LongCat-Flash-Omni and DeepSeek V4-Flash-Vision-Exp: Omni-Modal vs Vision-Exp Compared
A source-based comparison of LongCat-Flash-Omni (560B open-weight omni-modal model, MIT License, text+image+audio+video in one framework) and DeepSeek V4-Flash-Vision-Exp (experimental vision model, text parity with V4-Flash, multimodal agent capability near Opus 4.8, API-only). Covers modality scope, benchmarks, pricing, licensing, and deployment.
Independent third-party resource. Not affiliated with or endorsed by LongCat, Meituan, DeepSeek, or any other publisher discussed on this page.
Published: 2026-08-22 · Author: LongCat Community Hub editorial team
This is an independent, source-based comparison of LongCat-Flash- Omni (Meituan) and DeepSeek V4-Flash-Vision-Exp (DeepSeek) — the first multimodal-line comparison on this site. It compares documented modality scope, benchmarks, pricing, licensing, and deployment options. It does not report performance as a verdict — for publisher-reported scores, see the individual model pages and benchmark index linked below. Every claim on this page is sourced from the linked publisher documentation or attributed third-party coverage.
| Dimension | LongCat-Flash-Omni | DeepSeek V4-Flash-Vision-Exp |
|---|---|---|
| Developer | Meituan (LongCat team) | DeepSeek |
| Release date | November 3, 2025 | August 21, 2026 (experimental) |
| Parameters | 560B total (MoE), 27B activated | Based on V4-Flash (284B total, ~13B active) |
| Modality scope | Text + image + audio + video (input and output) | Text + image input; text output (vision) |
| Real-time interaction | Yes — streaming audio-visual, low-latency chunk-wise interleaving | No — standard request-response API |
| Context window | 128K tokens | 1M tokens (V4-Flash base) |
| Vision benchmarks | MMBench-EN 87.5, DocVQA 91.8, OCRBench 84.9 | Agent-focused: ApexBench 36.5, ZeroBench 35.0; multimodal agent near Opus 4.8 |
| Omni-modal benchmark | Omni-Bench 61.38 (open-source SOTA) | Not published (vision-only focus) |
| Audio capabilities | ASR (LibriSpeech CER 1.57), S2TT, TTS, audio understanding | None documented |
| Image input cost | Self-hosted (no per-image fee) | Max 384 tokens/image; pricing identical to V4-Flash (input ¥1.5–3/M, output ¥4.5–9/M) |
| License | MIT (open weights, released) | API-only (experimental); open-weight release not yet confirmed |
| Deployment | Self-hosted; LongCat App (iOS/Android) | DeepSeek API only (Chat Completions / Messages / Responses / Anthropic-compatible) |
Two Different Meanings of "Multimodal"
DeepSeek V4-Flash-Vision-Exp, launched August 21, 2026 as an experimental model, extends the V4-Flash text model with image understanding: text input plus image input (JPEG/PNG/GIF/WebP, up to 600 images per request) producing text output, with the multimodal agent capability reportedly approaching Anthropic's Opus 4.8 on agent benchmarks. Its text capability is documented as on par with the V4-Flash release.
LongCat-Flash-Omni, released November 2025, is a different kind of multimodal model: a 560B-parameter omni-modal framework that processes text, image, audio, and video — as input and output — in a single end-to-end model, with low-latency streaming interaction designed for real-time voice and image dialogue in the LongCat App. One is a vision extension of a text agent; the other is an omni-modal interaction system. They overlap on vision input but are positioned for different workloads.
Modality Scope and Real-Time Interaction
The clearest difference is modality breadth. LongCat-Flash-Omni documents audio input and output (ASR with LibriSpeech CER 1.57, speech-to-text-to-speech, TTS), video understanding (MVBench 75.2, VideoMME 78.2), and image understanding (MMBench-EN 87.5, DocVQA 91.8, OCRBench 84.9) — with chunk-wise feature interleaving enabling the model to process audio as it streams and begin responding before the input is complete. DeepSeek V4-Flash-Vision-Exp documents image input and text output only, without streaming or audio capability.
For teams building real-time voice agents or multimodal conversational products, LongCat-Flash-Omni's documented streaming and audio path is the differentiator. For teams whose need is image understanding inside an existing text-agent workflow, DeepSeek's vision extension slots into an established API with minimal integration cost. These are complementary use cases rather than directly competing capabilities.
Benchmarks: Different Suites, Don't Cross-Compare Blindly
LongCat-Flash-Omni reports open-source SOTA on Omni-Bench (61.38) and strong vision scores (MMBench-EN 87.5, DocVQA 91.8, OCRBench 84.9), all publisher-reported. DeepSeek reports text-parity with V4-Flash on agent/reasoning/world-knowledge suites and large jumps on visual-agent benchmarks (ApexBench 26.2 to 36.5), with independent attribution noting the model's multimodal agent capability approaching Opus 4.8.
These figures come from different evaluation suites and measurement regimes, so direct comparison is misleading. The two models' reported strengths reflect their different designs: DeepSeek's agent-centric vision benchmarks versus LongCat-Flash-Omni's omni-modal and vision-understanding suites. Evaluate on the benchmark closest to your production task rather than comparing headline scores.
Pricing, Licensing, and Deployment
DeepSeek V4-Flash-Vision-Exp is API-only with pricing identical to V4-Flash: images convert to tokens (max 384 per image), and text rates are roughly ¥1.5–3 per million input tokens (off-peak/peak) and ¥4.5–9 per million output, with cached input far cheaper. A free Files API avoids repeated uploads. This is aggressively low-cost for image understanding at scale.
LongCat-Flash-Omni has no per-image fee: it is MIT-licensed with open weights on GitHub and HuggingFace, self-hostable, and deployed in the LongCat App. Its open weights are unconditional, while DeepSeek's Vision-Exp release is currently API-only — though DeepSeek's track record (MIT-licensed V4-Flash weights, open-source Janus multimodal models) suggests an open-weight release is plausible but has not been confirmed.
This comparison is based on publicly available documentation accessed on 2026-08-22. LongCat-Flash-Omni specifications are from its technical report and GitHub repository. DeepSeek V4-Flash- Vision-Exp specifications are from DeepSeek's launch announcements and attributed third-party coverage.
This is a feature-level comparison, not a performance evaluation. This site has not independently tested either model. Benchmark figures are publisher-reported or independently attributed as noted; the two models' scores come from different suites and are not directly comparable.
Pricing, availability, and licensing are current as of the access date and may change. The DeepSeek V4-Flash-Vision-Exp is an experimental model; its open-weight status is unconfirmed. Verify current terms on each vendor's official documentation before deployment.
Related pages
- LongCat-Flash-Omni model profile
Full technical brief covering architecture, training, benchmarks, and deployment options.
- LongCat-2.0 model profile
The flagship text model that shares the LongCat product family.
- LongCat-2.0 vs DeepSeek V4-Flash comparison
The text-focused comparison against DeepSeek's V4-Flash, the base of the Vision-Exp model.
- LongCat-AudioDit model profile
The audio generation model in the LongCat multimodal family.
Related comparisons in this series
- LongCat-2.0 and DeepSeek V4-Flash: Open-Weight MoE Models Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, ~48B active, MIT License, $0.30/M input) and DeepSeek V4-Flash (284B MoE, 13B active, MIT License, $0.14/M input). Covers architecture, pricing, cache economics, deployment, and licensing.
- LongCat-2.0 and Qwen3.8-Max: Two Chinese Open-Weight MoE Flagships Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, 1M context, domestic-ASIC training) and Qwen3.8-Max (2.4T MoE, 95B active, custom license with revenue-share clause, open weights released August 12). Covers architecture, licensing, benchmarks, pricing, and deployment — the first LongCat vs Qwen comparison on this site.
- LongCat-2.0 and Kimi K3: Open-Weight MoE Flagships Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, domestic-ASIC training, $0.30/M input) and Kimi K3 (2.8T MoE, modified MIT License, native vision, $3.00/M input) — the two largest open-weight Chinese models. Covers architecture, licensing, pricing, modalities, and deployment.
Sources
- LongCat-Flash-Omni Technical Report (arXiv:2511.00279)
Primary sourcePublished 2025-11-03Accessed 2026-08-22
Technical report describing the omni-modal architecture, curriculum-inspired progressive training, and benchmark results across omni-modal, vision, video, and audio tasks.
- LongCat-Flash-Omni GitHub Repository
Primary sourceAccessed 2026-08-22
Official repository under MIT License with model weights, evaluation results, and deployment instructions.
- DeepSeek V4-Flash-Vision-Exp launch (The Paper / DeepSeek API)
Publisher documentationPublished 2026-08-21Accessed 2026-08-22
Launch coverage: model='deepseek-v4-flash-vision-exp', text capability on par with V4-Flash, multimodal agent capability approaching Opus 4.8, image billed at max 384 tokens each, pricing identical to V4-Flash.
- DeepSeek V4-Flash-Vision-Exp pricing and specs (Sina)
Third-partyPublished 2026-08-21Accessed 2026-08-22
Specification details: JPEG/PNG/GIF/WebP, detail modes (low/original/auto), up to 600 images per request, 8192px long edge, peak/off-peak pricing, 2500 concurrent limit, free Files API.
- DeepSeek V4-Flash-Vision-Exp benchmarks (AlphaPilot analysis)
Third-partyPublished 2026-08-21Accessed 2026-08-22
Independent analysis: Terminal-Bench 2.1 83.9, NL2Repo 57.7, DeepSWE 59.3, ApexBench 36.5 (up from 26.2), ZeroBench 35.0; notes open-weight release is not yet confirmed but aligns with DeepSeek's MIT strategy.
Independent third-party disclosure
This page is published by an independent third-party site. It is not affiliated with, endorsed by, sponsored by, or operated by Meituan, LongCat, or any of their affiliates. The content summarizes publicly-available primary documentation and does not represent the views of any referenced organization.
Last reviewed: 2026-08-22