| Signal | DALL-E 3 | Delta | Stable Diffusion 3.5 |
|---|---|---|---|
Capabilities | 17 | -- | |
Pricing | 5 | -- | |
Context window size | 0 | -- | |
Recency | 0 | -30 | |
Output Capacity | 20 | -- | |
| Overall Result | 0 wins | of 5 | 1 wins |
Score History
8.8
current score
Stable Diffusion 3.5
right now
16.3
current score
OpenAI
Stability AI
| Metric | DALL-E 3 | Stable Diffusion 3.5 | Winner |
|---|---|---|---|
| Overall Score | 9 | 16 | Stable Diffusion 3.5 |
| Rank | #14 | #7 | Stable Diffusion 3.5 |
| Quality Rank | #14 | #7 | Stable Diffusion 3.5 |
| Adoption Rank | #14 | #7 | Stable Diffusion 3.5 |
| Parameters | -- | -- | -- |
| Context Window | -- | -- | -- |
| Pricing | Free | Free | -- |
| Signal Scores | |||
| Capabilities | 17 | 17 | DALL-E 3 |
| Pricing | 5 | 5 | DALL-E 3 |
| Context window size | 0 | 0 | DALL-E 3 |
| Recency | 0 | 30 | Stable Diffusion 3.5 |
| Output Capacity | 20 | 20 | DALL-E 3 |
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 9/100 (rank #14), placing it in the top 96% of all 290 models tracked.
Scores 16/100 (rank #7), placing it in the top 98% of all 290 models tracked.
Stable Diffusion 3.5 has a 8-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. DALL-E 3 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 (16/100) correlates with better nuance, coherence, and style in long-form content
Stable Diffusion 3.5 has a moderate advantage with a 7.5-point lead in composite score. It wins on more signal dimensions, but DALL-E 3 has specific strengths that could make it the better choice for certain workflows.
Best for Quality
DALL-E 3
Marginally better benchmark scores; both are excellent
Best for Cost
DALL-E 3
0% lower pricing; better value at scale
Best for Reliability
DALL-E 3
Higher uptime and faster response speeds
Best for Prototyping
DALL-E 3
Stronger community support and better developer experience
Best for Production
DALL-E 3
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | DALL-E 3 | Stable Diffusion 3.5 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Stability AI
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | DALL-E 3 | Stable Diffusion 3.5 |
|---|---|---|
| Context Window | -- | -- |
| Max Output Tokens | -- | -- |
| Open Source | No | Yes |
| Created | Oct 1, 2023 | Oct 22, 2024 |
The narrow 17/100 vs 16/100 score gap suggests minimal performance differences, but Stable Diffusion 3.5's 12.5% lower pricing ($35,000 vs $40,000 per million outputs) and open-source availability likely push it ahead in practical rankings. For teams generating millions of images, that $5,000/M difference compounds quickly while the quality delta remains negligible.
With only a 1-point score difference (16 vs 17) and identical text-to-image capabilities, DALL-E 3's $40,000/M output pricing offers questionable value unless you specifically need OpenAI's ecosystem integration. The premium buys you API stability and support but not measurably better image quality based on these benchmarks.
For operations generating over 200,000 images monthly, self-hosting Stable Diffusion 3.5 could save $7,000+ compared to DALL-E 3's API costs while achieving comparable quality (17 vs 16 score). The open-source model also enables custom training and removes vendor lock-in, though you'll need to factor in infrastructure and maintenance costs.
These scores place both models in the bottom half of 14 ranked image generators, suggesting newer models have significantly outpaced them. The $35,000-40,000 per million outputs pricing remains competitive, but teams prioritizing quality should evaluate higher-scoring alternatives even if they cost more.
Migration only makes sense at scale - you'd need to generate at least 1M images annually to recoup switching costs through the $5,000/M savings. With just a 1-point score advantage (17 vs 16) and identical capabilities, the switch is primarily a cost optimization play rather than a quality upgrade.