AI visibility report
AI visibility report for Cognition (Devin) in Autonomous Coding Agents.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Augment Code is cited on 6 of those losses.
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Start free trialStill absent from 99.2% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
Bing Copilot
Perplexity
Google AI ModeHow to read this. Cognition (Devin) appears in 0.8% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Cognition (Devin) is losing
Prompts where competitors are visible and Cognition (Devin) is not.
These prompt-level losses are the first prompts to track and repair.
Where Cognition (Devin) is winning1
Which agentic coding platforms integrate with project management tools so engineers can assign tickets directly to an AI agent to action?
Avg # 3.0 · 1 platform
Where Cognition (Devin) is losing5
Which AI coding agents handle context window limitations most gracefully when working across dozens of files in an enterprise codebase?
Competitors on 3 platforms
Track this promptWhat AI coding agents support bring-your-own LLM provider so a platform team can route through an existing enterprise model contract?
Competitors on 3 platforms
Track this promptWhat agentic coding tools handle long-running tasks reliably — resuming after an interruption rather than starting over from scratch?
Competitors on 2 platforms
Track this promptWhich cloud coding agents integrate with CI pipelines to automatically attempt fixes when a build or test suite fails?
Competitors on 1 platform
Track this promptWhat autonomous coding agents run tasks inside a secure sandbox so a compromised prompt can't affect the host filesystem?
Competitors on 1 platform
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Track Cognition (Devin) daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Cognition is a San Francisco-based AI company that develops Devin, marketed as the world's first autonomous AI software engineer. Founded in August 2023 by competitive programmers Scott Wu, Steven Hao, and Walden Yan, Devin operates as a cloud agent inside isolated virtual machines equipped with a shell, code editor, and browser. Unlike inline coding copilots, Devin executes entire software engineering tasks end-to-end—from interactive planning to pull request submission—and can spawn multiple parallel agent instances for large-scale projects. Core use cases include code migration and modernization, security remediation, test generation, and PR review. Enterprise customers include Goldman Sachs, Citi, Mercedes-Benz, Nubank, and the U.S. Army. In July 2025, Cognition acquired Windsurf to add an AI-native IDE to its product suite. Having raised approximately $1.7 billion across all rounds, the company reached a $26 billion post-money valuation in May 2026.
Devin is Cognition's flagship autonomous AI software engineering agent. It runs inside isolated cloud VMs with full tool access (terminal, browser, VSCode-style editor) and handles entire development tasks end-to-end—accepting natural language tickets from Slack, Linear, or Jira and delivering reviewed, merged pull requests. The product suite includes Devin Cloud (async cloud agent), Devin Desktop (AI-native IDE, rebranded from Windsurf), Devin CLI, Devin Review (AI PR review), and Devin Windows VM. Cognition also trains its own SWE-series models (SWE-1.5, SWE-1.6) optimized for software engineering tasks and served at up to 950 tokens/second via Cerebras.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA, USA
- Founders
- Scott Wu, Steven Hao, Walden Yan
- Funding
- ~$1.7B
- ARR
- ~$492M
- Valuation
- $26B
- Status
- Private
Target users
Key Capabilities10
- Autonomous end-to-end task execution in isolated cloud VMs (shell, browser, code editor)
- Parallel multi-agent spawning for large-scale migrations and concurrent workstreams
- Interactive task planning with human review before execution
- GitHub/GitLab PR creation, review, and iterative CI feedback loop
- Devin Review: AI-powered PR review with intelligent diff organization and bug detection
- Codebase indexing, natural-language search (Devin Search), and auto-generated wiki
- Fine-tuning on customer codebases for domain-specific task improvement
- Scheduled and recurring autonomous sessions with persistent state
- Windows VM and Android emulator support for native platform development
- MCP marketplace for extensible third-party tool integrations
Key Use Cases8
- Large-scale code migrations (COBOL, .NET Framework, legacy ETL, Angular-to-React)
- Automated security vulnerability remediation (SonarQube, Veracode findings)
- Test coverage generation across large multi-repo codebases
- Automated PR review and visual QA with bug flagging
- Bug triage and incident response via Datadog/Sentry/Slack integration
- Documentation generation and architecture diagrams for legacy systems
- Scheduled CI maintenance, dependency upgrades, and release notes
- Ticket-to-PR automation from Linear or Jira backlog
Cognition (Devin) customer outcomes
8–12x engineering time efficiency gain; >20x cost savings on migrated scope
Deployed a fleet of Devin agents to migrate a multi-million-line monolithic ETL to sub-modules, replacing what had been planned as an 18-month, 1,000-engineer effort. Cognition reports the project was completed in weeks across multiple business units.
8-month project completed in 8 days
Partnered with Cognition to deploy Devin for legacy modernization across its global engineering organization. A project previously estimated at eight months was completed in eight days.
70% of security vulnerabilities fixed automatically
Latin America's largest bank deployed Devin for automated security vulnerability remediation across its codebase, achieving a high rate of autonomous fixes without human intervention.
3–4 hours per file migration vs. 30–40 hours for human engineers (10x improvement)
Used Devin to migrate hundreds of thousands of proprietary ETL framework files as part of a Java version modernization, with Devin completing each file migration significantly faster than human engineers.
Deployed Devin as an on-demand data analyst to handle the flood of ad hoc analytics requests, replacing multi-day turnaround times with near-instant answers via a trained autonomous agent.
Recent Trend
How AI describes Cognition (Devin)
No concise AI response excerpt is available for this brand yet.
Most cited sources1
Alternatives in Autonomous Coding Agents6
Devin positions itself as the world's first fully autonomous AI software engineer—an async cloud agent that owns tasks end-to-end (planning through merged PR) rather than an inline copilot.
- Its differentiation rests on cloud-sandboxed parallel agent spawning, codebase fine-tuning, and deep integration into enterprise workflows (Slack, Linear, Jira) rather than IDE-embedded autocomplete.
- Following the July 2025 acquisition of Windsurf, Cognition now offers both the autonomous cloud agent (Devin) and an AI-native IDE (Devin Desktop), positioning the company as the only vendor with a complete 'agent + IDE' product suite at scale.
- Enterprise targeting—large migrations, legacy modernization, security remediation—further separates Devin from consumer-focused coding tools.
- AAugment Code#19
- AAnthropic (Claude Code)#23
- Block (Goose)#33
- OpenAI (Codex CLI / Codex)#43
- CCursor (Anysphere)#62
- Factory (Droid)#52
Reviews
Praised
- End-to-end task execution from ticket to merged PR
- Parallel agent spawning for large migrations and batch tasks
- Deep Slack/Linear/Jira workflow integration
- Persistent codebase context and session memory
- Highly effective for repetitive tasks (migrations, security fixes, test generation)
- Interactive planning phase aligns execution before compute is spent
- Self-debugging and automatic CI failure recovery
- Non-technical team members can ship code with guidance
Criticized
- Opaque ACU billing; complex tasks can far exceed base plan cost
- Low success rate on complex or ambiguous open-ended tasks
- Requires very precise instructions; vague prompts frequently fail
- Lack of transparency in decision-making and failure explanations
- Silent task failures without surfacing uncertainty to user
- Not suited for open-ended architectural design or senior-level judgment
- Early-version reputation for promising more than it delivered
User sentiment is mixed and heavily context-dependent. Enterprise teams running well-scoped migration and remediation tasks report significant productivity gains—Nubank achieved 8–12x efficiency improvement and 20x cost savings on an ETL migration; a large bank saw 10x speed improvement on file migrations vs. human engineers. Individual developers find Devin strong for automation, web scraping, and repetitive bug fixes, but report inconsistent results on complex or ambiguous projects, with some independent tests recording ~15% task completion on hard problems. Recurring criticisms include opaque ACU billing that can far exceed the base plan cost, silent task failures without clear explanations, and the need for very precise prompting. A third-party site citing Trustpilot reported a 3.0/5 score as of March 2026, though the sample is extremely small.
Pricing
Devin offers five tiers as of June 2026. Free ($0): limited agent quota, restricted models, unlimited inline edits and Tab completions. Pro ($20/month): increased quotas, full model access (OpenAI, Claude, Gemini, SWE-1.6), Devin Cloud access, extra usage at API pricing. Max ($200/month): significantly higher quotas than Pro. Teams ($80/month base + $40/month per full developer seat): unlimited members, shared collaboration, admin dashboard, priority support. Enterprise (custom): adds VPC deployment, SAML/OIDC SSO, dedicated account team, and centralized enterprise admin controls. Usage is consumption-based via Agent Compute Units (ACUs); one ACU represents approximately 15 minutes of active autonomous work and was previously priced at $2.25/ACU on legacy plans.
Limitations
- Independent real-world tests report task completion rates as low as 15% on complex, ambiguous engineering problems.
- Devin struggles with open-ended architectural decisions, novel problem domains, undocumented internal APIs, and tasks where requirements evolve mid-execution.
- ACU-based billing can be opaque and unpredictable for complex tasks, with actual costs potentially exceeding the base plan price significantly.
- Effective use requires precise, well-scoped task definitions and mandatory human review of all outputs before merging.
- Parallel agent coordination above ~5 concurrent agents can introduce complexity.
- The product does not replace senior engineering judgment and performs best at the junior-to-mid engineer tier for well-defined, verifiable tasks.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Bing Copilot | Perplexity | Google AI Mode |
|---|---|---|---|---|---|
Capability0/5 cited (0%) | |||||
What AI coding agents handle multi-repo tasks well — making coordinated changes across a frontend and backend repo in a single session? | |||||
Which autonomous coding agents can reliably write and run tests, interpret failures, and self-correct without human intervention? | |||||
I'm looking for an agentic CLI that supports tool use like web search and shell execution during a coding task — what are my options? | |||||
What autonomous coding tools handle legacy codebases in dynamically typed languages best — Python 2 or older PHP specifically? | |||||
Which cloud coding agents are best for generating and merging pull requests asynchronously without a developer staying in the loop? | |||||
Developer Experience0/5 cited (0%) | |||||
Which autonomous coding agents give the best real-time feedback loop when running multi-step tasks so developers stay in control? | |||||
Which agentic IDEs have the smoothest experience for reviewing and approving AI-generated changes before they touch the main branch? | |||||
What AI coding agents do senior engineers prefer for refactoring large codebases without babysitting every intermediate step? | |||||
Which AI coding agents handle context window limitations most gracefully when working across dozens of files in an enterprise codebase? | |||||
What autonomous coding tools are best suited for a solo developer who wants to delegate routine feature work and focus on architecture? | |||||
Integrations & Ecosystem1/5 cited (20%) | |||||
Which cloud coding agents integrate with CI pipelines to automatically attempt fixes when a build or test suite fails? | |||||
Which autonomous coding agents integrate natively with popular code editors so devs can trigger agent tasks without leaving their IDE? | |||||
What AI coding agents support bring-your-own LLM provider so a platform team can route through an existing enterprise model contract? | |||||
Which agentic coding platforms integrate with project management tools so engineers can assign tickets directly to an AI agent to action? | |||||
What autonomous coding tools have the best ecosystem of community plugins for extending agent capabilities with custom tools and workflows? | |||||
Performance & Reliability0/5 cited (0%) | |||||
What autonomous coding agents run tasks inside a secure sandbox so a compromised prompt can't affect the host filesystem? | |||||
Which autonomous coding agents are most cost-efficient for high-volume use — minimising frontier LLM provider token spend per merged PR? | |||||
Which cloud coding agents have the best uptime and task success rates for a mid-size team running dozens of concurrent agent jobs daily? | |||||
Which AI coding agents complete multi-file tasks fastest without sacrificing correctness — benchmarks or real-world comparisons? | |||||
What agentic coding tools handle long-running tasks reliably — resuming after an interruption rather than starting over from scratch? | |||||
Setup & First Run0/5 cited (0%) | |||||
What are the best agentic IDEs for a team migrating from a traditional code editor that want AI-assisted multi-file editing from day one? | |||||
Which agentic CLI tools work out of the box on popular operating systems without requiring a container sandbox just to get started? | |||||
Which cloud coding agents can be connected to an existing private repo and start opening pull requests with minimal setup? | |||||
What's the easiest AI coding agent to get running locally on a large existing TypeScript monorepo without hours of configuration? | |||||
I'm evaluating autonomous coding agents for a 10-person startup — which ones can a new engineer get productive with in under an hour? | |||||
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Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Augment Code | 8.8% | 32.7% | 0.0% | 0.0% | 8.0% | #7.2 | +0.21 |
| 2 | Anthropic (Claude Code) | 3.2% | 12.7% | 0.0% | 0.0% | 3.2% | #3.9 | +0.35 |
| 3 | Block (Goose) | 3.2% | 12.7% | 0.0% | 0.0% | 3.2% | #4.9 | +0.54 |
| 4 | OpenAI (Codex CLI / Codex) | 3.2% | 10.9% | 0.8% | 0.0% | 2.4% | #7.7 | +0.25 |
| 5 | Factory (Droid) | 2.4% | 10.9% | 0.0% | 0.0% | 1.6% | #4.7 | +0.60 |
| 6 | Cursor (Anysphere) | 2.4% | 5.5% | 0.8% | 0.8% | 2.4% | #16.7 | +0.27 |
| 7 | Warp | 1.6% | 3.6% | 1.6% | 0.0% | 1.6% | #4.0 | +0.30 |
| 8 | All Hands AI (OpenHands) | 0.8% | 5.5% | 0.0% | 0.0% | 0.8% | #2.0 | +0.70 |
| 9 | OpenCode | 0.8% | 1.8% | 0.0% | 0.0% | 0.8% | #2.0 | +0.60 |
| 10 | Cognition (Devin) | 0.8% | 1.8% | 0.8% | 0.0% | 0.8% | #3.0 | +0.80 |
| 11 | Aider AI | 0.8% | 1.8% | 0.0% | 0.0% | 0.8% | #27.0 | +0.00 |
| 12 | Amp | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 13 | Cline Bot Inc. | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 14 | Lovable | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 15 | Replit (Agent 3) | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 16 | Roo Code (Roomote) | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 17 | StackBlitz (Bolt.new) | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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