# Devin vs EvalCore

Devin records 2,549,346 monthly visits with +1% month-over-month growth and Paid pricing. EvalCore records 0 monthly visits with 0% growth and Free pricing.

| Metric | [Devin](https://openaitools.app/tool/devin) | [EvalCore](https://openaitools.app/tool/evalcore) |
| :--- | :--- | :--- |
| Website URL | https://devin.ai | https://evalcore.cc |
| Categories | AI Code Assistant, AI Agent, AI Developer Tools | AI Testing, AI Developer Tools, Large Language Models (LLMs), AI Agent |
| Pricing Model | Paid | Free |
| Monthly Visits | 2,549,346 | 0 |
| MoM Growth Rate | +1% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 1,386,401 | 0 |
| Review Score | 4.3 | 4.8 |
| Saved Count | 0 | 0 |
| Supported Platforms | website | Website |

## Devin

Devin sits at the agentic end of the AI coding spectrum. Instead of suggesting the next line, it takes a ticket-level goal - "add SAML SSO for enterprise customers" or "investigate this API latency spike" - explores the codebase across repositories, implements changes, runs tests, and explains the resulting pull request in plain language. It maintains a wiki-style map of the codebase so it can navigate unfamiliar services and keep documentation current as the code changes.

A defining feature is that Devin works on its own machine. It provisions Windows, Mac, and Linux virtual machines as needed, builds the project, exercises it in a browser, and can record itself clicking through the feature it just built. Long-running work continues in the cloud when your laptop is closed, and large tasks can be split across parallel agents that Devin coordinates and reports on.

For teams, Devin extends beyond one-off prompts. You can tag @Devin in Slack or Teams so it reads a thread and replies with a PR, set up automations that fix failing tests or patch vulnerable dependencies as they appear, and run security reviews. Pricing is seat-based and enterprise-oriented, with an Ultra tier for heavier autonomous usage, and Cognition has marketed large security-credit programs for enterprises.

**Pricing & Plans:** Paid, enterprise-oriented. Devin is sold on a per-seat subscription with tiered plans (including higher-capacity tiers for heavier autonomous use) plus usage allowances; Cognition also runs credit programs and enterprise agreements. Because plans and credit packages change, check Cognition's official pricing page for current numbers.

- **End-to-end autonomous coding:** Devin plans, writes code, runs tests, and opens pull requests across complex, multi-repository codebases without waiting for line-by-line approval.
- **Its own virtual machines:** Devin provisions Windows, Mac, and Linux VMs, builds and runs the project, and tests in a real browser - even recording itself using the feature it built.
- **Slack, Teams, and alert integration:** Mention @Devin in a thread to delegate a fix, or connect CI, PagerDuty, and issue trackers so it reacts to failures and alerts automatically.
- **Parallel cloud agents:** Delegate a large task and Devin splits it across parallel cloud agents that keep working after you close your laptop, then reports back.
- **Codebase wiki and automations:** Devin maintains a living map of the architecture and supports recurring automations such as dependency upgrades, test fixes, and security scans.

**Strengths:** Handles ticket-level autonomous work rather than only suggesting code completions; Tests in its own browser and VMs, which catches issues that pure code generation misses; Deep workflow integration with Slack, Teams, CI, and alerting tools; Keeps working in parallel cloud agents even when your machine is off

**Limitations:** Premium, seat-based pricing puts it out of reach for casual individual use; Complex or poorly specified tasks still need human review before merging; Maximum value depends on having a well-structured repo and clean test setup

[Profile (Markdown)](https://openaitools.app/tool/devin.md)

## EvalCore

EvalCore solves a common failure mode in AI engineering: you tweak a prompt, swap a model, or bump a dependency, and something quietly breaks in production. Instead of trying to re-run expensive, non-deterministic model calls in CI, EvalCore treats AI behaviour like a snapshot. An eval is just a YAML file describing a target and a JSONL dataset, with scorers stacked on top to define what "good" means.

The first run happens live against a real model — anything OpenAI-compatible, vLLM, Ollama, a custom REST API, a shell command, or OpenTelemetry/OpenInference traces. Every request and response is written to a local SQLite cassette, keyed by a hash of the canonical request, along with token and cost figures on the summary. From then on, CI replays those recordings byte-for-byte with `--cache replay`: no network, no API keys, no flaky judge model, and identical inputs always produce identical verdicts.

Because the cassette is committed to your repository, a `--baseline main` comparison fails only on genuine regressions, and the process exit code gates the pipeline. Trials, model comparisons, agent traces, gates, and reports are all a few lines of YAML or a flag rather than a framework. It ships as one small Rust binary, works with any language on the other side of the target, and collects no telemetry.

**Pricing & Plans:** Free and open source under the Apache-2.0 licence, with $0 cost in CI thanks to offline cassette replay. Install via cargo or prebuilt binaries; no signup, no server and no telemetry.

- **Record once, replay forever:** A live run captures every model request and response into a local SQLite cassette that CI can replay offline and deterministically at zero cost.
- **Anything can be a target:** Point EvalCore at OpenAI-compatible endpoints, vLLM, Ollama, your own REST APIs, shell commands, or OTel/OpenInference traces — no SDK required.
- **Scorers that define 'good':** Stack contains, judge-rubric and other scorers in YAML to express exactly what a correct answer looks like for each case.
- **Regression gates and baselines:** Compare against a baseline branch so only real regressions fail, and let the process exit code gate your pull requests.
- **Cost, tokens and reports built in:** Every summary reports token usage and spend for live runs, while trials, model comparisons and history give you a clear picture over time.

**Strengths:** Genuinely free and open source under Apache-2.0, with no usage-based pricing; Deterministic offline replay removes flaky, expensive model calls from CI; Single dependency-free binary that works regardless of your app's language; Configuration is plain YAML plus JSONL, so suites are easy to review and version

**Limitations:** Requires a Rust toolchain or a prebuilt binary and comfort with the command line; Cassettes must be recorded live first, so initial setup needs real API access; No hosted dashboard — results and history live in your repo and CI logs

[Profile (Markdown)](https://openaitools.app/tool/evalcore.md)

## Frequently Asked Questions

### How do Devin and EvalCore compare in monthly traffic?

Devin has 2,549,346 monthly visits, while EvalCore has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Devin has a month-over-month growth rate of +1%; EvalCore has 0%.

### How does pricing compare?

Devin: Paid. Paid, enterprise-oriented. Devin is sold on a per-seat subscription with tiered plans (including higher-capacity tiers for heavier autonomous use) plus usage allowances; Cognition also runs credit programs and enterprise agreements. Because plans and credit packages change, check Cognition's official pricing page for current numbers. EvalCore: Free. Free and open source under the Apache-2.0 licence, with $0 cost in CI thanks to offline cassette replay. Install via cargo or prebuilt binaries; no signup, no server and no telemetry.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | An AI-native code editor providing advanced multi-file code generation and editing, though less fully autonomous end-to-end than Devin. | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Manus](https://openaitools.app/tool/manus) | 19,195,779 | -1.7% | Free Trial | A general autonomous agent platform executing multi-step goals and browsers, suitable for broader web and coding workflows. | [.md](https://openaitools.app/tool/manus.md) |
| 3 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Google's agentic development platform running parallel AI coding agents across an IDE and CLI workspace. | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 4 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | A cloud development platform featuring an AI agent that builds, runs, and deploys full-stack apps automatically. | [.md](https://openaitools.app/tool/replit.md) |
| 5 | [Qoder](https://openaitools.app/tool/qoder) | 5,145,040 | +81.8% | Freemium, Free Trial, Free | An agentic coding platform and IDE plugin that reads entire codebases to execute complex multi-file tasks. | [.md](https://openaitools.app/tool/qoder.md) |
| 6 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | An AWS spec-driven AI IDE and agent platform that plans, builds, reviews, and verifies software engineering work. | [.md](https://openaitools.app/tool/kiro.md) |
| 7 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) |
| 8 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Run many coding agents in parallel, each in its own worktree | [.md](https://openaitools.app/tool/orca.md) |
| 9 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | GenAI-native end-to-end QA agent that plans, writes and heals tests in plain language | [.md](https://openaitools.app/tool/kaneai.md) |
| 10 | [Trae](https://openaitools.app/tool/trae) | 702,366 | -7.8% | Freemium | An AI-powered IDE and coding agent that indexes entire repositories to plan, build, and test software. | [.md](https://openaitools.app/tool/trae.md) |

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Source: https://openaitools.app/compare/devin-vs-evalcore

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