01 SWE-Bench Performance Data Feb 2026 · Independent Run
CONTEXT LOSS — PRIMARY PAIN POINT
Cursor's effective usable code context is ~70–120K tokens (not the marketed 200K) after system prompts, chat history, and response budget are deducted. For cross-cutting changes across your Go + Rails + Java services, this is the root cause of inconsistent AI output.
Metric
Cursor
w/ Sonnet 4.6
w/ Sonnet 4.6
Claude Code
Opus 4.6
Opus 4.6
ChatGPT Codex
GPT-5.3 Codex
GPT-5.3 Codex
SWE-bench Verified Score
TOP RANKED
Multi-file Coherence
Good via RAG; degrades past ~50 files in session
Excellent — reads files on-demand, follows import chains BEST
Good for isolated tasks; cloud sandbox limits cross-repo
pass@5 (agent retries)
Moderate — model-dependent
Highest of all evaluated agents BEST
Strong on well-defined isolated tasks
Token Efficiency
Good — RAG reduces context per request
5.5x fewer tokens vs competitors BEST
Most token-efficient model architecture
Metric
Cursor
Claude Code (Opus 4.6)
GPT-5 family
SWE-bench Pro Score
(private unseen codebases)
(private unseen codebases)
Not independently measured
Key Insight
⚠ All models drop from 70%+ on Verified to ~23% on Pro (private, unseen codebases). This is the benchmark that reflects YOUR production codebase. No tool solves hard software engineering. Human review is non-negotiable.
Effective usable code context after overhead
Cursor Agent
~70–120K usable
of 200K claimed⚠ RAG truncation
Claude Code
200K+ reliable · reads on-demand
full window✓ Best for large repos
ChatGPT Codex
Cloud sandbox
per-task only⚠ no cross-repo
Why this matters for your stack: A cross-cutting change across Go microservices + Rails monolith + Java JVM services can span 300–500 relevant files. Cursor's RAG works for scoped, single-service tasks. Claude Code's on-demand file reading directly addresses your context loss pain point.
02 Task-to-Tool Decision Matrix
Daily Feature Dev
Endpoints, CRUD, UI componentsCursor
Endpoints, CRUD, UI componentsCursor
Inline Tab completions preserve flow state. Composer for multi-file scaffolding. RAG context is sufficient when work is scoped to one service.
Use: Claude Sonnet 4.6 in Cursor · fast, cost-efficient, strong on Go+Rails
Cross-Service Refactoring
Auth changes, API contracts, DB migrationsClaude Code
Auth changes, API contracts, DB migrationsClaude Code
This is exactly where Cursor's context truncation fails. Changes touching middleware, services, and clients across 10+ files need on-demand reading and full coherence.
Use: Claude Opus 4.6 via Claude Code CLI · 200K+ real context
Code Review & Explanation
Legacy Rails/Java, PR reviewCursor
Legacy Rails/Java, PR reviewCursor
Inline chat + @Docs + @codebase is fastest for "what does this do?" Single-module understanding stays within Cursor's context budget comfortably.
Use: Sonnet 4.6 or Gemini 3 Pro in Cursor
Autonomous Background Tasks
Test suites, doc gen, boilerplateChatGPT Codex
Test suites, doc gen, boilerplateChatGPT Codex
Well-defined, isolated tasks with clear specs. Queue 3–5 tasks, Codex works async in cloud sandbox, you review PRs. Ideal for test gen and migration scripts.
Use: GPT-5.3 Codex via ChatGPT ($20/mo flat)
Architecture & System Design
NATS patterns, event-driven, schema decisionsClaude Code
NATS patterns, event-driven, schema decisionsClaude Code
Deep reasoning over trade-offs and multi-step problem decomposition. Opus 4.6 leads SWE-bench on complex multi-step problems. Use for NATS JetStream, microservice boundaries, ODR sync architecture.
Use: Claude Opus 4.6 (Claude Code or claude.ai chat)
Debugging & Error Diagnosis
Stack traces, failing tests, Go panicsCursor
Stack traces, failing tests, Go panicsCursor
Cursor Debug Mode auto-captures error context, stack trace, and relevant file context. For distributed issues across services, escalate to Claude Code.
Use: Sonnet 4.6 in Cursor Debug Mode
Large-Scale Migrations
Rails 6→7, Java 11→21, Go module refactorsHybrid
Rails 6→7, Java 11→21, Go module refactorsHybrid
Plan with Claude Code (full context), execute file batches with Codex (parallel agents per module), validate diffs in Cursor (visual review). Proven: Rails 5→7 across 200+ files with 3 parallel agents.
Plan: Claude Opus → Execute: Codex agents → Review: Cursor
Security & Perf Review
Auth audit, N+1 detection, race conditionsClaude Code
Auth audit, N+1 detection, race conditionsClaude Code
Security needs holistic understanding across call chains — exactly where Cursor's truncated context fails. Never use Codex for this (code exposure risk in cloud sandbox).
Use: Claude Opus 4.6 · never use Codex for security-sensitive paths
03 Decision Routing Rules Team Policy
R1
Default tool is Cursor (Sonnet 4.6). All daily coding starts here. Only escalate when hitting a context or autonomy ceiling. This keeps baseline cost at $40/user/month.
CURSOR DEFAULT R2
Escalate to Claude Code when: (a) task requires coherent changes across 8+ files, (b) task is architectural, (c) doing a security audit, (d) Cursor produced inconsistent output suggesting context truncation.
CLAUDE ESCALATION R3
Use ChatGPT Codex for async delegation only. Task spec must be fully written, scope self-contained. Never for cross-service changes or security-sensitive code.
CODEX ASYNC R4
Model selection within Cursor: Default Auto/Sonnet 4.6. Switch to Opus 4.6 for complex reasoning (costs credits faster). Use Gemini 3 Flash for rapid iteration where speed > depth.
MODEL SELECTION R5
Context hygiene in Cursor: Use
CONTEXT HYGIENE .cursor/rules/ MDC files for architecture conventions. Always @-reference specific files explicitly for cross-service tasks. Keep sessions scoped to one service at a time. Run /summarize before context fills. R6
Never send production secrets, PII, or proprietary algorithms to ChatGPT Codex. It runs in OpenAI's cloud sandbox. For sensitive codepaths (auth, payment, PII), restrict to Cursor privacy mode or Claude Code with self-managed API key.
SECURITY POLICY 04 Model Quick-Reference Available in Cursor
| Model | Best For | Stack Affinity | Credit Cost | Available |
|---|---|---|---|---|
| Claude Sonnet 4.6 | Daily coding, PR work, debugging, test writing | Go, Rails, Java — all strong | Low ✓ | Default |
| Claude Opus 4.6 | Architecture, cross-service refactors, security — in Cursor or Claude Code CLI | Best on multi-service Go+Java | High ⚠ | Premium |
| GPT-5.2 / GPT-5.3 | Isolated async tasks, boilerplate, test generation (via Codex CLI) | Strong JS/TS; solid Go | Flat $20 | GPT-5.2 ✓ |
| Gemini 3 Pro | Large context refactors, fast frontend iteration | Particularly strong JS/TS | Low-Mid | Available |
| GPT-5 Mini / Grok Fast | Repo search, simple edits, quick Q&A | General — all languages | Minimal | Available |
05 Cost Model 10-Engineer Team
Scenario
Included
Monthly
Verdict
Cursor-Only Baseline
Cursor Team $40/user · Sonnet 4.6, GPT-5.2, Gemini 3 · IDE + agent + autocomplete
$400/mo
✓ Starting point
Cursor + Claude Code
(Recommended)
(Recommended)
Cursor Team + Claude Pro ~5 senior devs ($20/mo) for Claude Code CLI access on arch/security tasks
$500/mo
✓ Optimal for your pain point
Full Stack
Cursor Team + Claude Pro (5 devs) + ChatGPT Plus (3 devs for Codex async)
$640/mo
◊ Good for migration-heavy teams
Claude Code All-In
Claude Code Premium Teams $150/user — all get Opus 4.6 + 1M context + CLI
$1,500/mo
Only if context is mission-critical for all devs
06 Tool Identity & When to Use Each
Cursor
"Code alongside me in a familiar IDE"
Daily feature work on any service
Inline autocomplete + Tab completion
Scoped debugging (single service)
Quick code review & explanation
Multi-model flexibility per task
⚠ Avoid: cross-service, security audit, 20+ file tasks
Claude Code
"Reason through this with full context"
Cross-service architectural changes
Security audit + auth review
Full 200K+ context on large repos
System design & trade-off analysis
Go microservices + NATS patterns
⚠ Avoid: quick fixes, UI autocomplete
ChatGPT Codex
"Tell me what to do, I'll handle it"
Async background execution
Parallel agent workflows
Boilerplate & test suite generation
Documentation writing
Scoped migrations on isolated repos
🚫 Never: security code, cross-service, secrets
07 Token Governance ACTIVE PROBLEM
ROOT CAUSE: AUTO MODE IS A TRAP + OPUS AS DEFAULT
Cursor's Auto mode routes to claude-4.6-opus-high-thinking for subagents without warning. One documented case: a single prompt consumed 300M tokens (70% of monthly quota). Opus also greedily fills its context window on large codebases — the context compression it triggers worsens output quality and costs more. Opus should be ~10–15% of total token usage, not the default.
Model Usage Distribution — Drag to simulate your team's mix
Claude Opus 4.6 70%
Claude Sonnet 4.6 20%
GPT-5.2 / Codex 5%
Mini / Fast Models 5%
0%Usage distribution100%
Team Size
Avg sessions/dev/day
Codebase size
Est. Monthly Cost
—
Cursor tokens only
Waste Factor
—
vs recommended mix
Recommended Mix
~$—
Opus 15% · Sonnet 60%
Select tasks your team runs on Opus today
Projected Savings
0 / 8
THE THREE-PHASE RULE — Community-Validated Pattern
Opus should only be used for phases where its deeper reasoning is genuinely needed (Research + Plan). Mechanical execution uses cheaper models. Each phase produces a markdown artifact that carries context forward — eliminating the need to re-load the codebase.
Phase 01
Research
Claude Opus 4.6
Explore codebase architecture
Identify affected services + files
Document patterns + conventions
Output:
RESEARCH.mdKill session when done. Don't let it run into Phase 2.
→
Phase 02
Plan
Claude Opus 4.6
Load RESEARCH.md as context
Design implementation approach
Define acceptance criteria
Break into sub-tasks per service
Output:
PLAN.mdKill session when done. Never let planning bleed into execution.
→
Phase 03
Execute
Sonnet 4.6 / GPT-5.2
Load PLAN.md as context
Implement one sub-task at a time
Run
/summarize between sub-tasksUse Debug Mode for test failures
Review diffs in Cursor before commit
5–8x cheaper than running Opus here. Same quality for mechanical execution.
Cost comparison: 10-session sprint on a cross-service feature
WITHOUT 3-Phase Rule (current)
10 sessions × all on Opus
~200K tokens/session avg
$5 input + $25 output/session
≈ $300 / sprint
~200K tokens/session avg
$5 input + $25 output/session
≈ $300 / sprint
WITH 3-Phase Rule (recommended)
2 sessions Opus (Research + Plan)
8 sessions Sonnet (Execute)
$60 + $24 = $84 total
≈ $84 / sprint
8 sessions Sonnet (Execute)
$60 + $24 = $84 total
≈ $84 / sprint
→ 72% cost reduction on execution-heavy work. Output quality equivalent for mechanical tasks.