SWE-2 vs TheySaid 3.0: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of SWE-2 and TheySaid 3.0 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
SWE-2
Cognition
Cognition's coding model that scores 50.0% on FrontierCode 1.1 Main at 64% lower cost than comparable frontier models.
Key features
- 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.
Best for
- 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.
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TheySaid 3.0
TheySaid
Turn single-question surveys into real-time conversational surveys to boost engagement and surface richer insights.
Key features
- Conversational Survey Conversion: Transforms single-question surveys into AI-driven multi-turn conversations that probe respondents with contextual follow-ups to gather richer qualitative data.
- Real-time Engagement: Dynamically adapts follow-up prompts based on answers to keep respondents engaged and reduce drop-off during the survey experience.
- Automated Insight Extraction: Aggregates and summarizes responses, surfaces recurring themes and sentiment, and highlights actionable findings for faster analysis.
- Intelligent Question Generation: Generates clarifying and targeted follow-up questions tailored to each respondent’s answers to obtain deeper context and reasons.
- Response Analytics Dashboard: Provides aggregated views, filters, and breakdowns (e.g., sentiment and themes) to help teams interpret results quickly.
- Export & Integration: Enables exporting response data and integrating survey outputs with downstream analytics or research workflows for further analysis.
- Conversational AI Surveys that adapt follow-up questions based on responses
- AI Interviews to automate in-depth user interviews
- AI Pulses and Polls for single-question feedback and follow-ups
- Question recommendation and auto-generation from website content
- Embedding and delivery via existing channels
- AI-driven summarization of responses and insights
- Convert single-question surveys into interactive, AI-driven conversations
- Real-time capture of conversational survey responses
- Smart conversational survey design to boost respondent engagement
- Automated analysis and extraction of insights from conversational responses
- Web-based survey creation and results dashboard
Best for
- Converting NPS/CSAT single-question prompts into conversational flows to collect reasons, suggestions, and context behind scores for better actionability.
- Market research that requires scalable qualitative feedback by turning short surveys into richer interviews to surface customer needs and motivations.
- Customer support and feedback collection that triages issues via guided conversation and captures exact user language and sentiment for product teams.
- Employee pulse and HR surveys that solicit candid explanations and suggestions while maintaining higher completion rates through conversational engagement.
- Product discovery and usability testing to gather in-depth user reactions, feature requests, and pain points from compact, conversational surveys.
- Collecting product and UX feedback via conversational surveys
- Automating user interviews to surface deeper insights
- Running NPS/CSAT/CES pulses with follow-up probing
- Embedding surveys across websites and apps to increase engagement
- Conducting user tests and polls with AI follow-ups to understand reasons
- Customer feedback collection with richer qualitative responses
- Market research using conversational probes to uncover insights
- Product feedback and user experience surveys
- NPS and satisfaction measurement with follow-up conversational context
- Employee engagement and pulse surveys