| Signal | Stable Video Diffusion | Delta | Veo 2 |
|---|---|---|---|
Capabilities | 0 | -- | |
Pricing | 100 | +95 | |
Context window size | 0 | -- | |
Recency | 0 | -40 | |
Output Capacity | 20 | -- | |
| Overall Result | 1 wins | of 5 | 1 wins |
Score History
3
current score
Veo 2
right now
13
current score
Stability AI
| Metric | Stable Video Diffusion | Veo 2 | Winner |
|---|---|---|---|
| Overall Score | 3 | 13 | Veo 2 |
| Rank | #10 | #3 | Veo 2 |
| Quality Rank | #10 | #3 | Veo 2 |
| Adoption Rank | #10 | #3 | Veo 2 |
| Parameters | -- | -- | -- |
| Context Window | -- | -- | -- |
| Pricing | Free | Free | -- |
| Signal Scores | |||
| Capabilities | 0 | 0 | Stable Video Diffusion |
| Pricing | 100 | 5 | Stable Video Diffusion |
| Context window size | 0 | 0 | Stable Video Diffusion |
| Recency | 0 | 40 | Veo 2 |
| Output Capacity | 20 | 20 | Stable Video Diffusion |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 3/100 (rank #10), placing it in the top 97% of all 290 models tracked.
Scores 13/100 (rank #3), placing it in the top 99% of all 290 models tracked.
Veo 2 has a 10-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Stable Video Diffusion also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (0K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (13/100) correlates with better nuance, coherence, and style in long-form content
Veo 2 has a moderate advantage with a 10-point lead in composite score. It wins on more signal dimensions, but Stable Video Diffusion has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Stable Video Diffusion
Marginally better benchmark scores; both are excellent
Best for Cost
Stable Video Diffusion
0% lower pricing; better value at scale
Best for Reliability
Stable Video Diffusion
Higher uptime and faster response speeds
Best for Prototyping
Stable Video Diffusion
Stronger community support and better developer experience
Best for Production
Stable Video Diffusion
Wider enterprise adoption and proven at scale
by Stability AI
| Capability | Stable Video Diffusion | Veo 2 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Stability AI
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Stable Video Diffusion | Veo 2 |
|---|---|---|
| Context Window | -- | -- |
| Max Output Tokens | -- | -- |
| Open Source | Yes | No |
| Created | Nov 21, 2023 | Dec 16, 2024 |
Stable Video Diffusion's open-source nature (vs Veo 2's closed model) has enabled community optimization and fine-tuning that translates to a 10/100 score compared to Veo 2's 0/100. The image-to-video modality of Stable Video Diffusion also provides more consistent results than Veo 2's text-to-video approach, which struggles with temporal coherence at Google's $350,000/M output pricing tier.
Stable Video Diffusion operates on a fundamentally different business model - its open-source release means users bear their own compute costs, resulting in $0/M pricing. Veo 2's astronomical $350,000/M output cost reflects Google's need to recoup R&D investments and server infrastructure for their text-to-video pipeline, which ranks dead last (#10) among video generation models.
Despite both showing 0 tokens for context window and max output, Stable Video Diffusion's #7 ranking versus Veo 2's #10 reflects real-world performance differences in their core modalities. The image-to-video approach of Stable Video Diffusion produces more predictable results than Veo 2's text-to-video generation, which at 0/100 score suggests fundamental issues with prompt adherence and output quality.
Stable Video Diffusion would cost $0 in API fees (though you'd pay for your own GPU compute), while Veo 2 would cost $350 per 1M outputs - meaning even 1000 generations could run hundreds of dollars depending on output length. Given Veo 2's 0/100 performance score, you're essentially paying premium prices for the worst-performing model in the category.
The only technical advantage Veo 2 offers is text-to-video generation versus Stable Video Diffusion's image-to-video requirement, eliminating the need for initial frame generation. However, with a 0/100 score and $350,000/M output cost, Veo 2 appears to be an early-access preview rather than a production-ready model, making it suitable only for R&D teams specifically testing Google's approach.