01CodingAnysphere
Cursor
An AI-first code editor with repository context, agent workflows, and selectable model providers. The dominant AI-native IDE by revenue.
USE IT
ENTRY COSTFree Hobby tier; Pro $20/mo
94/100INDEX SIGNAL
HEAD-TO-HEAD SPECIFICATION & VRAM MATRIX
Cursor vs GitHub Copilot vs Qwen2.5-Coder-32B-Instruct
Side-by-side technical trade-offs across real GGUF quantization VRAM math (24GB target rig), context retention, commercial license terms, and brutal truth verdicts.
COMPARED ENGINES3 / 3
01Coding
02CodingGitHub
GitHub Copilot
The most widely deployed AI coding assistant, embedded across VS Code, JetBrains, and GitHub itself with multi-model routing.
WAIT
ENTRY COSTFree tier; Pro $10/mo
90/100INDEX SIGNAL
03CodingAlibaba / Qwen
Qwen2.5-Coder-32B-Instruct
Alibaba Qwen's flagship open-weight coding model with 32.5B dense parameters, supporting a 128K context window with verified state-of-the-art Python, TypeScript, and multi-language code generation.
SELF-HOST
ENTRY COSTFree weights; compute and storage cost extra
94/100INDEX SIGNAL
SIDE-BY-SIDE TECHNICAL MATRIX
3 engines · 13 criteriaWhere each engine wins: Cursor vs GitHub Copilot vs Qwen2.5-Coder-32B-Instruct
| CRITERION | CursorAnysphere | GitHub CopilotGitHub | Qwen2.5-Coder-32B-InstructAlibaba / Qwen |
|---|---|---|---|
| VERDICT | USE IT | WAIT | SELF-HOST |
| THE BRUTAL TRUTH | It's a wrapper — you're paying $20/mo for orchestration on top of GPT/Claude. Only worth it if it saves you 1+ hour per week. Otherwise, self-host Qwen2.5-Coder-32B. | Cheaper than Cursor, but the agentic workflow is still behind. If you're on GitHub Enterprise already it's a no-brainer — otherwise, look at Cursor first. | The premier open coding model for local developers. If you have a 24GB GPU, it delivers frontier coding assistance with zero API fees. |
| CATEGORY | Coding | Coding | Coding |
| ARCHITECTURE & PARAMS | Multiple selectable models | Multi-model routing | Qwen2.5-Coder-32B-Instruct |
| QUANTIZATION & MIN VRAM | Hosted only | Hosted only | Q4_K_M: 20GB · Q5_K_M: 24GB · Q8_0: 36GB · FP16: 65GB |
| RECOMMENDED VRAM | Cloud API | Cloud API | 25 GB (with KV Cache) |
| CONTEXT WINDOW | Varies by selected model | Varies by model | 128K tokens |
| LICENSE TERMS | Product terms | GitHub terms | Apache 2.0 |
| PRICING & ENTRY COST | Free tier / $20 mo per seat | Free / $10 mo per seat | Free weights · your hardware |
| TRUE FOUNDATION ENGINE? | Wrapper / Aggregator | Wrapper / Aggregator | ✓ Foundation Engine |
| STRENGTHS | Repository-aware editing and agent workflows · Flexible model selection across providers | Deep IDE integration and code review · Generous free tier for individuals | Matches closed frontier models on SWE-bench and human-eval coding tasks · 128K context window with 32K generation length · Apache 2.0 permissive open license |
| KNOWN GOTCHAS | Advanced usage may require a paid plan · Underlying model depends on configuration | Agent features vary by editor · Enterprise governance adds cost | Dense 32B footprint requires a full 24GB VRAM GPU for comfortable local execution · High memory bandwidth required for high-token/sec throughput |
| CLOUD GPU DEPLOY | Official Website ↗ | Official Website ↗ | Deploy on RunPod ($0.14/hr) ↗ |
VRAM Precision Footnote: Stactora benchmarks assume typical KV cache overhead at 8K context and modern runtime engines (Ollama, vLLM, ComfyUI). Actual memory usage may vary with longer context or parallel batching.