Tl;Dr: I dug up the transcripts of GPT-5.6 Terra vs Mimo-2.5-pro from my agent's task history and found gpt-5.6-terra used on average 48.5% more transcript tokens than Mimo in my tasks (sample size >80 for each). Mostly due to pulling unnecessary reads, running noisy commands, and poking at Git when nobody asked.
OpenAI had given me free credits that expire soon so I spent the last few days testing gpt-5.6 as my main model. I used mostly Terra in my work.
My overall feeling was, it does get things right most of the time but results in a much larger context window than necessary. This was obviously anecdotal, so I thought I should check against the previous model I had been using (mimo-2.5-pro) for several days.
The setup: Same repo (https://github.com/dirac-run/dirac), relatively averaged out task distribution due to a large enough sample size, Dirac was the harness used.
Data: Only the transcript size at the task end was picked, to eliminate cache effects entirely (Mimo cached significantly better than Terra, for reference).
Terra vs Mimo: Task Metrics Comparison
Average Final Transcript Size
| Model | Tasks | Avg Transcript Tokens |
|---|---|---|
| gpt-5.6-terra | 95 | 91,794 |
| xiaomi/mimo-v2.5-pro | 81 | 61,831 |
gpt-5.6-terra's average transcript is 48.5% larger than xiaomi/mimo-v2.5-pro's (91,794 vs. 61,831 tokens).
Average Tool Calls per Task
| Tool | gpt-5.6-terra | xiaomi/mimo-v2.5-pro |
|---|---|---|
| read_file | 6.99 | 12.68 |
| execute_command | 6.36 | 6.31 |
| search_files | 4.24 | 7.32 |
| edit_file | 3.74 | 2.63 |
| get_function | 2.36 | 3.31 |
| list_files | 1.01 | 1.26 |
| write_to_file | 0.59 | 0.99 |
| get_file_skeleton | 0.63 | 0.96 |
| diagnostics_scan | 0.56 | 0.56 |
| say | 0.51 | 0.70 |
| find_symbol_references | 0.18 | 0.06 |
| replace_symbol | 0.02 | 0.05 |
Observations
- read_file: Mimo emitted a lot more file reads but they were very frugal reads with disciplined line ranges; Terra preferred at least a few hundred lines every call.
-
execute_command: Anecdotally, this was the biggest token waste in Terra’s runs. It showed
little to no harness awareness. Executed a bunch of commands which resulted in very large outputs. For
instance,
npm run test:unit 2>&1(Terra version) vsnpm run test:unit 2>&1 | tail -20(typical Mimo version).
Terra also showed a surprisingly high number of edit-and-check loops, even for trivial changes such as, make one line change, and make sure things compile/test before making the next change. This caused more edit_file tool calls on average. Mimo on the other hand produced all changes first and then went on fixing loops once changes were done.
Most annoying parts
- Terra showed a tendency to maximize context window, even when the things it read were evidently not relevant (the "you are right, I overstepped" case). These qualities of maximal exploration and measured small incremental change/test loops make models score high on benchmarks but also make the model very annoying to use.
- Terra showed an obsession with Git. Used Git as a debugging tool by constantly examining Git commits and Git diffs. So much so that I had to change the AGENTS.md to instruct it not to do that too much; it ignored the instructions.
Overall, Terra is not a bad model by any means, but I wouldn’t use it at the current price - mainly because of the volume cost and to a larger extent, the amount of time it takes to get tasks done. The experience felt a bit more sluggish than expected.