SWE-2 vs Wan 2.6: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of SWE-2 and Wan 2.6 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

SWE-2

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.

Wan AI

Freemium

Wan 2.6 is Wan's multimodal video-generation model for text-to-video, image-to-video, text-to-image, image-to-image, and image editing.

Text-to-Video Generation: Converts natural-language prompts into multi-frame video outputs, enabling rapid creation of animated concept sequences from text descriptions.
Image-to-Video Conversion: Transforms a static image into temporally coherent video content, facilitating motion design and animation starting from existing visuals.
Text-to-Image and Image-to-Image: Produces new images from textual prompts and refines or reimagines existing images while preserving stylistic or semantic constraints.
Image Editing Tools: Provides in-model editing capabilities to modify, retouch, or augment images based on textual or visual instructions within the platform.
Multimodal Input Support: Accepts both text and image inputs to guide generation, allowing combined prompt-and-reference workflows for greater control.
Creative Workflow Integration: Designed as part of the Wan platform to streamline creative iteration, enabling creators to prototype and refine ideas quickly using unified tools.
Text-to-image synthesis from natural-language prompts
Image-to-image transformation and style transfer
Text-to-video generation for short-form video creation
Image-to-video conversion and animation of stills
Image editing tools (retouching, compositing, iterative edits)
Web-based platform for onboarding and content export
Workflow tools for rapid prototyping and iterative refinement

Marketing Video Production: Generate short promotional videos from campaign briefs to accelerate social media and ad content creation without full production pipelines.
Concept Storyboarding: Create animated storyboards from text descriptions to visualize scenes and motion for pre-production and client presentations.
Visual Asset Variant Creation: Produce multiple stylistic or compositional variants of an image for A/B testing, campaign variations, or iterative design.
Rapid Prototyping for Creatives: Quickly prototype visual concepts and motion ideas from prompts to explore creative directions before committing to full production.
Content Repurposing: Transform existing images into animated formats suitable for reels, ads, or dynamic website assets to increase content lifespan.
Image Repair and Enhancement: Use image-editing capabilities to retouch, adjust, or modify assets guided by textual instructions to meet project requirements.
Generating concept art and visual assets from text prompts for design and game development
Creating short promotional videos and social media content from prompts or images
Transforming and stylizing existing images for marketing and advertising
Rapid prototyping of visual ideas and storyboarding for video production
Image retouching and automated editing to accelerate creative workflows