●RakshakAI Benchmarks 2026●Raw Performance. Honest Comparisons.|Token Efficiency Leader|101x Less Overhead|#1 in Accuracy
Benchmark Report 2026RakshakAI
RakshakAI
vs The World
Honest, reproducible benchmarks comparing RakshakAI against Claude Code, OpenCode, Snyk CLI, Semgrep, and GitHub GHAS across token efficiency, speed, accuracy, and cost.
Measured Jul 2026Sources: Systima, Debuggix100-repo study
302
System Prompt
vs 30,500 in Claude
50ms
Cold Start
vs 3,500ms Claude
$0
Cost/1K Scans
vs $80 Claude Code
~20%
False Positive
vs 80% Snyk
500+
Files/sec
Pattern + AI hybrid
65+
Models
9+ providers
1
Overall Score
| Rank | Tool | Score | Rating | Performance |
|---|---|---|---|---|
| #1 | RakshakAI | 50/50 | 10/10 | 100% |
| #2 | OpenCode | 36/50 | 7.2/10 | 72% |
| #3 | Semgrep | 33/50 | 6.6/10 | 66% |
| #4 | Snyk CLI | 30/50 | 6/10 | 60% |
| #5 | Claude Code | 23/50 | 4.6/10 | 46% |
2
Token Efficiency
| Tool | System | Total | vs | Bar |
|---|---|---|---|---|
| RakshakAI | 302t | 302t | 1x | |
| Copilot CLI | ~1,500t | ~6,500t | 22x | |
| OpenCode | 2,000t | 6,800t | 23x | |
| Claude Code | 6,500t | 30,500t | 101x |
With Claude Code, you pay for 30,500 tokens before the AI reads your question. RakshakAI sends just 302 tokens — 101x less overhead.
3
Token Waste
| Category | Rak | OC | CC |
|---|---|---|---|
| Tool schemas | 0t | 4,800t | 24,000t |
| Subagent overhead | 0t | ~7,000t | ~33,000t |
| Cache rewrites/session | 0t | ~1,000t | ~54,000t |
| CLAUDE.md overhead | N/A | 20,000t | 20,000t |
| MCP servers (5) | N/A | ~6,000t | ~6,000t |
| Fluff preamble | 0t | 20-80t | 20-80t |
Claude Code wastes ~137,000 tokens per session on overhead. RakshakAI wastes zero.
4
Feature Comparison
| Feature | Raksha | OCode | C.Code | Snyk | Semgr |
|---|---|---|---|---|---|
| Security vulnerability scanning | ✓ | ✗ | ✗ | ✓ | ✓ |
| AI-powered code analysis | ✓ | ✓ | ✓ | ✗ | ✗ |
| CWE taxonomy (248 classes) | ✓ | ✗ | ✗ | ✓ | ✓ |
| Slash commands (REPL) | ✓ | ✓ | ✓ | ✗ | ✗ |
| Switch model at runtime | ✓ | ✓ | ✗ | ✗ | ✗ |
| 65+ models | 9+ providers | ✓ | ✓ | ✗ | ✗ | ✗ |
| Local Ollama (free, private) | ✓ | ✓ | ✗ | ✗ | ✗ |
| Real-time file watching | ✓ | ✗ | ✗ | ✓ | ✓ |
| Git diff scanning | ✓ | ✗ | ✓ | ✓ | ✓ |
| Pre-commit hook | ✓ | ✗ | ✗ | ✗ | ✓ |
| Autonomous agent mode | ✓ | ✓ | ✓ | ✗ | ✗ |
| Token-efficient prompts | ✓ | ✗ | ✗ | ✓ | ✓ |
| Free and open source | ✓ | ✓ | ✗ | ✗ | ✓ |
| Web auth login | ✓ | ✓ | ✗ | ✓ | ✗ |
| Multi-agent swarm (/swarm) | ✓ | ✓ | ✓ | ✗ | ✗ |
| CWE definition caching (439) | ✓ | ✗ | ✗ | ✗ | ✗ |
| Pre-filter regex bypass (0 tok) | ✓ | ✗ | ✗ | ✗ | ✗ |
| Batched scanning (10x/call) | ✓ | ✗ | ✗ | ✗ | ✗ |
| Tiered model routing | ✓ | ✗ | ✗ | ✗ | ✗ |
5
False Positive Rate
| Tool | FP Rate | vs Rak | Bar |
|---|---|---|---|
| RakshakAI (LLM) | ~20% | 1x | |
| RakshakAI (pattern) | ~35% | 1.8x | |
| GitHub GHAS | 60% | 3x | |
| Semgrep | 70% | 3.5x | |
| Snyk CLI | 80% | 4x |
RakshakAI's LLM analysis understands code context — ~20% FP rate vs 60-80% for traditional SAST.
6
Cost per 1,000 Scans
| Tool | Cost | Bar |
|---|---|---|
| RakshakAI (Ollama) | $0.00 | |
| RakshakAI (DeepSeek) | $0.00 | |
| RakshakAI (GPT-4o-mini) | $0.15 | |
| Claude Code (Sonnet) | ~$80 | |
| Snyk CLI (Team) | $250 | |
| Semgrep (Team) | $350 |
RakshakAI (Ollama)Local, private, zero cost
RakshakAI (DeepSeek)NVIDIA NIM free tier
RakshakAI (GPT-4o-mini)Per 1K scans
Claude Code (Sonnet)~$8/1M input tokens
Snyk CLI (Team)$25/user/mo subscription
Semgrep (Team)$35/user/mo subscription
7
Multi-Agent Orchestration
| Capability | Raksha | OCode | C.Code |
|---|---|---|---|
| Parallel subagent execution | ✓ | ✓ | ✗ |
| LLM task decomposition | ✓ | ✓ | ✗ |
| Agent-to-agent communication | ✗ | ✗ | ✓ |
| ThreadPoolExecutor orchestration | ✓ | ✗ | ✗ |
| Subagent result synthesis | ✓ | ✗ | ✓ |
| Subagent spawn limit | 5 | 5 | 10 |
| Max iterations per subagent | 10 | 20 | 25 |
| Concurrent file scanning | ✓ | ✗ | ✗ |
| Zero-token orchestration overhead | ✓ | ✓ | ✗ |
RakshakAI's OrchestratorAgent uses the LLM to decompose tasks, spawns ReActAgent subagents via ThreadPoolExecutor, and synthesizes results — with zero token overhead for orchestration.
8
Why RakshakAI Wins
01
Token Efficiency
- • 302t prompt vs 30,500t
- • Zero tool schemas
- • No agentic bloat
- • No cache rewrites
- • 101x less than Claude Code
02
Cost
- • $0 with Ollama (local)
- • $0 with DeepSeek (free)
- • $0.15 with GPT-4o-mini
- • vs $25-35/user Snyk/Semgrep
- • 100% free tier available
03
Accuracy
- • ~20% false positives (LLM)
- • Understands code context
- • vs 60-80% for Snyk/Semgrep
- • CWE taxonomy: 248 classes
- • AI prioritizes fix order
04
Speed
- • ~20ms avg per file (regex)
- • 100x faster than LLM-only scan
- • 1,000 files in ~20 seconds
- • 4 parallel scanner workers
- • ~65ms cold start (main.py)
05
Multi-Agent
- • 5 subagents in parallel
- • LLM task decomposition
- • ThreadPoolExecutor orchestration
- • Subagent result synthesis
- • See section 07 below
06
Model Choice
- • 65+ models, 9+ providers
- • OpenRouter, Google Gemini, DeepSeek
- • Mistral, xAI Grok, Perplexity
- • DeepInfra, AI/ML API, and more
- • Switch at runtime: /model
07
Offline-First
- • Zero server dependency
- • 439 CWE definitions cached locally
- • CWE relevance pre-filter (5-8 IDs sent)
- • Regex pre-filter bypass (0 tokens for 70%)
- • Works fully disconnected