Ami vs SIMA 2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Ami and SIMA 2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Ami
AiSDR
AI GTM agent that picks the audience, writes and launches outbound campaigns, reads the results and fixes what stops working.
Key features
- Autonomous Campaign Loop: Ami picks the target audience, builds and launches the campaign, reads what comes back and changes what is not working, so each campaign sharpens the next without a human restarting the cycle.
- Baked-In GTM Experience: Arrives with 27 industry playbooks and the lessons of 17,150 prior AiSDR campaigns and 19,501 meetings, so a first campaign launches with patterns other teams paid to learn.
- Signal-Triggered Outreach: Watches hiring, funding and job-change signals and acts at the moment they happen rather than months later.
- Performance Triage: When response rates slip, Ami digs into audience, message and sequence to pinpoint what is breaking and proposes fixes before the budget is spent — flagging, for example, a positive response rate under 1% after 21+ days.
- Omnichannel Sequences: Configurable sequences combining email via Gmail or Outlook, LinkedIn connection requests, DMs and InMail, and AI call steps through the Aircall dialer with scripts and automated follow-ups.
- Deep Per-Lead Personalization: Researches the top three most relevant data points per lead and personalizes from ICP data, activity, LinkedIn data and HubSpot properties.
- Native CRM Sync: Two-way HubSpot sync on every plan and two-way Salesforce sync on higher tiers, with AI research and monitoring running over that CRM data.
- Review Mode: Campaigns and Ami's proposed corrections stay drafts until approved, so the agent's autonomy is opt-in rather than assumed.
Best for
- Founder-Led Outbound: A solo founder builds pipeline without hiring an SDR, starting self-serve with no sales call required.
- Rescuing Stalled Campaigns: A revenue team catches a dying sequence early when Ami flags a collapsing positive-response rate and rewrites the audience or message.
- Replacing Outbound Agencies: A company that has paid outside firms without results brings the motion in-house under one agent.
- Warm-Signal Prospecting: A sales team reaches buyers right after a funding round, a relevant hire or a job change instead of cold-listing an industry.
- CRM-Grounded Targeting: A HubSpot or Salesforce team has outreach built from and logged back into existing CRM data rather than a disconnected tool.
- Multichannel Follow-Up: A team runs email, LinkedIn and dialer touches in a single sequence with replies handled in 5-10 minutes or in co-pilot mode.
SIMA 2
A Gemini-powered multimodal agent that plays, reasons, and learns in rich 3D virtual worlds, following instructions and adapting to new games.
Key features
- Gemini Integration: Uses advanced Gemini models for higher-level reasoning, planning, and natural-language understanding to convert instructions into multi-step actions.
- Multimodal Perception and Control: Reads pixel and UI observations from 3D worlds and issues control inputs (e.g., mouse/keyboard) at interactive frame rates to operate within environments.
- Instruction Following and Dialogue: Accepts natural-language commands and holds conversational exchanges to clarify goals, report progress, and receive guidance from human users.
- Goal-Directed Planning: Explicitly represents and reasons about goals, formulates subgoals, and sequences actions to achieve complex, long-horizon tasks in virtual worlds.
- Skill Generalization: Transfers learned behaviors and strategies to novel games and environments, allowing zero- or few-shot adaptation to previously unseen tasks.
- Human-in-the-Loop Learning: Incorporates demonstrations and interactive feedback from humans to refine performance and learn new capabilities during play.
- Real-Time Interaction: Operates at interactive frame-rates (observed controlling inputs at ~30+ fps in demonstrations) enabling fluid gameplay and rapid reaction to changing environments.
- Integrates Gemini models for higher-level reasoning and decision-making
- Follows natural language instructions within 3D virtual worlds
- Goal-directed planning and reasoning about objectives
- Conversational interface for user interaction and guidance
- Real-time perception and control (reads screen and controls input at ~30+ fps)
- Self-improvement via learning from interaction and environment feedback
- Generalizes to previously unseen environments and tasks
- Trained and evaluated in complex simulated games/environments (e.g., Goat Simulator 3)
Best for
- Research on generalist embodied agents: studying how language, perception, and action combine to create adaptable agents in 3D simulated worlds.
- Game testing and playtesting: automating exploration and interaction with game mechanics to find bugs, balance issues, or emergent behaviors across complex titles.
- Human-in-the-loop training: enabling developers and researchers to teach and correct agent behavior interactively via natural language and demonstrations.
- Benchmarking multimodal reasoning: evaluating agent performance on tasks requiring planning, long-horizon goal management, and perceptual understanding.
- Simulated robotics and control research: using virtual 3D environments as safe, rich testbeds for developing transferable control and decision-making skills.
- Research on embodied agents and generalization in simulated 3D environments
- Human-agent collaborative play and instruction following in virtual worlds
- Automated playtesting and exploration of open-ended video games
- Prototyping and benchmarking reasoning-capable agents in simulation
- Developing interactive virtual assistants or tutors inside simulated environments