Short answer: it depends on your use case, and I don’t have verified information about “Seedream 4.5” or a 2026 “GPT Image 1.5.” My knowledge ends in Oct 2024, so I can’t reliably rank these 2026 versions. Here’s how to decide quickly and safely without relying on unverified claims.
What to compare
- Image quality and style range: photorealism, illustration, typography, lighting, anatomy, hands/faces.
- Prompt adherence and controllability: negative prompts, fine-grained attributes, masks, region edits, outpainting/inpainting, reference-guided generation.
- Text in images: legible, spelled correctly, layout fidelity.
- Visual understanding (if multimodal): OCR accuracy, chart/table reading, step-by-step reasoning, grounding.
- Editing workflow: iterative refinement, reversible edits, consistent characters/products across shots.
- Speed and scale: latency at batch sizes you need, throughput, rate limits, cold starts.
- Cost: per image/per token, retries, long-context or high-resolution surcharges.
- Safety and governance: content filters, bias behavior, watermarking, copyright protections, opt-out/data retention.
- Deployment: cloud vs on-prem/edge, region availability, SLAs, version pinning, seed control/reproducibility.
- Ecosystem: SDKs, integrations (design tools, 3D, ControlNet-like tools), community models, fine-tuning or LoRA support.
A quick bake-off plan (1–2 days)
- Define tasks: e.g., product hero shots, marketing banners with text, character-consistent scenes, technical diagrams, photo edits, OCR+reasoning.
- Build a small, fixed prompt suite (10–30 prompts) with expected outputs and, if possible, fixed seeds and identical negative prompts/parameters.
- Measure:
- Automatic: CLIPScore, PickScore, aesthetic predictors; OCR word accuracy on rendered text; color/pose/attribute compliance.
- Human: blind A/B(X) voting by 3–5 reviewers for fidelity, appeal, and prompt-following.
- Robustness: multilingual prompts, long prompts, typos, low-light scenes, tiny text, overlapping objects.
- Ops: average/95th percentile latency, failure rate/timeouts, cost per accepted image.
- Record reproducibility: version IDs, seeds, exact parameters, API regions.
Rules of thumb by use case
- Marketing/brand visuals with text: favor the model that reliably renders clean typography and preserves brand colors/layouts.
- Photoreal product shots and people: choose the model with fewer anatomical artifacts and better lighting/shadow coherence.
- Precise edits and consistency: prioritize strong inpainting/masking and reference-based control; test character/product consistency across 5–10 images.
- Charts, docs, OCR+reasoning: pick the model with higher OCR accuracy and fewer hallucinations in visual Q&A.
If you can share:
- Your primary tasks (generation, editing, or vision understanding)
- Target styles (photoreal, flat illustration, 3D, typographic)
- Volume/latency and budget constraints
- Deployment needs (on-prem/compliance)
I can suggest a tailored head-to-head prompt suite and scoring sheet you can run in a few hours to determine which is better for you.
GPT Image 1.5 (OpenAI, dic 2025) si distingue per una generazione 4× più veloce (5–15 secondi), punteggi LM Arena ELO di prim’ordine (~1,264–1,285) e una superiore capacità di seguire le istruzioni per l’editing. Seedream 4.5 (ByteDance, dic 2025) eccelle nella tipografia, nella risoluzione 4K, nella coerenza tra più immagini (fino a 14 riferimenti) e in un prezzo fisso di $0.04/immagine. Scegli GPT Image 1.5 per velocità e versatilità; Seedream 4.5 per lavori commerciali ad alto contenuto di design. Entrambi sono accessibili a costi contenuti tramite la piattaforma unificata di **CometAPI**, con risparmi del 20%+ e integrazione con una singola chiave.
