Google Whisk vs SWE-2: Features, Pricing & Which Is Better (2026)

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

Google Whisk

Google

Free

Experimental web tool that uses images as prompts to visualize ideas and craft visual stories.

Image-as-Prompt Input: Accepts user-provided images as the primary input to seed visualizations and guide output generation, enabling idea exploration from existing visuals.
Visual Storytelling Focus: Provides tools and workflows geared toward arranging and refining visual elements into a coherent narrative or presentation to communicate ideas.
Rapid Prototyping Experience: Positioned as a Labs experiment, Whisk emphasizes quick iteration and exploratory workflows that let users test concepts without heavy setup.
Web-Based Accessibility: Delivered as a browser-accessible Labs tool so users can try image-prompt workflows without installing software or configuring environments.
Refinement & Iteration: Supports iterative editing of prompts and visual outputs so creators can progressively refine visuals and story structure (experimental capabilities may vary).
Use images as prompts to drive visual outputs
Visualize ideas and concepts from image-based inputs
Support for narrative/storytelling workflows using images
Web-based UI hosted under Google Labs (labs.google/fx)
Experimental preview — intended for exploration and feedback

Concept Visualization: Turn a photo, sketch, or mood image into a set of visual explorations to communicate product, design, or branding concepts during early-stage ideation.
Storyboarding & Narratives: Use images as seeds to assemble visual storyboards or sequences that illustrate a narrative arc for presentations, pitches, or creative projects.
Marketing & Content Creation: Rapidly prototype visual assets and scene ideas from reference images to inform campaign creatives or social media content planning.
Creative Prototyping: Experiment with different visual directions by iterating on image prompts and generated outputs to evaluate style, composition, and mood.
Educational Visual Aids: Create illustrative visual sequences or concept visuals from real-world images to support lectures, lessons, or explanatory content.
Rapidly prototype visual concepts from reference images
Create narrative or storyboards guided by image prompts
Generate visual assets for presentations or social media
Explore multimodal creative workflows and ideation

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.