Jev was asked to take the throne: StartLux.

9月30日、上海AI、StartLux、完全オープンソースの決定モデルをリリース。 StartLux-DecisionA one-time launch of 0.8B, 2B, 4B, 9B, 27B, 5. According to the evaluation published by the team, its flagship 27B version received 63.88 points on Decision Index 0.2.1, more than Jeff 1.13 (57.91), the first on the list.決定モデルの新しい王は、骨につながっています。お問い合わせ

より興味深いのはR & Dの率です:The entire model was only 3 days to complete research and validation.I don't know. Behind this is the Auto Research line built by StartLux -- officially, it's one of their core paths to advance RSI.

Let's start by saying, this is a team test, not an official list.

これは明確でなければなりません。そうでなければ、タイトルを上回るのは簡単です。

StartLux's grades are:Decisión Indexの公式評価ツールを使用して、チームは2026年9月28日に完了した公開リストショットに対して自己測定I don't know. In other words:

• They're using the checklist assessment tool and the same day's comparison data.
• しかし...自分の成績は公式リストではありません。「正式に認定されていない」という公式記事にも書かれています。
• お問い合わせTop score on the public list.(2026-09-28)

So the exact answer is:"StartLux-Decisión has surpassed the top of the current public list by team self-measured."I don't know. It's not demeaning — it's self-measured that means strength, but the reader needs a scale. All the numbers below are taken with the default premise of "team self-measurement".

What is it: a decision model that does not generate text?

Start by understanding the fundamental difference between the StarLux-Decisión and the general large model. It's not a generation model.自然言語を記述しないお問い合わせ

それがどのように機能するかです。ステータスAdd a groupお問い合わせ— the selection of the theme (one from several options), is not, or is graded. The state can be text, JSON or picture, up to 256 K token.Returns the probability of each question to each optionお問い合わせ

The point is in the last sentence:It doesn't produce anything.I don't know. The answer is read directly from the option letter after a forward transmission. So a short question takes a few milliseconds and the probability can be used directly as confidence.

This solves a particular kind of work in a smart body:HF, clear candidate setThe judgment. For example, the passenger service order came — “the order is repeatedly withheld but still shows no payment” — and the same information can ask three questions at the same time: which team was given it, whether it is being processed today, and how much impact it has had. The traditional approach is to allow the generation model to write a piece of it, which is slow and easy to interpret errors; one request from the StateLux-Decisión can complete the three judgements together.

Report cards: 63.88 vs. 57.91,38 out of 31 won

A comparison of Decision Index 0.2.1 (both 2026-09-28 snapshots):

• StartLux-Decision-27B:63.88
• Jev 1.13:57.91(先着)
• ルーン 26B-A4B: 57.44
• デビダーチャット 31B:57.33
• AutoJev-27B: 56.40
• StartLux-Decision-9B: 58.639Bはジェフの先にあります。

ジェブよりも27B高い 5.97 分, of the 38 benchmarks contained in the index,There are 31 higher scores.お問い合わせ

しかし、公式の引用符に注意して下さい:「組み合わせたスコアは、全ての能力が強まるという意味ではありません。」I don't know. This sentence is worth reading.

勝つ場所, 失う場所.

In the area score for Decisión Index, 27B leads in four areas, but loses to Jev:

• 言語の理解74.5対Jev 62.0(高 12.5)
• Search and classification:66.8 vs 55.4 (high 11.4)
• ツールと自動化:82.2 vs 75.1 (high 7.1)
• Art and human preferences:47.9 対 37.7
• Knowledge and reasoning:44.3対Jev 51.4(Later 7.1)

This distribution is very illustrative. StartLux-Decision Strong“understanding conditions, distinguishing among candidates, choosing the next course of action”— It happens to be the three types of capabilities that are most needed in the day-to-day implementation of intelligence; it is not superior to purely intellectual reasoning. So the conclusion is not "who's crushing who," but...The selection depends on what kind of judgment your intelligence is most likely to encounter.お問い合わせ

A similar division exists on individual items. For example, API-Bank, which does not lead, Home applications pull from 52.3 in Jev to 77.3, and POP909 from 15.9 to 50.6. These are the scores of Decisión Index that have been corrected by random baseline.Could not directly be considered as the accuracy or completeness of the original version of the baselineお問い合わせ

Seven public tests: 91.82% average, few mistakes 8

In addition to Decisión Index, the team ran away from Shanghai's artificial intelligence lab to launch the Inter-Design.7組のテスト(JevBench EASY/Original/Hard, Typed Decisions, ToolaCE, AG News, WildjailBreak):

• StartLux-Decision-27B average accuracy rate 91.82%
• Jev 1.13:88.74%
• Inter-Decisión-4B: 90.02%

Publicly, JevBench has 231 questions, three answers. 208 / 199 / 201 Question - 27B vs. 9 more than Jeff. In the hardest JevBench-Hard (111 open dilemmas), 27B error 23, compared to Jev 31質問8お問い合わせ

Here, too, there is a lot to be noticed: the public JevBench’s 208/231 is just an open question.It's not an official score of classified tests, speed and cost.; additional training data used the open training set of 14 of these benchmarks and the corresponding test questions were filtered out (official label).

Speed: milliseconds, all problems counted forward at once

The value of the decision model is half right, half fast. In a short request test for card H200, BF16, local HTTP,Three questions at a time.時間の消費:

• 0.8B:12.2ミリ秒
• 2B:15.5ミリ秒
• 4B: 26.0 ms
• 9B:35.7ミリ秒
• 27B:102.3ミリ秒
• 35B-A3B (MoE): 52.5 msEvery token in 35B only activates about 3B, twice as fast as 27B.

When you answer only one question, 4B on average. 14.7 msI don't know. Optimization within the same model is more indicative of engineering gains: 4B CUDA Graph was 90.3 ms when it was not enabled, down to 26.0 ms when enabled- More than three times the promotion depends on engineering. The project also uses quick linear attention algorithms and consolidates multiple issues into a forward calculation to reduce double calls.

参照として、Jev 1.13は同じ種類の3つの要求で、APIゲートウェイによって報告されたサービス終了は 64.0 msI don't know. However, the authorities have restrained themselves from reminding:Hardware and service depots vary from one party to another and cannot be used as a basis to conclude that hard hardware is the same speedand these milliseconds are not the time to complete the entire web-page task (both input length, page loading, tool execution). Rather, this self-contained expression is the more credible part of the publication.

Field presentation: from browser operation to game

The official presentation covered several directions, with the core being "models for decision-making, implementation systems for action":

ブラウザ/ウェブ操作: Each step asks two questions — which controls are the next operation and whether the task is completed. In the demonstration, the filtering list was completed for "Buy the cheapest, free of charge, 8 AA batteries sent home" and the designated members were invited to the Design team and set up as Editor. Attention to official statements:制御された環境以下は連続運転です。It's not the same as free entry on any website., the input box also completes the selection through the candidate proposal.

チェス: StartLux-Decisión-27B and Jev 1.13 compete, neither search, and the code never overwhelms the selection of models (Stockfish only draws assessment bars). It's just a matter of time.256, 27B, 58.4%.お問い合わせ

ゲーム: Mario (simulate 11 candidate actions first, write the results into text before selection), StarCraft II ( ask three questions every 12 seconds of game time: what to build, what to give priority to, what to attack/defend/collect), DOOM (each 4 Tic judge targets, fire, move), JevBall (one request for separate action for several players near the ball). In the same vein:These are not a uniform Direct Read Screen test., there are scripts for perception and execution, and the model is only responsible for the part of decision-making defined by the interface.

And there's one more detail that's interesting -- "Hanna, have you seen Jeff today?" There's Hannah and Jeff in that sentence. The system has to judge.He's been questioned about Hannah.Instead of having another actor respond because of Jeff. It's taken separately to determine whether the model understands the conversation or only matches the name.

Five sets of specifications + MoE + Quantified, locally deployed friendly

StartLux-Decistion provides a five-tier dense model (0.8B / 2B / 4B / 9B / 27B), plus one 35B-A3B Mixed Expert (MoE)Version. October 3rd.Picture input and 256K token contextEach one of them can be read as evidence.

ローカル展開はフォーカスです: llama.cpp は各トランシュのために利用可能です GUF Files, including BF16 to retain original accuracy, and two quantitative formats Q8 0, Q4 K M,15 GGFエントリーI don't know. Take the example of 4B:

• Q8 0 ファイル 4.48 GBIn 231, on JevBench with the original right Heavy意思決定の一貫性率 100%(204年のサメ回答)
• Q4 K Mプレス 2.71 GB98.3%、はい201

Perhaps you should pay particular attention.: Code uses Apache-2.0, butModel weighting with CC BY-NC 4.0 - Research and non-commercial only, commercial use subject to a separate StartLux Labs authorizationI don't know. This is different from the “full open source” propaganda, which must be confirmed before commercialization.

The interface is compatible, Jev. Probability can be diverted.

There's a smart design at the access level: StartLux-Decisión with TypeSafe /v1/systemone Interface format,Jevのクライアントはそのまま使えます, business processes retain the current `state plus problem' mode of call and change the service address. The interface (llama.cpp load model with accompanying decision-making services responsible for organizational issues and reading out options) is also maintained for GGF deployment.

Another mechanism of concern is確率分散Since each candidate has a probability, the system can directly implement decision-making in a “validated routine scenario”; when the candidate is difficult to distinguish, is in inadequate condition or has a higher operational risk, it can be followed up with additional observations, submitted to a large model analysis, or requests for manual confirmation.

政府はまた実質の警告を与えます:Probability output does not equal the real success rate, and the diversion threshold is aligned with operational data calibration.; payment, deletion, authority to modify such operations shall remain subject to the original authorization and confirmation rules - - The decision model is the next step for the system to judge, not to replace these constraints.

自動研究開発とRSIの背後にある

StartLux に応じて 5 段階モデルを作る 3 日間。 Auto Research Development Pipeline(Constructed by co-founder and CTO Guo Guo Guo Jiu) According to a previous interview with the machine heart, in this approach, AI did not only carry out pre-set experiments, but also...Analysis of failures based on the previous round, presentation of new assumptions, participation in deciding what to look at next; 研究の目的、評価基準およびキー規則が開いたままである間。

StartLuxのシステム部門では、StartLux-27Bは、一般的な機能層、高周波数決定層に焦点を当てたDecisiónモデルを運ぶAnd above it is Agent and Memoory, the bottom is supported by quantification, reasoning systems and hardware. So Decisión is not an isolated release made in pursuit of hot spots, but a piece of this architecture that was built up as planned.

Guo Quan defined Auto Research as the core element for achieving RSI: First, to involve AI in research and model improvements, then to further request “improved AI, even the ability to improve AI itself has been enhanced”. StartLux-Decision is a concrete result of this line.

お問い合わせ

Jevをスローンの上に置きます。A Chinese team at the bottom of Agent's Decision Level.I don't know. The idea that Jev brings — using a dedicated model to take on high frequency, to stand for a clear judgement rather than allowing the generation model to be both a referee and an athlete — is proving to be a real need. StartLux uses an open source, a fifth set of specifications, local deployment and a Jev compatible interface to make the road more “landable”.

しかし3つのリマインダーはまだ立ちます:

• It's a team test.公式リストに認定されていません。
• まずは、It still lags behind in intellectual reasoning, Jeff. It depends on the distribution of tasks.
• The weight is CC BY-NC 4.0., "Full open source" only for non-commercial scenarios, with commercials authorized separately

In any case, it is worth remembering that a relatively young Shanghai team, three days of making a five-stage decision model and surpassing the current top of the public calibre.Agent's “judgement level” is becoming an independent and deserving technology track.

The decision model is not intended to replace the big model, but rather to make less of the trivial judgements it should not make.