Veo vs Luma Ray
Veo is stronger for Google-native cinematic generation; Luma Ray is stronger when multi-keyframe control, HDR/EXR and Ray API workflow matter.
快速建议
Most ordinary users should start with Luma Ray when they want the safer overall pick from the current evidence. Pick Veo instead when its winning scenarios below match your workflow better.
选择 Veo 的情况
- subject consistency原因: Veo has stronger subject consistency evidence from product features, workflow tags, or access data.证据: deepmind.google, lumalabs.ai, lumalabs.ai
- camera control原因: Veo has stronger camera control evidence from product features, workflow tags, or access data.证据: deepmind.google, lumalabs.ai, lumalabs.ai
- native audio原因: Veo has stronger native audio evidence from product features, workflow tags, or access data.证据: ai.google.dev, lumalabs.ai, lumalabs.ai
- company background原因: Veo has stronger company-background evidence in the local company profiles and milestone/source coverage.证据: deepmind.google, lumalabs.ai, deepmind.google
选择 Luma Ray 的情况
- feature depth原因: Luma Ray has broader stored workflow and feature coverage in the product profiles.证据: deepmind.google, blog.google, ai.google.dev
- version maturity原因: Luma Ray has the newer tracked version event in the local version dataset.证据: docs.dev.runwayml.com, lumalabs.ai
购买前仍需确认的证据
Veo 概览
Google DeepMind's flagship video generation family for cinematic clips, image-referenced video, vertical output, native audio, and API workflows.
公司: Google DeepMind
模型 page: Veo details
当前分数： 86
适合场景
Luma Ray 概览
Luma's cinematic video model family focused on creative control, keyframes, HDR/EXR output, and professional production workflows.
公司: Luma AI
模型 page: Luma Ray details
当前分数： 85
适合场景
优势总结
Every dimension below names an advantage, a reason, evidence status, and the user type it matters for. 证据 pending means the page needs live pricing, leaderboard, benchmark, or product-term verification before naming a winner.
Veo 的优势
- subject consistency原因: Veo has stronger subject consistency evidence from product features, workflow tags, or access data.证据: deepmind.google, lumalabs.ai, lumalabs.aiBest for: Character, product, and object continuity work
- camera control原因: Veo has stronger camera control evidence from product features, workflow tags, or access data.证据: deepmind.google, lumalabs.ai, lumalabs.aiBest for: Directors who need repeatable camera language
- native audio原因: Veo has stronger native audio evidence from product features, workflow tags, or access data.证据: ai.google.dev, lumalabs.ai, lumalabs.aiBest for: Dialogue, sound effects, music, and audio-video sync
- company background原因: Veo has stronger company-background evidence in the local company profiles and milestone/source coverage.证据: deepmind.google, lumalabs.ai, deepmind.googleBest for: Users who care about product longevity, distribution, and support risk
Luma Ray 的优势
- feature depth原因: Luma Ray has broader stored workflow and feature coverage in the product profiles.证据: deepmind.google, blog.google, ai.google.devBest for: Creators choosing by workflow breadth instead / one demo
- version maturity原因: Luma Ray has the newer tracked version event in the local version dataset.证据: docs.dev.runwayml.com, lumalabs.aiBest for: Teams choosing stable current products
持平或视情况而定
- price / cost原因: Both products have usable pricing/access evidence, but real cost still depends on duration, resolution, region, and plan.证据: gemini.google.com, labs.google, ai.google.devBest for: Budget-sensitive teams and API buyers
- access / availability原因: Both products have comparable access evidence in the local dataset; the real winner depends on country, account status, API route, and plan limits.证据: gemini.google.com, labs.google, workspace.google.comBest for: Ordinary users who need the product to work today
- generation quality原因: Fallback scores are close, so current use case and benchmark evidence matter more than the raw score.证据 pending: needs live pricing, benchmark, or product documentation source.Best for: Teams optimizing final visual quality
No public source link is attached yet.
- speed / delivery原因: Speed evidence pending; measure current queue time and generation latency before naming a winner.证据 pending: needs live pricing, benchmark, or product documentation source.Best for: Users who need faster iteration or production throughput
No public source link is attached yet.
- motion realism原因: Benchmark evidence pending; use current leaderboard or controlled tests.证据 pending: needs live pricing, benchmark, or product documentation source.Best for: Ads, action shots, camera moves, and physical scenes
No public source link is attached yet.
- prompt adherence原因: Prompt adherence needs benchmark evidence or side-by-side testing.证据 pending: needs live pricing, benchmark, or product documentation source.Best for: Prompt-heavy storyboards and precise scene requests
No public source link is attached yet.
- reference control原因: Both products have comparable reference control evidence or the difference depends on workflow depth.证据: deepmind.google, ai.google.dev, lumalabs.aiBest for: Brand/product references and character consistency
- editing / extension原因: Both products have comparable editing or extension workflow evidence or the difference depends on workflow depth.证据: deepmind.google, lumalabs.ai, lumalabs.aiBest for: Iterative editors and production teams
Advantage By 维度
This is the decision layer: not just yes/no support, but who leads, why they lead, what evidence backs it, and who should care.
| 维度 | 优势 / 胜出方 | 原因 | 证据 | 适合用途 |
|---|---|---|---|---|
| price / cost | 持平或视情况而定 | 原因: Both products have usable pricing/access evidence, but real cost still depends on duration, resolution, region, and plan. | 证据: gemini.google.com, labs.google, ai.google.dev | Budget-sensitive teams and API buyers |
| access / availability | 持平或视情况而定 | 原因: Both products have comparable access evidence in the local dataset; the real winner depends on country, account status, API route, and plan limits. | 证据: gemini.google.com, labs.google, workspace.google.com | Ordinary users who need the product to work today |
| feature depth | Luma Ray advantage | 原因: Luma Ray has broader stored workflow and feature coverage in the product profiles. | 证据: deepmind.google, blog.google, ai.google.dev | Creators choosing by workflow breadth instead / one demo |
| generation quality | 持平或视情况而定 | 原因: Fallback scores are close, so current use case and benchmark evidence matter more than the raw score. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Teams optimizing final visual quality |
| speed / delivery | 证据 pending | 原因: Speed evidence pending; measure current queue time and generation latency before naming a winner. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Users who need faster iteration or production throughput |
| motion realism | 证据 pending | 原因: Benchmark evidence pending; use current leaderboard or controlled tests. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Ads, action shots, camera moves, and physical scenes |
| prompt adherence | 证据 pending | 原因: Prompt adherence needs benchmark evidence or side-by-side testing. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Prompt-heavy storyboards and precise scene requests |
| subject consistency | Veo advantage | 原因: Veo has stronger subject consistency evidence from product features, workflow tags, or access data. | 证据: deepmind.google, lumalabs.ai, lumalabs.ai | Character, product, and object continuity work |
| camera control | Veo advantage | 原因: Veo has stronger camera control evidence from product features, workflow tags, or access data. | 证据: deepmind.google, lumalabs.ai, lumalabs.ai | Directors who need repeatable camera language |
| reference control | 持平或视情况而定 | 原因: Both products have comparable reference control evidence or the difference depends on workflow depth. | 证据: deepmind.google, ai.google.dev, lumalabs.ai | Brand/product references and character consistency |
| native audio | Veo advantage | 原因: Veo has stronger native audio evidence from product features, workflow tags, or access data. | 证据: ai.google.dev, lumalabs.ai, lumalabs.ai | Dialogue, sound effects, music, and audio-video sync |
| editing / extension | 持平或视情况而定 | 原因: Both products have comparable editing or extension workflow evidence or the difference depends on workflow depth. | 证据: deepmind.google, lumalabs.ai, lumalabs.ai | Iterative editors and production teams |
| API / developer availability | 持平或视情况而定 | 原因: Both products have comparable API availability evidence or the difference depends on workflow depth. | 证据: ai.google.dev, lumalabs.ai, lumalabs.ai | 开发者s, automation builders, and product teams |
| commercial use | 证据 pending | 原因: Commercial use depends on current product terms, regional plans, and license language; verify live terms before recommending a winner. | 证据: deepmind.google, lumalabs.ai, lumalabs.ai | Brands, agencies, and revenue-generating projects |
| open / closed source | 持平或视情况而定 | 原因: Both products have similar open/closed-source posture in the current dataset (Veo: closed, Luma Ray: closed). | 证据: deepmind.google, lumalabs.ai, lumalabs.ai | Researchers, self-hosters, and teams needing model control |
| version maturity | Luma Ray advantage | 原因: Luma Ray has the newer tracked version event in the local version dataset. | 证据: docs.dev.runwayml.com, lumalabs.ai | Teams choosing stable current products |
| external ratings / leaderboard | 持平或视情况而定 | 原因: Fallback scores are close, so current use case and benchmark evidence matter more than the raw score. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Buyers who want market validation |
| SEO / popularity / web authority | 证据 pending | 原因: SEO, popularity, and authority metrics need richer imports before naming a winner. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Teams using adoption and discoverability as risk signals |
| open-source GitHub metrics | 证据 pending | 原因: GitHub metrics only apply when a model has open-source or open-weight repositories; neither side has enough repo evidence here. | 证据 pending: needs live pricing, benchmark, or product documentation source. No public source link is attached yet. |
Open-model evaluators and infra teams |
| company background | Veo advantage | 原因: Veo has stronger company-background evidence in the local company profiles and milestone/source coverage. | 证据: deepmind.google, lumalabs.ai, deepmind.google | Users who care about product longevity, distribution, and support risk |
Feature 维度 Comparison
Supported values are source-backed where product detail records include source URLs; inferred values are marked.
| 维度 | Veo | Luma Ray |
|---|---|---|
| text-to-video | Supported | Supported |
| image-to-video | Supported | Supported |
| video-to-video | Supported Veo 3.1 supports video input for extension in Gemini API docs. | Supported |
| reference control | Supported | Supported |
| native audio | Supported | Watchlist |
| lip sync | Partial Native audio is confirmed; standalone lip-sync controls need feature-level confirmation. | Watchlist |
| camera control | Supported | Watchlist |
| editing/extension | Partial | Partial |
| API | Supported | Supported |
| commercial license | Provisional Commercial use depends on current hosted product terms. No public source link is attached yet. | Provisional Commercial use depends on current hosted product terms. No public source link is attached yet. |
| open/closed source | [Object Object] Veo weights are not open. | [Object Object] No official open weights are listed in the current profile. |
Veo 价格 / 入口
Gemini API pricing lists Veo 3 at USD 0.40 per second, Veo 3 Fast at USD 0.15 per second, and Veo 2 at USD 0.35 per second; check page for Veo 3.1 pricing before display.
If exact public pricing is not listed here, treat it as provisional and check the linked source before publishing a cost claim.
访问渠道
价格事实
- Veo 3 APIUSD 0.40 per second on paid tier
- Veo 3 Fast APIUSD 0.15 per second on paid tier
Luma Ray 价格 / 入口
Luma offers consumer subscriptions and Ray3.2 API access. Exact API cost should be checked from current Luma docs or dashboard.
If exact public pricing is not listed here, treat it as provisional and check the linked source before publishing a cost claim.
访问渠道
价格事实
- Pricing verificationlive check required
Performance / Quality 证据
Rows say source-backed when comparison JSON provides a cited claim; otherwise they are provisional.
| Quality 维度 | Veo | Luma Ray |
|---|---|---|
| motion realism | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. |
| prompt adherence | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. |
| temporal consistency | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. |
| subject consistency | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. |
| speed | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. |
| resolution/duration/public benchmark evidence | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. | provisional Public benchmark evidence pending; use current benchmark/ranking sources before making a hard quality claim. No public source link is attached yet. |
版本进展
Latest and historical versions are pulled from product profiles plus version-events JSON.
Veo versions
- Veo negative prompt supportRunway API adds optional negativePrompt for veo3, veo3.1, and veo3.1_fast text-to-video and image-to-video requests.
- Veo model cardsOfficial DeepMind model-card index for Veo safety/version updates.
- Veo 3.1 Lite model cardGoogle DeepMind published the Veo 3.1 Lite model card for the text/image-to-video system with audio.
- Veo 3.1 in Google Vids free monthly accessGoogle announced that personal Google accounts get monthly no-cost Veo 3.1 video generations in Google Vids, with higher limits for Google AI Pro/Ultra and Workspace AI Ultra accounts.
- Veo 3.1Improves Ingredients to Video. Adds native vertical outputs. Adds 1080p and 4K upscaling options.
- Veo 3.1 Preview / Fast PreviewGemini API model codes include veo-3.1-generate-preview and veo-3.1-fast-generate-preview. Supports text, image, last-frame, reference-image, and video-extension inputs depending on model.
- Veo 3 PreviewGemini API changelog lists video with audio generation for Veo 3 preview.
- VeoVeo announced at Google I/O 2024 as Google DeepMind's generative video model.
Luma Ray versions
- Luma Ray current model infoOfficial structured source for current Luma model naming and Ray version.
- Luma Agents ray model docsOfficial Luma Agents API model capability and pricing surface.
- Ray3.2API release. Frame-level control. Up to 20-second 1080p generations. HDR and EXR support.
- Ray3.14Intermediate Ray3 update listed in Luma news and partner model catalogues.
- Ray3Major cinematic-generation release and foundation for later Ray3 updates.
- Ray2Luma's post-Dream-Machine video model line for improved realism and motion.
- Dream MachinePublic consumer product for AI video generation.
外部评分
Fallback scores, SEO/web authority signals, GitHub status, and preset editorial winners are shown with caveats.
| Signal | Veo | Luma Ray |
|---|---|---|
| Fallback score | 86 Google Gemini Omni Flash/Veo-family entries lead the Artificial Analysis audio-video snapshot and rank highly in Arena/Vercel snapshots.; professional score is provisional/evidence pending unless backed by scoring JSON. | 85 Arena snapshot places Luma Ray 3 in the top-ten I2V region; official Ray docs support high-control workflow evidence.; professional score is provisional/evidence pending unless backed by scoring JSON. |
| SEO / web authority | Reachability collected; third-party authority metrics pending. | Reachability collected; third-party authority metrics pending. |
| GitHub / open source | Veo weights are not open. | No official open weights are listed in the current profile. |
| Comparison preset | Google model family; Ingredients/reference control; Native audio direction Access varies by Google product | Ray3.2 API; Multi-keyframe direction; HDR/EXR output Feature availability can differ between Dream Machine and API |
到底该选哪个？
- Google ecosystem: VeoBest for users already inside Gemini/Flow/API workflows.
- Frame-level control: Luma RayRay3.2 API is positioned around complete creative control.
来源
本页面使用的官方来源、新闻/版本来源、外部排名或权威来源，以及开源仓库链接。