next-token logits. A public demo needs no key. JevBench listed Qwen3.8-27B at 67.3, with hard-tier accuracy 75.0%, matching Jev on that slice and trailing on the composite.
Spring publishes a Java client that asks Jev before the chat model runs
Opened October 8, 2026, the README quick start pins 0.4.0, and Maven Central lists that release. Christian Tzolov wrote up spring-ai-community/spring-ai-typesafe, an Apache 2.0 Java SDK and a set of Spring AI advisors. On his laptop a three-question ticket call took 310 ms. The blog says 0.1.0 is on Maven Central. The README quick start still shows 0.1.0-SNAPSHOT, and a search of search.maven.org returned no documents for those artifact ids. GitHub release 0.3.0, September 30, says the same client talks to Laya. The Ollama page is on the snapshot docs. A Maven search on October 1 still returned no documents for spring-ai-starter-typesafe.
TypeSafe posted an API and console outage, then said it was back
At 08:30 UTC on September 21, TypeSafe posted that the API and console had issues and pointed at status.typesafe.ai. At 09:53 UTC it posted "We are back!" The status incident is titled "Console is unavailable" and marked resolved at 08:16 UTC, before those tweets.
Vercel's form router keeps Jev's destination when confidence is at least 0.95
Vercel published a Next.js template that asks Jev to pick a team for a form submission and sends the same submission to GPT-5.6 Luna Fast when confidence is below 0.95, missing, or the call fails. The fallback does not see Jev's answer. Tests mock both models.
AstroHan ran 30 coding tasks with a Jev context filter: 25 passed, 22 without it
AstroHan scored every tool result with Jev before it reached deepseek-flash, on the same 30 FrontierHarness tasks. Filtered 25/30 pass@1 versus 22/30 unfiltered. On meriyah the unfiltered run timed out at 0/49 tests; the filtered run passed 49/49. One run per arm. Token spend did not fall.
DocJev classifies and splits real PDFs with Jev, 5.7x to 6.5x faster than Luna on the author's set
Jerry Liu posted DocJev, an Apache 2.0 library that classifies and splits PDFs with local LiteParse and hosted Jev. On 40 public-sector originals, both engines scored 40/40. Jev split 7 of 8 packets exactly against Luna 8/8, at 5.73x and 6.45x the median decision speed.
TypeSafe drops the Jev waitlist; new accounts start with $5
On October 8, 2026 the same signed-in console still showed available credits of $0.00. On September 20 TypeSafe dropped the Jev waitlist and posted $5 of credit. New signups no longer get it. On October 6, 2026 a new account showed $0.00.
jevctl puts Jev on the command line, with a compaction hook that keeps lines verbatim
jaidev posted jevctl, a friend's npm CLI that wraps TypeSafe's Jev as shell commands. verify, screen, classify, extract, find, rerank, match, route, ask, compact, and batch. Keys can be TypeSafe, OpenRouter, or Cloudflare. compact drops stale tool results the way Tran's sieve does, at a 0.5 keep threshold.
Jevflake puts Jev in Snowflake: noul, choice, and score as SQL functions
Luke Kranz published Jevflake, a dbt package (and a Terraform module) that lets Snowflake call TypeSafe's Jev API from SQL. Answers are stored once, reused, and tested like any other model. Version 0.1, run against one live account. Row content leaves Snowflake.
Jared Palmer's Kev family copies Jev's API on Qwen, and trails it on new sources
Cognition's VP of engineering published Kev, an Apache 2.0 family of Jev-like models you train and serve yourself. On data the models never trained on, Kev-8B scored 79.6% against Jev 85.7%. JevBench listed kev 0.6B at 66.7. A Qwen3.5 generation followed the same day. On September 25 he posted Kev-4B on OpenRouter. A September 26 paired run scored that checkpoint at 1,216 of 1,253 full-schema function selections, against 1,235 for a fresh jev-1.13.0. On September 28 Christoph Görn scored the same 4B at 1 of 7 on eight of his own prompts. On October 2 Jacob Lin's StreamDecisionBench chart prints Kev-27B at 62.15 and the Jev API at 59.63, as normalized log-AUC over 0.5 to 8 seconds.
Laya, an Apache 2.0 System One stack, publishes where it beats Jev's numbers and where it does not
Louis Grenard pointed at Laya, Convai Innovations' open typed-decision engine. Three checkpoints, a Router that picks among them, Apache 2.0 weights on Hugging Face. The author's table puts routed Laya above published Jev on some English sets and far below on Banking77. Jev was not called. JevBench's independent row is 70.1 against Jev 75.3.
GitHub Next ships LocalJev, a prompted stand-in that speaks /v1/systemone
GitHub Next published LocalJev, a Bun server that turns Jev questions into JSON-probability prompts for models running on oMLX. A 1,200-request bake-off on an M5 Max compared five 4-bit checkpoints. The authors say the probabilities are self-reported, not read from logits.
LlamaIndex's Logan Markewich ships jeff, a local System One API on GLiFormer
Logan Markewich released jeff, a self-hosted drop-in for TypeSafe's /v1/systemone endpoint backed by Knowledgator's GLiFormer. On 1,600 public items it trails Jev, especially on irony and reading comprehension, and comes in cheaper to serve on a GPU.
LiteLLM adds day-0 Jev 1.14 images, box questions, and long context on both decision routes
LiteLLM's October 15, 2026 post adds day-0 support for Jev 1.14 images, box questions, and long context on /v1/systemone and /v1/decisions. The proxy forwards TypeSafe-Danger-Zone- headers to TypeSafe only. This desk did not run the proxy.
JevBench v1.5 ranks Cygnet 73.7 and Jev 1.13.0 72.1
Opened October 19, 2026, JevBench JSON revision v1.6.29 ranks 184 open systems and 38 hosted ones. basal-omni-1.0-ultra is first of 184 on Capability at 82.5. kettei-fat stays first of 38 hosted at composite 77.8. Jev 1.14.0 is ninth at 70.2. Opened October 17, the API board named revision v1.7.46 and 39 hosted systems, with kettei-fat first at 77.8 and Jev 1.13.0 seventh at 71.5. Opened October 11, that board ranked 27 hosted systems, Sage 1.3.0 first at 74.0. JevBench v1.5.0, posted September 28, scores 1,624 decisions. The v1.5 page ranks Cygnet 73.7, Winnow-12B Q8 73.2, and Jev 1.13.0 72.1.
LangChain ran Jev as a judge: 500 matching pass/fail labels, and a $0.34 bill against Claude's $28
Daniel Shea and Seán Roche compared Jev with GPT-5.6 Luna, GPT-5.6 Terra, and Claude Sonnet 4.6 on five frozen weather-agent traces. Jev matched a human pass/fail label on all 500 repeated decisions. Continuous quality-score variance was 92 to 913 times lower than the LLM judges. Five cases, one reviewer.
Jason Lemkin tried Jev on SaaStr Connect and stopped on a 32% false-admission rate
Jason Lemkin ran 600 judgments against Claude Sonnet. Jev cost $0.064 against about $5, roughly 90 times cheaper. A blind third-model referee scored Jev 70.5% and Sonnet 77.5% on 200 random pairs. Jev let through 47 of 148 negatives. He says that is too high for Connect.
SemIf, a 4B open rebuild that reads option logits, sits 0.7 behind Jev on JevBench
Theodore Lee's SemIf (formerly OpenJev) scores typed options from a frozen Qwen3.5-4B by reading logits, not by sampling JSON. On JevBench v1.2.2 it is 74.6 against Jev 1.13.0 at 75.3. The author's own 3090 timings put direct readout at 1.023 s for 21 questions against 5.332 s for a compact JSON array.
Aman Kumar ran about 16,000 Jev calls and called it a filter, not a classifier
Aman Kumar compared Jev with gpt-5.4-mini and gpt-5.6-luna on four public sets, then on production page gates and an inbox. On short text it tied or led. On a whole-page read it fell apart. He published the public cases and per-item answers.
Bespoke Nimble 9B matches 90% of its own labels and 74.8% of a public suite
Bespoke Nimble 9B scores 90.12% vs 93.21% for Jev 1.13.0 on 324 synthetic holdout labels. On 13 public subsets the repository macro is 74.8% against Jev at 76.0%. Ollama's later mean on the same 3,880 records is 75.7% for Nimble, with Jev still listed at 76.0%. The September 24 checkpoint lengthens the context and prints no new score. llama.cpp pull 29844 names Bespoke-Nimble-9B-v3. That card prints Decision Index 0.2.1 at 56.88 and CC BY-NC 4.0. It is not the 324-row holdout.
Charly Poly put Jev next to open encoders, not GPT-5.6, and it lost on calibration
Browserbase's Charly Poly ran Banking77 against ModernBERT, BART-MNLI, and GLiClass. Jev won macro-F1 and automated about twice as much traffic at a 90% precision bar. Expected Calibration Error favored the 396M open encoder, and Jev assigned probability 0 to the right label 6.6% of the time.
TypeSafe lists nine ways Jev 1.13 is still jagged, and asks for more
Cofounder Sasha Sheng posted that Jev is still too slow, too expensive, and too dumb, and pointed at TypeSafe's jaggedness page for jev-1.13. The docs, last reviewed September 17, name nine failure modes and ask people to file more in Discord.
Jev scored 384 morning headlines in 25 seconds; Opus 5 finished four
Elvis ran a morning Google News wire through Jev and Claude Opus 5 at the same time. Jev tagged 384 headlines for 15 brand desks in 24.9 seconds at $0.19. Opus got through four headlines and cost $0.77 before the run was stopped.
OpenRouter, Cloudflare, and Netlify start serving Jev
Opened October 19, 2026, Cloudflare lists typesafe/jev-1.14.0 at $0.042 per million input tokens, output free, and a 32,000-token window. OpenRouter's typesafe/jev-1.14 listing stays 262K. GET /api/v1/models did not list that OpenRouter id. This desk did not call either host.
Venice lists Jev on its API: same key, no copy of the state
Venice put Jev on a beta Decisions API the night of September 18. Calls go to POST /api/v1/decisions with model jev-latest, billed at TypeSafe's $0.042 per million input tokens with free output. Venice says it keeps no copy of the state or the answers, and TypeSafe quoted the launch.
Almeida's bet on Jevons paradox over chatbot price cuts
In replies three days after launch, TypeSafe's founder argued that the business depends on enabling new software loops rather than capturing existing language model budgets.
TechCrunch covers Jev, and quotes a Vercel engineer who swapped Luna for it
On October 9, 2026, Marina Temkin's TechCrunch In Brief says TypeSafe raised $870 million at a $7.5 billion valuation. The round was led by Andreessen Horowitz, with Sequoia and existing investor DCVC. The startup claims a third of Fortune 500 companies are already using Jev. Tim Fernholz's TechCrunch story is the first large press write-up of Jev. It reports that TypeSafe briefly could not serve the API under demand, quotes Pranit Sharma at Vercel on a 5 to 18 times speedup versus gpt-5.6-luna, and points at Nikhil Mudholkar's email test against Gemini. On September 25 the Financial Times account posted a $200mn valuation for TypeSafe. On September 24 Stephanie Palazzolo posted that TypeSafe is raising more than $1 billion at a valuation above $10 billion, a week after a round at $200 million. Techmeme's line is "in talks," and it says PitchBook records a $40 million seed at $200 million. We did not read the newsletter.
Vercel's October 2026 AI Gateway Production Index prints Jev at 35.3% of paid teams in its first two weeks
Vercel's October 2026 AI Gateway Production Index, published October 15, prints Jev at 35.3% of paid teams in its first two weeks, nearly double Sonnet 5.5 at 18.1%. TechCrunch, October 16, 15:33 UTC, rounds that to 35%. The September 18 post still prints nearly 13% of paid teams in the first 24 hours.
Bryo ran 1,565 business emails: Jev at 96.4%, Gemini a point or two higher
Nikhil Mudholkar, CTO of Bryo AI, routed 1,565 German and English supplier emails into 10 categories. Jev scored 96.4% against Gemini 3.5 Flash-Lite at 97.5% and Gemini 3.8 Flash at 98.5%. Jev was about 10 and 22 times cheaper per thousand emails, and its high-confidence band had no errors on this set.
Tetris as a legal-move test: Jev picks from the landings you give it
Two independent demonstrations evaluated Jev playing Tetris from enumerated legal placements, comparing latency and costs against Claude Haiku 4.5 and Gemini 3.5 Flash-Lite.
TypeSafe starts pulling people off the Jev waitlist
TypeSafe began admitting waitlist users in batches, reporting 20,000 approvals in an hour while addressing browser compatibility issues.
Zachi's pg-jev judges 2,000 Postgres rows in about 3.5 seconds
Zachi's pg-jev judges a 2,000-row Postgres table in about 3.5 seconds for about $0.012. Version 0.2.0 packs 20 rows per request. The page at pgjev.com still says 40. Jose Mejias's pg-jev is a different repository.
One builder ran Jev on 1,500 of his own emails
Ryan Vogel posted a long clip of Jev classifying his mail. The sample size is real; the accuracy number is not in the post. Parallel calls were the part viewers argued about. On September 30, 2026 this desk scored 30 fictional messages on jev-1.13.0. Precision and recall were 1.0 in each of five classes. That set is not his mailbox.
A buy-or-sell every Monad block, with a mock default model
Jarrod Watts demonstrated a trading bot querying Jev for buy or sell decisions on each Monad block. The open-source repository runs in dry-run mode by default.
Every ran 777 judgments in under a second and still wanted a better accuracy check
Mike Taylor at Every put 37 documents through 21 AI-writing questions at once. Jev returned 777 answers in under 0.7 seconds for about a quarter of a cent. A later defect test with Dan Shipper missed one of seven planted problems that Claude Fable 5.1 caught.
TypeSafe leaves stealth and ships Jev, a model that refuses to chat
Former OpenAI researcher Diogo Almeida announced Jev, a System One model built for typed software decisions rather than text generation, following two years in stealth. On September 30, 2026 this desk scored 32 labeled tickets. jev-1.13.0 and gpt-6-luna were each right on 32 of 32. p50 wall time was 913.5 ms and 2,698.5 ms.
What Jev returns: System One in plain language
TypeSafe defines a System One model as parallel, typed, and calibrated decisions without text generation, distinguishing it from language models operating in JSON mode. On October 3, 2026 the confidence page prints the Choice and Score formulas. Noul still has no separate confidence field.