GPT Image 1.5 vs SWE-2: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of GPT Image 1.5 and SWE-2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

GPT Image 1.5

OpenAI

Freemium

Cost-efficient GPT-5 image generator for high-quality visuals, precise edits, and UI designs up to 4× faster.

GPT-5 Powered Generation: Uses GPT-5-based image synthesis capabilities to produce detailed visuals and creative outputs with improved semantic understanding.
Cost-Efficient Processing: Optimized to reduce compute costs per image, enabling larger-scale generation or lower-priced usage for teams and projects.
Fast Throughput (Up to 4×): Engineered for accelerated image creation and editing workflows, reducing iteration time for designers and content teams.
Precise Image Edits: Supports targeted edits and refinements to existing images, enabling corrections and modifications without full re-generation.
UI Design Output: Tailored to generate UI components and mockups, helping product designers rapidly prototype interfaces and visual assets.
High-Quality Visuals: Focus on producing clean, high-fidelity images suitable for marketing, product imagery, and design presentations.
Cost-efficient image generation
High-quality visual outputs
Precise image edits
UI design generation
Up to 4× faster generation throughput

Rapid UI Prototyping: Generate multiple UI mockups and component variations quickly to iterate on app and web interfaces during product design sprints.
Precise Asset Edits: Apply targeted corrections or enhancements to product photos and marketing images without recreating the entire visual.
High-Volume Visual Production: Produce large batches of marketing visuals or social media imagery while managing compute costs for startups and agencies.
Design Exploration: Create diverse concept art and visual directions for branding exercises, enabling fast A/B comparisons of styles and layouts.
Prototype-to-Presentation Workflow: Turn rough design sketches or briefs into polished visuals for stakeholder demos and pitch materials.
Cost-Conscious Creative Teams: Allow small teams to scale image generation and iteration without incurring high per-image costs.
Rapid creation of marketing visuals and illustrations
Iterative UI and product-design mockups
Precise image edits and refinements
Fast prototyping of visual assets for applications and websites

Cognition

Paid

Cognition's coding model that scores 50.0% on FrontierCode 1.1 Main at 64% lower cost than comparable frontier models.

Pareto-Frontier Cost Efficiency: Matches GPT-5.6 Sol and Fable 5/5.1 on coding benchmarks at a fraction of their price and comes within a few points of GPT-6 Astra at roughly a quarter of the cost.
Single-Run Multi-Effort RL: A reinforcement learning algorithm trains all reasoning-effort levels in one run, applying a per-level linear cost penalty derived from the base model's local frontier slope.
Focused Codebase Exploration: Stronger engineering judgment lets the model decide which parts of a repository matter, cutting mean steps per run from 127 to 53 at medium effort.
Selectable Effort Levels: Ships medium, high and max reasoning settings so teams can trade additional steps and cost for accuracy on harder tasks.
End-to-End Test Writing: Produces tests that validate an implementation end to end, catching regressions and edge cases more reliably than previous SWE models.
Resourceful Task Recovery: When an expected route is blocked — an unavailable MCP integration, for example — it finds an alternative path to the same answer within the user's stated boundaries.
Efficient Training and Serving Stack: NVFP4/FP8 kernels, quantization-aware training and an online draft model cut memory use and train-inference mismatch despite nearly 3x the base parameters of SWE-1.7.
Hardened Verifier Flywheel: Triples the number of RL environments, adds instruction-following overlays, and uses earlier SWE-2 checkpoints to iteratively strengthen verifiers.

Agentic Software Engineering: Powering Devin sessions that plan, edit, build and test changes across a real repository with minimal supervision.
Cost-Sensitive Coding at Scale: Teams running large volumes of automated coding tasks pick a model that holds frontier-adjacent accuracy at a materially lower per-task cost.
Terminal and Tooling Workflows: Strong Terminal-Bench results suit tasks driven through shell commands, build systems and command-line tooling.
Regression Test Generation: Generating end-to-end tests for existing implementations to catch edge cases before a release.
Effort-Tiered Task Routing: Routing simple tickets to medium effort and hard migrations to high or max effort within the same model deployment.
Benchmark and Model Evaluation: Engineering leaders compare coding model options on published FrontierCode, DeepSWE and Terminal-Bench numbers alongside cost.