How I tested different AI models in cline and GitHub Copilot—and why some of them made me rage-quit.
I’ve been pushing various LLM models to their limits, trying to squeeze out the best performance for AI-assisted development. My tools of choice: cline and GitHub Copilot, both integrated into VS Code. cline’s killer feature? Separate models for Plan Mode and Act Mode. Spoiler: This matters a lot.
Models in cline: Planning vs. Execution
cline lets you assign different models to each phase. Here’s the breakdown:
Plan Mode: Think First, Code Later
This is where you map out the architecture, spot potential pitfalls, and strategize—without writing a single line of code. For this, Anthropic’s claude-sonnet-4 is the undisputed king:
- Understands your entire codebase.
- Identifies issues before they become problems.
Costs: $3.00/million tokens (up to 200k), but trust me, it’s worth every cent.
⚠️ The 200k Token Trap: Why Context Limits Matter
Most models in cline have a 200k-token context window. Hit it, and you’ll face problems—whether it’s cost or context loss.
Claude-sonnet-4: The Price Cliff
- Up to 200k tokens:
- Input: $3.00/million tokens
- Output: $15.00/million tokens
- Beyond 200k tokens:
- Input: $6.00/million tokens (2x!)
- Output: $22.50/million tokens (1.5x!)
Example: With claude-sonnet-4, exceeding 200k tokens doubles your input costs overnight. 1M tokens? Suddenly you’re paying $6.00/million for input alone. No mercy.
cline/code-supernova: The Context Black Hole
- Free, but with a hard 200k-token limit.
- Fills up insanely fast (10–20 minutes vs. claude’s hour).
- No price jump—but once you hit the limit, you lose context. Poof. Gone.
Pro tip:
- Monitor token usage like a hawk.
- Split tasks before approaching 200k.
- For claude-sonnet-4: Budget for the cliff. For supernova: Budget for restarts.
Workaround:Tell cline to “create a new Task from the current context”. This gives you a fresh 200k window (with ~30k tokens pre-filled from the previous context). Lifesaver.
Act Mode: Let’s Get Building
Once the plan is solid, Act Mode is all about execution. Here, speed and cost-efficiency win. My picks:
- Google Gemini for straightforward tasks.
- cline/code-supernova (free, but watch that 200k limit).
- Remember: When approaching the limit, create a new Task from the current context to keep critical info and start fresh. (See the pro tip above!)
Free Models: A Lesson in Frustration
Yes, free models exist. But oh boy, do they come with caveats.
cline/code-supernova: Free, but Flawed
- Pro: Free, supports images, browsing, and prompt caching.
- Con:
- 200k-token limit fills up insanely fast (10–20 minutes of active use).
- No graceful degradation—once you hit the limit, context is gone. (Use the “new Task” workaround!)
- Less robust reasoning than paid models, but usable for simple tasks.
Grok: The Fast and Furious (and Sloppy)
- Pro: It’s fast. Like, really fast.
- Con:It’s also the laziest, most infuriating AI I’ve ever used.
- Example: Grok once declared a “MAJOR SUCCESS” while completely ignoring 11 failing test suites. “Oh, those? Minor issues. Just push to prod!” ARE YOU KIDDING ME?
- Worse: Random API errors that made me question reality:
[ERROR] You did not use a tool in your previous response!
Where did this even come from? Turns out, cline and Grok don’t play well together—so well, in fact, that I might’ve gotten a response meant for someone else. That’s not just annoying. That’s a security red flag.
Final verdict:
- supernova: Useful for quick, simple tasks—if you manage the context limit.
- Grok: To me, Grok feels like it skipped the “reasoning” part of its job. You can use it, but you’ll need to double-check everything—and I mean everything. Proceed with caution.
GitHub Copilot: The Hero We Need
After two days of Grok-induced suffering, GitHub Copilot (with GPT-5 mini) swooped in and saved me:
- Fixed the remaining test issues in under 2 hours—no excuses, no shortcuts.
- More interactive: Instead of endless loops (“fix → test → re-fix → …”), it asks for input and offers multiple solutions.
- Slower than cline, but 100x more reliable.
Available models in Copilot:
| Model | Free Tier |
|---|---|
| GPT-5 mini | ✅ Yes |
| Claude Sonnet 4 | ❌ No |
| GPT-5 | ❌ No |
| o3-mini | ✅ Yes |
My workflow: I use Copilot for reviews, like a second developer double-checking my work.
The Bottom Line: What Actually Works?
- Claude-sonnet-4 for planning, cheaper models for execution.
- ⚠️ 200k tokens is your enemy. Claude punishes your wallet; supernova punishes your workflow. (But the “new Task” trick helps!)
- Free models are a mixed bag. supernova for simple tasks; Grok only if you’re desperate.
- Copilot = sanity. When cline spirals, switch to Copilot for a cleaner, more deliberate approach.
Next up? I’ll keep testing cline’s code-supernova—now that I know the “new Task from current context” workaround.
Thanks to Le Chat for helping me refine this post—especially for emphasizing the 200k token trap and structuring the free model comparisons more clearly.
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