FLUX.1 Pro vs Stable Diffusion 3.5
| Signal | FLUX.1 Pro | Delta | Stable Diffusion 3.5 |
|---|---|---|---|
Capabilities | 14 | -- | |
Pricing | 5 | -- | |
Context window size | 0 | -- | |
Recency | 0 | -10 | |
Output Capacity | 20 | -- | |
| Overall Result | 0 wins | of 5 | 1 wins |
Score History
8
current score
Stable Diffusion 3.5
right now
10.5
current score
FLUX.1 Pro
Black Forest Labs
Stable Diffusion 3.5
Stability AI
| Metric | FLUX.1 Pro | Stable Diffusion 3.5 | Winner |
|---|---|---|---|
| Overall Score | 8 | 11 | Stable Diffusion 3.5 |
| Rank | #13 | #10 | Stable Diffusion 3.5 |
| Quality Rank | #13 | #10 | Stable Diffusion 3.5 |
| Adoption Rank | #13 | #10 | Stable Diffusion 3.5 |
| Parameters | -- | -- | -- |
| Context Window | -- | -- | -- |
| Pricing | Free | Free | -- |
| Signal Scores | |||
| Capabilities | 14 | 14 | FLUX.1 Pro |
| Pricing | 5 | 5 | FLUX.1 Pro |
| Context window size | 0 | 0 | FLUX.1 Pro |
| Recency | 0 | 10 | Stable Diffusion 3.5 |
| Output Capacity | 20 | 20 | FLUX.1 Pro |
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 8/100 (rank #13), placing it in the top 96% of all 290 models tracked.
Scores 11/100 (rank #10), placing it in the top 97% of all 290 models tracked.
With only a 3-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Choose FLUX.1 Pro when you need:
- Budget-friendly applications with moderate quality requirements
Choose Stable Diffusion 3.5 when you need:
- Self-hosted deployments where you need full control over the model
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. FLUX.1 Pro 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 (11/100) correlates with better nuance, coherence, and style in long-form content
FLUX.1 Pro and Stable Diffusion 3.5 are extremely close in overall performance (only 2.5 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
By Use Case
Best for Quality
FLUX.1 Pro
Marginally better benchmark scores; both are excellent
Best for Cost
FLUX.1 Pro
0% lower pricing; better value at scale
Best for Reliability
FLUX.1 Pro
Higher uptime and faster response speeds
Best for Prototyping
FLUX.1 Pro
Stronger community support and better developer experience
Best for Production
FLUX.1 Pro
Wider enterprise adoption and proven at scale
by Black Forest Labs
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 0% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | FLUX.1 Pro | Stable Diffusion 3.5 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
FLUX.1 Pro
Black Forest Labs
Stable Diffusion 3.5
Stability AI
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | FLUX.1 Pro | Stable Diffusion 3.5 |
|---|---|---|
| Context Window | -- | -- |
| Max Output Tokens | -- | -- |
| Open Source | No | Yes |
| Created | Aug 1, 2024 | Oct 22, 2024 |
The ranking gap reflects how tightly clustered image generation models are in the 13-17 score range, where small performance differences create large rank swings. Stable Diffusion 3.5's open-source nature likely contributes to its higher placement at #6, as it offers 30% lower output costs ($35,000/M vs $50,000/M) while maintaining competitive quality metrics.
At $50,000/M output versus $35,000/M, FLUX.1 Pro's pricing premium doesn't correspond to superior performance - it actually scores 4 points lower (13 vs 17). For high-volume commercial deployments generating millions of images monthly, choosing FLUX.1 Pro would cost an extra $15,000 per million outputs with no measurable quality advantage according to benchmark data.
Stable Diffusion 3.5's open-source status enables on-premise deployment and model fine-tuning, critical for enterprises handling sensitive visual content or requiring custom style adaptation. This flexibility, combined with 30% lower API costs and a higher performance score (17 vs 13), makes it particularly attractive for organizations prioritizing data sovereignty and customization over Black Forest Labs' managed service approach.
Text-to-image models don't operate on token-based context windows like LLMs - they process prompt text into embeddings and generate pixel outputs rather than sequential tokens. The 0 values reflect how these models consume minimal text input (typically under 77 tokens for clip encoding) and produce image files rather than tokenized text, making traditional token metrics irrelevant for comparing their $35,000-$50,000/M output pricing.
Despite ranking #6 and #13 with scores of 17 and 13 respectively, both models command premium pricing ($35,000-$50,000/M outputs) that suggests the image generation market values API reliability and infrastructure over pure quality metrics. The 14-model category appears highly competitive, where even lower-ranked models maintain enterprise-grade pricing due to computational demands of high-resolution image synthesis rather than their benchmark performance.