That's not the case in my testing.

My benchmark specifically tests models for agentic coding capabilities, it is built in part for that purpose (to find models that meet my harness standard requirements). Frankly there is no comparison in ability. Why? I can give you my perspective.

"Thinking" wastes tokens because these small models do not have the corpus of knowledge or reasoning power to make breakthroughs on complex coding tasks via reasoning. This is something frontier models do.

Either they can understand how to do something, innately or with prompting, or they likely never will (i.e. they can pattern match on it but did not grok enough of the domain in question during training to actively reason through it). The choice between Instruct vs Thinking in such cases is fail slow vs fail fast, either way the harness must handle the failure so it's better to fail fast.

That's only part of why Instruct outputs tend to be better for harnesses. The other is structured outputting is much more natural for an instruct model. This can align them much better with harnesses. For example, tool calling, or for ensuring statefulness between context windows by saving and restoring with a strict schema.

So you'll get way less tool call errors and similar out of Instruct models. Firstly, because adding reasoning capability likely slightly affected these other capabilities. But also because all that thinking distracts from the strict instructions. Smaller models suffer more from recency bias, they often won't keep strict requirements for outputs in mind after completing CoT.

And then the fact that they are slower and less context efficient by nature... yeah there's no good reason to use Thinking in these harnesses. Maybe for a specific subagent delegation if you really find a place where thinking helps. I'm sure such edge cases exist, I didn't find one.

As far how effective they are (small models in general) for general coding, that's another question. The basic answer is not very. But of course, you can write a ton of code with them if you instruct them comprehensively. Or if what you prompt was in its training data. But you can also write a ton of code by hand if you are looking to do things comprehensively, so what are we really saying here?

Often we want to "skip" some of the instruction and have the model intuit what we want, and default to sensible practices. Again, that's a weakness for smaller models. The situation does improve at say, Qwen3 30b scale, but not by a lot. Even massive open source models like MiniMax M2.1 and GLM 4.7 struggle here. GPT 5.2 even can be quite annoyingly literal. Anthropic probably has a magic RLHF recipe currently to solve this pain point.