Moondream 3 vs LFM2.5-VL 3B：一方は答えを指し示し、もう一方はただ答えるだけ

The most useful difference between M​oondream 3 Preview and LFM2.5-VL-3B is not in their benchmark tables. It is that M​oondream 3 Preview reasons about an image by pointing into it — its reasoning traces are grounded, referencing specific regions — while LFM2.5-VL-3B is explicitly a non-reasoning model that answers directly, a choice Liquid AI made to keep latency low for on-device work. Same image in, two entirely different things come out, and which one you want depends on whether a downstream system has to trust the answer or merely consume it.

Everything else follows from that fork. M​oondream 3 Preview is a sparse mixture-of-experts model at 9B total parameters with 2B active, released on 18 September 2025 by M​oondream, which was acquired by Prime Intellect in October 2026. LFM2.5-VL-3B is a dense 3.1B model — a 2.6B language backbone with a 400M SigLIP2 NaFlex vision encoder — released by Liquid AI on 12 August 2026 as a direct successor to LFM2-VL-3B. One is a specialist that was built to detect and point and can also answer questions. The other is a generalist that was built to answer and recently learned to point.

最初に気づくべきメトリックの衝突

二つのグラウンディング数値を並べてみると、ほぼ互角に見える。しかし、同じ測定ではないのだから、そうではない。

M​oondream's launch chart lists M​oondream 3 Preview at 91.1 on RefCOCO. Liquid AI reports LFM2.5-VL-3B at RefCOCO precision@1 of 87.9. RefCOCO accuracy and RefCOCO precision@1 are different statistics computed over the same dataset, and a 3.2-point gap between them tells you nothing about which model localises better. Anyone stacking those two figures in a bar chart is comparing a number to a differently-defined number.

• M​oondream 3 Preview grounding — RefCOCO 91.1, on a chart where frontier baselines (GPT-5, Gemini 2.5-Flash, C​laude 4 S​onnet) were queried through a template because they have no native grounding, and where some preview scores came from a 100-question sample rather than the full set. Vendor-reported and, as M​oondream says itself, not a fair fight.

• LFM2.5-VL-3Bグラウンディング — RefCOCO precision@1は87.9で、前世代のLFM2-VL-3Bの57.1から上昇しており、Liquidはこれを合成グラウンディングデータのスケーリングによるものとしている。ベンダー報告値であり、自社の前世代モデルとの同一ハーネスでの比較である点が、より意味のある評価軸である。

• Detection and pointing — M​oondream 3 Preview: native detect and point skills, structured coordinate output, part of the architecture. LFM2.5-VL-3B: grounding added by training, returned as coordinates the same way, with function calling also new to the VL line (ToolSandbox 26.4 to 59.5, BFCL v4 20.5 to 32.5).

The honest read: Liquid AI has a clean before-and-after on identical harnesses and M​oondream has a cross-model chart with template caveats. Neither gives you a head-to-head. If grounding accuracy is the deciding factor in your evaluation, your own labelled set is the only instrument that will settle it.

根拠に基づく推論があなたに強いる代償

M​oondream's query skill runs in reasoning mode by default. The model works through the question and its trace references the regions of the image it is using, which you can inspect in the playground by hovering over the text. For anything where a human has to audit the answer — a defect call on a production line, a security incident, a medical triage flag — that property is worth a lot. It is the difference between a model that says "no" and a model that says "no, here." p>The cost is tokens. Reasoning mode generates a trace before the answer, and M​oondream's own documentation offers the escape hatch: pass reasoning=False for simple questions, because the company knows the mode is not free. That is a real architectural admission — a well-designed model would not need a flag to switch the thought off.

LFM2.5-VL-3B に相当するものはない。これは非推論モデルとして設計されており、Liquid AI はそのトレードオフについて率直だ。直接答えることで、リアルタイム用途やオンデバイス用途の遅延を低く保てる。検査できるトレースはなく、無効化できるモードもない。レジでバーコードを読み取る場面や、スマートフォンがメニューを翻訳する場面では、それが正しいエンジニアリング上の判断であり、トレースがないことは欠陥ではない。

So the question is not which is better. It is whether anything downstream of your model reads the reasoning. If a human does, M​oondream's grounded traces are the feature you are buying. If only a program does, you are paying for tokens nobody will read.

3.1Bが実際に優位に立つ場面

LFM2.5-VL-3Bのサイズに関する主張は、マーケティングではない。Apple M5 Maxで毎秒228トークン、AMD Ryzen AI Max+ 395で毎秒116トークンという性能を、3.3 GB未満のメモリで実行し、ネイティブのチェックポイントと併せてGGUF、ONNX、MLXのエクスポートも公開されている。これはノートPCに載せられるモデルであり、Liquid AIはブラウザタブで動作するWebGPU版のHugging Face Spaceとしても提供している。

M​oondream 3 Preview has a smaller active parameter count — 2B against 3.1B — but the comparison is not symmetric, because a MoE checkpoint has to be fully resident even though only a fraction of it fires. The 9B of weights need somewhere to live. M​oondream's own answer to that is Photon, its local inference engine, which runs the model on N​VIDIA GPUs from Ampere onward and on Apple Silicon Macs. Different constraint: LFM2.5-VL-3B is small enough to fit anywhere, M​oondream 3 Preview is fast in the compute sense but large in the memory sense.

• Context — both 32K tokens. Genuine convergence, and worth noting given the two-year jump from the 2K windows of the previous M​oondream generation.

• Languages — LFM2.5-VL-3B lists sixteen: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish, and doubled its vocabulary to 128,000 to support non-Latin scripts. M​oondream 3 Preview's card leads on visual capability rather than a language list; treat multilingual VQA as something to test rather than something promised.

• Document reading — LFM2.5-VL-3B: DocVQA 91.1, ChartQA 81.3, OCRBench v1 84.2, OCRBench v2 English 47.5, MMMU 48.4. M​oondream 3 Preview: DocVQA 88.3, ChartQA 86.6, POPE 89.0. Different harnesses, vendor-reported on both sides.

• LFM2.5-VL-3B が「やらないほうがよい」と教えてくれること — Liquid AI のカードには、ビジュアルなウェブデザインや設計図に関する技術的な質問など、長いコンテキストや推論を要するタスクには推奨されないと明記されている。この一文は、たいていのベンチマーク表より役に立つ。

デプロイ、そしてルーターがどこに当てはまるか

Neither of these models is served through OrcaRouter, and it is worth saying so directly rather than dancing around it: both are open-weight checkpoints you run yourself, and M​oondream 3 Preview's hosted endpoint — M​oondream Cloud — is being shut down on 13 November 2026 following the Prime Intellect acquisition, with unspent credits refunded.

それが変えるのは、決断の形だ。セルフホスト型のVLMに標準化しようとするチームは、たいてい、ローカルのモデルが苦手とするケースのために2つ目のホスト型モデルを抱え込むことになり、その時点で問題になるのは統合作業だ。2つのSDK、2つの認証方式、2つのリトライポリシー、そしてベンダーの障害がそのまま自社の障害になる。

This is the case OrcaRouter's routing DSL is built for. You compose several models into a single call — the 3.1B local model answers what it can, a hosted model takes the residue — behind one key for more than 200 models, with provider list price passed through at zero markup and automatic failover when an upstream degrades. Since neither M​oondream 3 Preview nor LFM2.5-VL-3B is in that catalogue, the routing layer sits above them rather than replacing them: it is how you keep the cheap local path cheap without making a vendor outage your outage.

ライセンスの分岐点は、ベンチマークよりも鋭い

ライセンスに関する2つの異なる思想があり、その違いは3点のスコア差よりも製品の意思決定にとって重要だ。

M​oondream 3 Preview ships under the Business Source License 1.1 with an Additional Use Grant (No Third-Party Service). Internal and production use, personal projects, research, benchmarks, fine-tunes, merges, quantisations, weight deltas, and use inside a product that does not substantially overlap M​oondream's paid offerings are all permitted. Selling a hosted API that exposes similar vision capabilities, managed "M​oondream-as-a-service", or embedding the weights in a paid SDK that delivers comparable capabilities requires a separate agreement. M​oondream 3.1 moved to the M​oondream Model License 1.0, which states the same rule more cleanly: component of your product is allowed, access to M​oondream itself as the product is not. Edge devices, customer-controlled VPCs, and running the model on leased cloud or neocloud GPUs are all explicitly on the allowed side.

LFM2.5-VL-3B is under Liquid AI's own LFM Open License v1.0. Read it before you ship, as you would any bespoke licence, but the families are different in kind: M​oondream's restriction targets one specific business — reselling the model — while Liquid's is a broad open licence with its own terms.

If you are a domain-specific builder, both are fine and the licence question is a formality. If your business is anywhere near "we host vision models for other people," M​oondream's licence is the constraint you read first and LFM's is the one you probably clear.

どちらを選ぶべきですか

Choose M​oondream 3 Preview if the answer has to be inspectable. If a human reviews the output, if the trace needs to show which part of the image drove the decision, if detection and pointing are the primary product rather than a feature — that grounded reasoning is the whole reason to pay 9B of memory for a 2B-active model. Budget for Photon or a GPU, and plan around the hosted endpoint closing.

選ぶべきLFM2.5-VL-3B、モデルをデバイス上に置かなければならない場合だ。スマートフォン、ノートPC、工場のフロアのボックス、ブラウザのタブへ出荷するのであれば、そしてワークロードが単発・高スループット・低レイテンシ——画面の読み取り、文書の文字起こし、ストリーム内の物体検出——なのであれば、考えずに答える密な3.1Bモデルこそがまさに求めているものであり、M5 Maxでの毎秒228トークンという数字がその決め手になる。

Pick both if you have the budget for it, and route between them: LFM2.5-VL-3B on every frame because it is nearly free to run, M​oondream 3 Preview on the frames where a grounded answer is worth waiting for.

数字からは分からないのは、それが現場で何か問題を起こしていないかどうかだ。このクラスのモデルに関する、記録されたAIインシデントやエージェントの障害は、OrcaRouter AIインシデントアーカイブに集められており、各項目にはそれぞれの情報源、深刻度、信頼度グレード、異議ありフラグが付されている。