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Augment Code

AI Code Assistantfreemium

Enterprise AI coding platform with deep codebase understanding. Designed for large teams with context-aware suggestions.

AugmentWebsiteDocsCompare →Changelog (1)Last evaluated: Sep 25, 2026
7.8/10
Weighted average across all evaluation dimensions. Scores are based on benchmark testing, expert review, and community feedback.
Capability Profile
Code GenerationQuality and accuracy of generated code, including correctness, completeness, and adherence to best practices.
Context UnderstandingAbility to comprehend project structure, dependencies, and codebase-wide context for accurate assistance.
Developer ExperienceEase of use, IDE integration quality, onboarding speed, and workflow friction reduction.
Multi-file EditingCapability to make coordinated changes across multiple files while maintaining consistency.
Debugging & FixingEffectiveness at identifying bugs, suggesting fixes, and resolving errors in existing code.
Ecosystem IntegrationSupport for various languages, frameworks, package managers, and development tools.
Score Trend
7.3 → 7.8▲0.5
Score Changes
Code Generation7.5 → 8.0 ▲0.5
Sep 25, 2026#1 SWE-bench Pro result
Context Understanding8.0 → 8.4 ▲0.4
Sep 25, 2026Context Engine as standalone MCP server
Developer Experience7.2 → 7.3 ▲0.1
Sep 25, 2026Cosmos/Experts enterprise feature
Multi-file Editing7.0 → 7.6 ▲0.6
Sep 25, 2026SWE-bench Pro multi-file benchmark win
Debugging & Fixing7.0 → 7.4 ▲0.4
Sep 25, 2026Context Engine fault localization
Ecosystem Integration7.2 → 8.0 ▲0.8
Sep 25, 2026Context Engine MCP server usable in rival tools
Pricing & Licensing
Pricing
Free tier, Pro pricing on request, Enterprise custom
License
Proprietary
Community
Developers
50K+ developers
Benchmark Results
Context Window Stress Test
Time: 10
Broken Imports: 1
Compiles Clean: 0
Changes Correct: 4
Features
vscodejetbrainsenterprisecodebase-understandingcontext
Dimension Scores
8-105-7.9<5
How we score →
Code GenerationQuality and accuracy of generated code, including correctness, completeness, and adherence to best practices.Weight: 100%
8.0

Auggie CLI scored #1 on Scale AI's SWE-bench Pro (51.80%, Apr 2026), ahead of Cursor (50.21%), Claude Code (49.75%), and OpenAI Codex (46.47%) -- despite Augment and Cursor sharing the same underlying model, attributed to Augment's context-retrieval architecture.

Context UnderstandingAbility to comprehend project structure, dependencies, and codebase-wide context for accurate assistance.Weight: 100%
8.4

Context Engine released as a standalone MCP server (Feb 2026), pluggable into Cursor/Claude Code/Zed; claimed >70% agentic-coding improvement when used with those tools -- direct evidence of context-retrieval strength.

Developer ExperienceEase of use, IDE integration quality, onboarding speed, and workflow friction reduction.Weight: 100%
7.3

"Cosmos"/Experts concept (persistent AI experts carrying memory across sessions) targets the 200-500 developer enterprise segment; no funding distress or reliability scandal found.

Multi-file EditingCapability to make coordinated changes across multiple files while maintaining consistency.Weight: 90%
7.6

SWE-bench Pro is a multi-file-heavy benchmark; the #1 result reflects strength here specifically.

Debugging & FixingEffectiveness at identifying bugs, suggesting fixes, and resolving errors in existing code.Weight: 90%
7.4

Context Engine's retrieval accuracy aids fault localization in large monorepos (400K+ files).

Ecosystem IntegrationSupport for various languages, frameworks, package managers, and development tools.Weight: 80%
8.0

Context Engine as an MCP server is now usable inside competitor tools -- a genuine ecosystem-integration expansion beyond Augment's own product.

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