DeepSeek vs GLM vs Claude for coding: cost per task
The question “which model should I use for coding?” usually gets answered with a fanwar. For daily work, though, the question is not “which one is best”, but “which model is enough for this task, and what does it cost”.
This article compares three model families commonly used for coding ( DeepSeek, GLM, and Claude) on the side you can compute yourself: the per-million-token price, and a worked example costing the same task on each. On quality, we give you a framework for trying it yourself, not a verdict.
Price per million tokens (catalog reference)
From the Giant Router reference price catalog (public demo catalog snapshot 2026-11-08; prices can change at any time, check the homepage for live numbers):
| Model | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| deepseek-flash | $0.242 | $0.726 |
| glm-5.3 | $1.40 | $4.40 |
| claude-sonnet-5 | $2.00 | $10.00 |
All three can be used through the same API key from Giant Router; that’s one of the reasons to use a gateway: comparing models doesn’t mean managing three accounts and three payments.
Worked example: the same task, three models (illustration)
Assume one medium coding task: refactor one module with several test iterations. Estimated usage shape: 100,000 input tokens, 20,000 output tokens.
Worked example: an illustration, not a real measurement. The token shape is an estimate of typical agentic coding usage; your task’s exact numbers will come out of your own usage report.
| Model | Math | Total | ≈ Rupiah* |
|---|---|---|---|
| deepseek-flash | 0.1M x $0.242 + 0.02M x $0.726 | $0.0387 | Rp640 |
| glm-5.3 | 0.1M x $1.40 + 0.02M x $4.40 | $0.228 | Rp3,760 |
| claude-sonnet-5 | 0.1M x $2.00 + 0.02M x $10.00 | $0.400 | Rp6,600 |
* Display rate Rp16,500/USD, figures rounded.
The pattern to take away from this table matters far more than the numbers:
1. The gap can be 10x for the same task. Claude Sonnet in this example is ten times more expensive than DeepSeek Flash. If your workload is 20 tasks a day, that difference becomes a monthly bill you can feel.
2. Input dominates agentic cost. Agents re-send context on every request, so the input price decides, not the output price that usually gets the spotlight.
3. A model that is “enough” is usually enough. Drafts, test fixes, localized edits, and why-questions rarely need the heaviest model. Big models earn their keep for design decisions, cross-module refactors, or stubborn debugging.
How to choose without dogma (10 minutes)
Instead of trusting anyone’s comparison (including this article), run your own small experiment:
- Pick one real task from your codebase that takes 5-15 minutes.
- Run it on the cheapest model first. Note: how many iterations it took, how many tokens, what it cost (all of it is in the usage report).
- Repeat the same task (or a similar one) on a bigger model.
- Compare: how the quality differs, how the cost differs.
If the cheapest model passes with one extra iteration but costs ten times less, you finally have an answer for your team: “for this kind of task, we use X”. That decision belongs to your own data, not to somebody’s tweet.
One note about quality
We deliberately don’t give “model A 8/10, model B 9/10” scores in this article. Quality comparisons depend heavily on task type, language, and codebase. Most scores on the internet can’t be reproduced. What we can stand behind is the cost side: per-token prices are openly displayed, and the usage report shows exact figures per request, so your experiments can be counted.
If you want a starting point: start with a flash/economy model for 80% of daily tasks, and keep the big model for the 20% that truly needs it. The editor setup is in Cline + your own API key: 5-minute setup.
When to pick which: a practical guide
Without turning this into a verdict, here is the starting point that usually makes sense:
- deepseek-flash: drafts, small refactors, localized edits, repetitive tasks with clear patterns. In the illustrative example, ten times cheaper than sonnet-5; for tasks that don’t need heavy reasoning, the quality gap often doesn’t match the cost gap.
- glm-5.3 (the middle class): code review, implementation from a clear specification, debugging with a clear error trail. The balance point when the economy model often needs many extra iterations.
- claude-sonnet-5: architecture decisions, cross-module refactors, stubborn debugging, or tasks whose price is small next to the value of your time. If one big task happens once a month, the best model is often cheaper than hours of iterating.
And one decision that gets forgotten most often: switching models is cheap. One configuration line in your editor: no account migration, no new payment. Try the cheapest one first for every new task type; if the iterations spread, move up one class.
Summary
- deepseek-flash: economy class, for drafts, small edits, and repetitive tasks.
- glm-5.3: middle class, the balance between capability and price.
- claude-sonnet-5: top class, for tasks that need heavy reasoning.
All three prices are in one catalog, one key, one QRIS payment. Try the same task on the three models and read the cost reports. That’s a comparison nobody can argue with.