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

A side-by-side comparison of SWE-2 and Textable — 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.

Unknown Developer

Freemium

Generates a fully-fledged retro Teletext channel from a single prompt, producing hundreds of stylized teletext pages.

Single-Prompt Channel Generation: Builds a complete Teletext channel or 'universe' starting from one user-provided prompt, automating the creation of interconnected pages.
Bulk Page Generation: Produces hundreds of Teletext-style pages in a single run to populate a full channel without manual page-by-page effort.
Retro Teletext Styling: Applies classic teletext visual characteristics—blocky text, constrained layout and palette choices—to recreate an authentic vintage look.
Thematic Consistency: Generates cohesive content and layout across pages so that the resulting channel reads and looks like a unified publication.
Rapid Prototyping for Creative Projects: Enables fast iteration and experimentation when designing nostalgia-driven media, art installations, or web mockups.
Single-prompt generation of an entire Teletext channel
Generates hundreds of Teletext-style pages per project
Retro Teletext visual styling and layout generation
Assembles pages into a coherent channel/universe
Web-based access via official site (no API details provided)
Designed for rapid large-scale content generation

Creating a full retro Teletext channel for an art project or installation that needs authentic vintage broadcast aesthetics.
Generating large volumes of stylized teletext pages for use as visual assets in web design, video sets, or promotional materials.
Prototyping themed content and layouts quickly for media experiments or interactive exhibits that reference classic teletext.
Producing cohesive nostalgia-driven publishing mockups or digital zines that require many interlinked pages with consistent styling.
Supplying retro-styled content for marketing campaigns or social media that leverage vintage visual language to attract niche audiences.
Creating nostalgic Teletext-themed digital art and galleries
Generating UI/UX mockups or assets with retro styling for games and apps
Producing themed content collections or microsites in Teletext format
Rapid prototyping of multi-page retro layouts for creative projects
Educational or demo materials demonstrating Teletext aesthetics