Nemotron 3.5 Super VL：NVIDIAの120Bオープンビジョンモデルが登場、その5倍という主張を検証するスコアボードはまだない

N​VIDIA N​emotron 3.5 Super VL shipped on October 15, 2026 — an open 120-billion-parameter vision-language model with open weights, open datasets and training recipes, and a claim attached to it that nobody can check yet. The company's launch post calls it "the fastest open agentic model in its class — 5x faster than the next best," and attributes the number to the Artificial Analysis Intelligence Index leaderboard. As of this writing, Artificial Analysis does not list Super VL at all; its N​emotron 3.5 entries stop at N​emotron 3.5 Lightning. That does not make the claim false. It makes it vendor-reported, and it should be read that way until a third party runs the checkpoint.

The other thing worth knowing up front is a three-day gap. The weights went up on Hugging Face at 2026-10-12T15:32Z — the BF16 post-trained build, the base model and the NVFP4 build all within a minute of each other — and the announcement post did not appear until October 15 at 15:00Z, with the model card's own release-date field still reading 10/15/2026. N​VIDIA uploaded first and announced later. That is now the normal shape of an open-weights release: the repository is the primary source, and the blog post is the commentary.

実際に出荷されたもの、そして出荷されなかったもの

3つのSuper VLチェックポイント、すべて現在公開中：

• N​VIDIA-N​emotron-3.5-Super-VL-120B-A12B-BF16 — the full-precision post-trained model, the reference build and the one to fine-tune from.

• N​VIDIA-N​emotron-3.5-Super-VL-120B-A12B-NVFP4 — the quantized deployment build, produced with N​VIDIA Model Optimizer and quantization-aware distillation from the BF16 model.

• N​VIDIA-N​emotron-3.5-Super-VL-120B-A12B-Base-BF16 — the pre-post-training base, for anyone who wants to run their own post-training rather than adapt N​VIDIA's.

There is a fourth repository, N​VIDIA-N​emotron-3.5-Super-VL-120B-A12B-NVFP4-LiLiCorr, which is not a model you prompt. It is a speculative-decoding draft checkpoint: load it alongside the NVFP4 target and it proposes tokens ahead of the main model. Its own card dates it 10/15/2026 and describes it as intended for LiLiCorr deployments tuned for DGX Spark and low-concurrency data-center work.

Two things did not ship, and each matters. There is no price. N​VIDIA's announcement states the model is available as an N​VIDIA NIM microservice — a production microservice the company says is refreshed every two weeks with security patches and performance work — but publishes no per-token figure anywhere, for NIM or for the third-party platforms carrying it. And there is no independently measured speed number, for the reason above. Everything in the next two sections is N​VIDIA's own reporting.

アーキテクチャを1段落で。それがデプロイの話を説明してくれるから

Super VLは、Mamba-2とTransformerを組み合わせたハイブリッド型Mixture of Expertsです。Mamba-2、MoE、アテンションの各層が交互に配置され、各MoE層には512個のルーティングされたエキスパートがあり、さらにC-RADIOビジョンエンコーダと、言語バックボーンに接続された視覚言語コネクタを備えています。総パラメータ数は120B、トークンあたり12Bがアクティブです。コンテキストは最大1Mトークンに対応し、Multi-Token Predictionがネイティブに組み込まれており、LiLiCorrドラフトチェックポイントはこれを活用しています。入力はテキスト、画像、動画——動画は最大2分のMP4で、フレームとしてサンプリングされます——で、文書はページ画像と付随するテキストとして入力できます。出力はテキストのみです。

That shape is why the deployment table is the most useful part of the documentation. N​VIDIA's BF16 build wants 4× H100 or 2× GB200. The NVFP4 build wants 1× DGX Spark, 1× H200, 2× H100, or 1× GB200. The gap between those two lines is the whole reason the quantized checkpoint exists, and it is the difference between "a 120B model you rent" and "a 120B model on the desk."

The benchmarks N​VIDIA published, and the two problems with them

Label all of this vendor-reported. N​VIDIA evaluated Super VL through NeMo Gym and published the recipes, containers, prompts and scoring settings — which is more than most labs do — but N​VIDIA trained the model, chose the baselines and ran the harnesses. Nothing here has been reproduced outside the company.

The agentic numbers are the ones the launch leans on. SWE-bench Verified 71.10, SWE-bench Multilingual 67.72, SWE-bench Pro 51.78, tau2-bench retail 69.24, airline 76.50, telecom 73.19, BrowseComp 78.91, and GraphWalks 89.71 at the over-1M-token setting. On reasoning, GPQA-Diamond 89.52 and HLE without tools 31.79, rising to 46.01 with tools — that last figure, N​VIDIA notes, comes from a separate internal harness rather than the standard path.

On vision, which is the new capability relative to the text-only N​emotron 3.5 Lightning from August: CharXiv-RQ 80.82, MathVision 78.16, V* 85.60, OCR-Reasoning 65.08, OCRBench-v2 at 68.12 English and 59.15 Chinese, MMLongBench-Doc 62.82, OSWorld 52.14, and Video-MME-v2 32.85.

さて、ここで2つの問題です。

First, the Terminal-Bench 2.1 figure is stated twice and does not agree with itself. The model card reports 66.70. The technical report's summary table reports 60.67. Everything else in those two documents lines up, so this is not two different models — it is one number carrying two values in N​VIDIA's own paperwork, and until the recipes are re-run it is not safe to quote either. That is the sort of thing a single independent replication settles in an afternoon, which is exactly why the absence of one matters this week.

Second, the headline efficiency claim and the headline intelligence claim pull in opposite directions, and only one of them is checkable. N​VIDIA says Super VL reaches accuracy on multimodal agentic tasks "comparable with N​VIDIA's largest reasoning model at roughly one-fifth the parameter count" — N​emotron 3 Ultra 550B A55B has 55B active parameters against Super VL's 12B, a ratio of 4.6×, and the report says Super VL's 51.78 on SWE-bench Pro does surpass Ultra. Fine.

But look at the columns where Super VL is genuinely behind. MMLU-ProX 76.91, against Qwen3.5-122B-A10B at 83.2 and Qwen3.8-Flash-Next at 83.0. AA-Omniscience accuracy 26.20, which is close to the floor of that benchmark and sits below DeepSeek-V4-Flash-0731's 45.0. Video-MME-v2 32.85 against Qwen3.8-Flash-Next at 45.1. BabyVision 29.60 against 46.8. Agentic-terminal at 60.67 or 66.70 against 85.80. Super VL is not the accuracy leader in this weight class on most rows; it is a 12B-active model that N​VIDIA is selling on speed and on the breadth of what it ingests in one call, not on beating everything at everything.

効率の数値のほうが、実際に興味深いものだ

知能の比較を除けば、エンジニアリングの数値は具体的だ：

• Speculative decoding — N​VIDIA's post-training MTP boosting lifts average acceptance length from 4.287 to 4.718 tokens under greedy decoding and 3.787 to 4.612 under probabilistic draft sampling.

• Quantization — the released NVFP4 checkpoint recovers 99.62% of BF16 accuracy as a median across 36 benchmarks, so the deployment build is close to lossless by N​VIDIA's own measure.

• スループット — 20ターンのエージェント型プロキシワークロードにおいて、単一のGB200で最大4,856出力トークン/秒/GPUを達成しつつ、ユーザーあたり少なくとも50トークン/秒を持続。同一条件下でNVFP4はBF16の2.43倍のスループット。

• 相対スループット — そのワークロードにおいて Qwen3.5-122B-A10B の 2.34 倍、Qwen3.8-Flash-Next の 1.90 倍。

• Serving stack — on N​VIDIA Dynamo, N​VIDIA claims up to 8× more concurrent users at 85% lower latency.

それらはどれもベンダーの公称値だ。その中で誰にとっても最も偽りやすいのは、4,856 tokens/s/GPU という数値である。なぜならそれは、特定の GPU と特定のワークロードの特性であって、スコアリングハーネスの背後で行われるベンチマーク実行の特性ではないからだ。

それを提供すること、そして2つのビルドがハードウェア面でどれほどの費用になるか

Both vLLM and SGLang are named as supported runtimes, and N​VIDIA ships working launch configurations: four-way tensor parallelism with three MTP draft tokens on 4× H100 for BF16, two-way with five draft tokens on 2× GB200. Hardware coverage is Hopper and Blackwell, Linux only, and N​VIDIA lists DGX Spark, DGX Station, RTX PRO, RTX and Jetson on the client side — the same silicon spread the model is positioned against.

ローンチ日にこれを読んでいるチームにとっての実践的な判断は、どのチェックポイントを取得するかだ。ファインチューニングするなら、BF16ビルドだけが妥当な出発点になる。NVFP4ビルドはすでに量子化認識蒸留を経ており、さらなる学習はそれとうまく噛み合わないからだ。サービングするなら、NVFP4ビルドが単一のH200やDGX Sparkに収まるものであり、中央値99.62%のリカバリは、特定の退行を実測していない限り、それをデフォルトにする。ベースモデルの取得が意味を持つのは、独自のポストトレーニングパイプラインを書くつもりである場合だけで、それは別のプロジェクトだ。

Super VLは実際には何のためのものか

Read N​VIDIA's own framing and the target is narrow and specific: always-on enterprise agents that digest visual documents. The announcement's example is a web page — extract the text, read the charts, return findings — and the same claim is extended to PDFs, tables and structured documents. The argument for one model instead of a text model plus a separate vision pipeline is context continuity: the document, the data and the decision stay in the same window rather than being handed between systems.

その主張はスペックシート上では信頼できる。コンテキストウィンドウが100万トークンで、ビジョンエンコーダが後から付け足されたのではなく共同でトレーニングされているからだ。100万トークンで実際のドキュメントと接触しても耐えられるかどうかは、まさにGraphWalksとMMLongBench-Docの数値が狙っている問いであり、両方ともベンダー報告である。

N​VIDIA also lists a long adoption roster in the launch post — Scale AI building agents for Skydance and EY, AfterQuery for Hebbia's credit-agreement analysis, AI21 and Alice on cybersecurity, Mercor on coding agents, plus Atlassian, CrowdStrike, Dassault Systèmes, Perplexity, Salesforce and Synopsys among integrators. Those are partnerships named in a press release, which is a statement of intent rather than evidence of production traffic. Treat the roster as a signal about which verticals N​VIDIA is targeting, not as a reliability claim.

本番の経路をそれに賭けずに試す方法

今週の正直な見解としては、Super VL は興味深く、十分に文書化された、まったく検証されていないオープンモデルであり、検証されていないオープンモデルを評価する正しい方法は、初日から本番トラフィックをそれに流すことではない。

Locally, the NVFP4 build on a single H200 or a DGX Spark is the cheap test: pull the checkpoint, run your own document set through it, and compare against whatever vision model you use today. That costs you GPU time and nothing else, and it is the only way to get a number that is not N​VIDIA's. N​VIDIA's own documentation gives you one free head start: the model card and the technical report both specify temperature 1.0 and top_p 0.95 for text, and temperature 0.6 with top_p 0.95 for vision — a starting point you would otherwise spend a day rediscovering.

If you would rather test it behind an API, the availability line matters: Super VL is on Hugging Face, as N​VIDIA's own NIM microservice, and through several third-party platforms. N​VIDIA has published no price for any of those paths, so any per-token figure you see this week is set by the platform, not by the vendor. On the OrcaRouter side, N​emotron 3.5 Super VL is not on our catalogue — we do not serve it today, so there is nothing here to try it through yet, and we would rather say that than imply otherwise. Everything we do serve sits behind one key across 200-plus models at provider list price with 0% markup, and where a model is available from more than one provider, automatic failover across providers gives you a way to route to a new model on a fraction of traffic and let it fall back to the incumbent when it stumbles. That is the mechanism this kind of launch wants; it is not an offer to serve this particular checkpoint.

One more piece of the release worth a look regardless of whether you want the model: N​VIDIA published its post-training datasets alongside it — spans covering agentic tool use, finance, instruction following, long context, fairness and vision-language understanding — plus the RL environments in NeMo Gym. The dataset release is often more reusable than the checkpoint, and it is the part of this launch that costs N​VIDIA the most to give away.

何を見るべきか

この記事を変えるであろう4つのことを、おおよそ重要度の高い順に挙げる。

Artificial Analysis であれ他の誰であれ、5倍という主張が示唆する Super VL の行を提示するか、そうすることを拒むか、そのどちらかを行う最初の独立系ベンチマーク。それが存在するまでは、このローンチの中心的な効率性の主張は、リーダーボードの名前が添えられたプレスリリースの一文にすぎず、その留保条件はその数字と同じ息遣いで語られるべきだ。

A price. N​VIDIA has told you the hardware, the runtimes and the licence, and withheld the one figure a buyer needs to compare this against a closed model. When NIM pricing appears, the comparison becomes arithmetic instead of positioning.

The Terminal-Bench 2.1 discrepancy — 66.70 on the model card against 60.67 in the technical report. Whichever is right, one of N​VIDIA's two primary documents is wrong, and a corrected re-run would settle several other rows at the same time.

And the walk from "available now" to "actually served." Super VL's benchmark rows compare it against Qwen3.5-122B-A10B, Qwen3.8-Flash-Next, DeepSeek-V4-Flash-0731, GLM-5.3-Flash and N​emotron 3 Ultra 550B A55B — all of which are mature enough to have measured throughput, real prices and production track records. A 12B-active model that ingests video, 1M-token documents and tool calls in one pass is a genuinely useful shape. Whether it earns a routing rule is a question about latency and price stability under load, and N​VIDIA has published the first of those only on its own hardware, under its own workload, in its own units.

ベンチマークは古び、価格は動くが、失敗の記録は動かない。このクラスのモデルの背後にある、記録に残されたAIインシデントとエージェントの失敗はOrcaRouter AI Incident Archiveに集められており、各エントリには独自の情報源、重大度、信頼度グレード、係争中フラグが付いている。