GLM 5.3 vs DeepSeek V4 Pro: Results, Time, and Task Cost
Compare task completion, elapsed time, and cost per accepted result—and account for the planned retirement of DeepSeek V4 Pro.
Contents
Start by testing GLM 5.3 when an additional accepted result on a difficult task could justify higher spending. DeepSeek V4 Pro-0813 is worth comparing when the cost of repeated runs matters most. In one published evaluation, GLM completed more tasks and had a lower median time among successful runs; Pro cost less. That does not establish a universal winner.
There is an imminent availability constraint. DeepSeek plans to retire V4 Pro on September 14, 2026, at 12:00 Beijing time: 04:00 UTC, or 07:00 Moscow time. A notice in the official platform, visible after sign-in, says Pro requests will be routed to V4.1 Flash and billed at its rates. Do not build a new long-term official API integration on the assumption that the old name will keep serving Pro-0813.
This comparison concerns GLM 5.3 and Pro-0813, not GLM 5.3 Flash or V4.1 Flash. Historical Pro results can serve as a replacement baseline; they cannot describe the model answering behind that name after retirement.
What the 30-task evaluation shows
On August 27, Composio published a five-model comparison covering 30 multi-step agent tasks. Here is the relevant pair:
| Composio metric | GLM 5.3 | DeepSeek V4 Pro-0813 |
|---|---|---|
| Tasks completed | 22 of 30 | 19 of 30 |
| Total cost for all 30 tasks | $5.31 | $1.23 |
| Reported cost per successful task | $0.24 | $0.065 |
| Median time among completed tasks | 2m 54s | 4m 41s |
| Timeouts | 1 | 3 |
Primary results: completion, total cost, cost per success, time, and timeouts.
GLM completed three more tasks in this sample. Pro’s cost per success was about 3.7 times lower: $0.24 / $0.065. These are historical evaluation expenses, excluding human rework, not current API prices.
The time medians include only successful tasks, and the successful task sets differ. They do not show that GLM is faster on every matched task or that an entire work session will finish proportionally sooner. Failed attempts and timeouts consume time too.
This is Composio’s evaluation, not our own test. Without complete settings, the execution environment, and repeated runs, 22 versus 19 cannot reliably predict success on your project. Multi-step work across external tools also differs from fixing a repository bug.
Coding benchmarks are more mixed
The official GLM 5.3 model card includes Pro-0813 results:
| Test in Z.ai’s table | GLM 5.3 | Pro-0813 |
|---|---|---|
| Terminal Bench 2.1 | 88.2 | 87.9 |
| DeepSWE v1.1 | 66.9 | 62.7 |
| NL2Repo | 58.0 | 61.1 |
| Toolathlon Verified | 73.0 | 74.1 |
GLM leads the first two rows; Pro leads the next two. These are Z.ai’s published results under the conditions explained in the card, not a substitute for independently testing both models in your client. Different tasks and scoring methods cannot be added into one quality percentage.
Defaults differ too. GLM 5.3 defaults to reasoning_effort = max. The current DeepSeek V4 guide defaults to thinking enabled with high effort. Two unconfigured runs therefore use different modes; matching effort names across models would not prove equal compute budgets either.
Use the published conditions when reproducing a benchmark. When choosing a working model, instead define your task’s acceptable cost and time, then test suitable settings for each model within those limits.
Count every attempt before acceptance
Define success first. A bug fix might require the previously failing test to pass without regressions. An agent touching several systems may need to leave every system in the correct final state without extra records or repeated actions.
cost_per_accepted_task = total_api_cost_of_all_attempts / accepted_tasksInclude unsuccessful attempts, retries, and fallback-model calls in the numerator. With no accepted tasks, the metric is undefined: record zero successes and the full expense, not zero cost per success.
Track human checking and repair time separately. If converting it to money, use your own hourly cost and report it separately from API spending. A lower model bill can be outweighed by lengthy rework; measure that on your tasks.
In the BetterToken Dashboard, you can check the model, request time, input, output and cached tokens, and corresponding expense. Task acceptance and human effort require separate records. A logged model name is not independent proof of the weights an external provider served.
Choose using your own workload
Take a small set of recurring tasks. Give both models the same project starting state, instructions, tool permissions, and acceptance criteria. Use test data for external actions so repeated runs do not create real duplicates.
Record the model and client versions, provider, reasoning mode, timeout, and retry limit. Keep the expense and outcome of every run, including failures. Compare which specific tasks each model solved, not just the total bill.
- Complex changes with costly human repair: test GLM 5.3 first and see whether its advantages in some evaluations translate into more accepted changes for you.
- Well-defined tasks under a strict budget: historical Pro-0813 results illustrate why cost per success matters. You can compare it with your current model before retirement; continued work needs a confirmed available option.
- Long multi-tool processes: cap time for the whole task, including retries. Successful-run medians omit time lost on unfinished work.
After September 14, a new official deepseek-v4-pro request cannot automatically be treated as a Pro-0813 test. Verify routing first. If V4.1 Flash answers, that is a new comparison against GLM 5.3 and requires new results. Check third-party availability and timing separately.
