Claude Opus 4.6 대 Sonnet 4.6 코딩 비교

Claude Opus 4.6 vs. Claude Sonnet 4.6 Anthropic recently dropped the updated Claude 4.6 lineup, and as usual, the two names everyone cares about are Opus 4.6 and Sonnet 4.6. Opus is the expensive “best possible” model, and Sonnet is the cheaper, more general one that a lot of people actually use day to day. So I wanted to see what the real gap looks like when you ask both to build something serious, not a toy demo. Benchmark-wise, there’s a difference of course, but it doesn’t look that huge when it comes to SWE and agentic coding. I kept it super basic: one test (but a big one), same prompt, same workflow. I just compared how close they got without me stepping in. ⚠️ NOTE: Don’t take the result of this test as a hard rule. This is just one real-world coding task, run in my setup, to give you a feel for how these two models performed for me. TL;DR If you just want the takeaway, here’s the deal with these models: First, Opus 4.6 is the peak for coding right now. At the time of writing, it’s basically the OG, and nothing else comes that close. - Claude Opus 4.6 had a cleaner run. It hit a test failure too, but fixed it fast, shipped a working CLI + Tensorlake integration, and did it with way fewer tokens. Rough API-equivalent cost (output only) came out around ~$1.00, which is kind of wild for how big the project is. - Claude Sonnet 4.6 Surprisingly close for a cheaper, more general model. It built most of the project and the CLI was mostly fine, but it ran into the same issue as Opus and couldn’t fully recover. Even after an attempted fix, Tensorlake integration still didn’t work. Output-only cost was about ~$0.87, but it used way more time and tokens overall to get there. 💡 Obviously, this isn’t a test to “compare” the two head-to-head. It’s just to see the difference in code quality. In general, there’s never really been a fair comparison between Opus and Sonnet since their very first launch, Opus has always been on another level. Test Workflow ℹ️ NOTE: Before we start this test, I just want to clarify one thing. I'm not doing this test to compare whether Sonnet 4.6 is better than Opus 4.6 for coding, because obviously Opus 4.6 is a lot better. This is to give you an idea of how well Opus 4.6 performs compared to Sonnet. For the test, we will use everyone's favorite CLI coding agent, Claude Code. As both models are from Anthropic, it works best for both and is not biased toward either. We will test both models on one decently complex task: - Task: Build a complete Tensorlake project in Python called research_pack , a “Deep Research Pack” generator that turns a topic into: - a citation-backed Markdown report, and - a machine-readable source library JSON with extracted text, metadata, summaries, you get the idea. It also has to ship a nice CLI called research-pack with commands like: research-pack run "" research-pack status research-pack open We’ll compare the overall feel, code quality, token usage, cost, and time to complete the build. 💡 NOTE: Just like my previous tests, I’ll share each model’s changes as a .patch file so you can reproduce the exact result locally with git apply . Why Tensorlake? Tensorlake is a solid choice for this Opus 4.6 vs Sonnet 4.6 test because it is a real platform with enough complexity to quickly show whether a model can actually build something end to end. It has an agent runtime with durable execution, sandboxed code execution, and built in observability, so the test is not just writing a few functions, it is wiring up a production workflow. And selfishly, it is also a good dogfood moment. 👀 If a model can spin up a Tensorlake project from scratch and get it working, that is a pretty strong sign for two things: these recent models are getting scary good and how usable Tensorlake is for building serious agent style pipelines. Coding Tests Test: Deep Research Agent For this test, both models had to build the research_pack Tensorlake project in Python. The goal was simple: give it a topic, it crawls stuff, figures out sources, improves them, and spits out: report.md with[S1] style citationslibrary.json with the full source library- a clean CLI: research-pack run/status/open - plus Tensorlake deploy support so you can trigger it as an app, not just locally One thing that went a bit crazy is that both models ran into basically the exact same/similar issue during the run. That shows how similarly these models can behave, which is kind of creepy. If you give them the exact same task and constraints, they’ll often make similar choices. I wanted to call that out because you might’ve noticed the same pattern too. Not surprisingly, Opus fixed it much faster and with way fewer tokens. Sonnet took longer, burned a lot more context trying to debug it, and even after the fix pass, it still didn’t fully work. Claude Opus 4.6 Opus was pretty straightforward. It did hit a failure while running tests, but it was a quick fix. After that, everything looked clean: CLI worked, offline mode worked,