I believe the group-chat comparison. I do not believe it reflects the model’s true value.
One person used the same prompt with GLM 5.2 + Claude Code and DeepSeek V4 Flash + Codex. The first run produced a playable game in a little over twenty turns. Another similar test left hundreds of records and a long repair loop. The first comparison is more persuasive because the person and prompt were the same. But the prompt still left the product undefined: audience, platform, core delight, trade-offs, and acceptance criteria.
A model that gets an underspecified task running has shown that it can fill in blanks. It has not shown that it built the artifact you actually intended.
Two axes, not one leaderboard
I think about models on two axes.
Capability is the ceiling on unfamiliar problems: useful questions, coherent plans, product trade-offs, and hypotheses when the bug is unknown.
Maturity is reliability once the process is defined: steady tool use, plan adherence, state preservation, recovery, and willingness to check again.
Parameter count may influence the capability ceiling. Maturity is not a simple function of parameters; post-training, tool adaptation, context policy, and real-task feedback matter.
That is how I see DeepSeek V4 Flash in my current Codex setup. It is not my first choice for product direction or open-ended exploration. It is valuable when the goal and steps are settled: read files, make bounded edits, run checks, repair known errors, and verify again.
How I would start from zero
For a lightweight Angry Birds-inspired mobile game, I would first ask a strong model to clarify the audience, platform, core fun, visual and mechanics trade-offs, and what version one means. Then I would ask it to turn that direction into module boundaries, dependencies, acceptance evidence, and recovery points.
I switch to a mature executor when the plan stops drifting, every step has observable evidence, and adversarial review no longer changes the overall direction. The switch is not a turn count.
Debugging follows the same rule. If I know roughly where the bug is and how to repair it, I use a fast mature model. If I do not know where it is, I escalate to a stronger model that can rebuild the problem model. Acceptance gets the original goal, plan, diff, tests, risks, and run logs—not just “done.”
The handoff between models is uncertainty, not a file extension.
My practical measures are simple: how close the repaired artifact is to the initial expectation, how much time and token budget it consumed, and whether the approach can be reused.
What is your signal that a task has become definite enough for a cheaper executor?
Disclosure: This article was written with AI assistance and reviewed by the author.
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