AI API costs: DeepSeek vs GLM via router — $0.001 per request vs $50 prepaid cap in 2026

This guide delivers a verify-before-you-commit comparison of DeepSeek vs GLM billing under a single OpenAI-compatible inference router, treating the per-token or per-request unit as something to confirm on each provider's live rate card rather than assume.

It focuses on how request volume burns down a prepaid credit balance under one router, and on verifying the live billing unit and rate card for each exact model before committing credits.

How It Works

The mechanism at play is a split of responsibility: a single OpenAI-compatible inference router accepts one call in an OpenAI-style format, but forwards it to either DeepSeek or GLM based on the model name or routing rule you have set. The router itself does not have to apply the same deduction as the provider. Instead, the provider that actually serves the request applies its own per-request pricing to that request, and that provider-side deduction is what drives prepaid credit burn against your prepaid balance. Because of that split, the label shown by the router is not proof of the charge until you confirm the provider's live billing rule for that exact model.

To verify this path, three key terms must be checked against the live option. First, per-request pricing is the charge rule a provider states for serving a request (the unit it uses to deduct), not a router fee by default. Second, prepaid credit burn is the reduction of a prepaid balance by that provider deduction for that request, so burn is tied to the serving provider's rule. Third, OpenAI-compatible inference router is a gateway that maps OpenAI-style calls to different backends, as noted by Prism | Inference for agents. On the model side, DeepSeek-V3-Base (Hugging Face) describes DeepSeek as a Mixture-of-Experts model, but that architecture alone does not state a universal deduction unit, so the unit must be taken from the provider's current terms.

With that split in mind, the check is to trace the full path for one test call before committing credits: confirm which provider will serve the exact model string under your router, read that provider's live page for the billing unit for that model, and see if the provider states any treatment for cached content. DeepSeek pricing 2026: V4, R1, API costs, and how to optimize (CloudZero) points to caching as something that can affect cost structure, but it does not fix a single unit for every model, so the presence or absence of that treatment must be verified on the live provider page for the option you intend to use.

This section alone explains the mechanism and key terms: the router forwards, the serving provider prices per-request by its own rule, and prepaid credit burn follows that provider rule. Treat "same prompt" as unverified across DeepSeek and GLM until you have confirmed the billing unit and the deduction path for that exact model under that router, because only that confirmation shows what will actually be deducted from prepaid credits.

Key Factors to Consider

This section alone lists the top decision criteria and numbers. The first criterion to lock in is the billable unit. Before any total, confirm whether both DeepSeek and GLM are billed strictly by input tokens, strictly by output tokens, or by a split for the exact model strings your router will send. According to CloudZero, DeepSeek pricing can differ by model family, so the unit for one variant cannot be assumed for another. If the router's metering log uses a different unit than the provider's stated billable unit, that mismatch alone must be resolved before comparing prepaid credit burn.

The second criterion is the live rate card for the exact routed models. Listings from Puter and LMMarketcap show DeepSeek rates exist for specific models, but the provided grounding does not enumerate a complete GLM rate card. For that reason, the check is not "which brand is lower", but pulling the current rate card for the exact GLM model name at the moment of verification from its own source. Only after both exact names are locked can any comparison be considered like-for-like.

The third criterion is the prepaid credit terms. A rate alone does not dictate burn if the terms define what draws from the prepaid balance. Verify the credit consumption rule for each exact model against the billable unit from the first criterion. If the rule states credits apply to a different unit than what is billed, treat that as a term-based factor to test with a small sample, not something to guess. This is the only way to know if credits will track the per-request math.

As for the numbers that matter, limit to three per candidate route: the input rate, the output rate, and the stated credit consumption rule for that exact model. If any one of these three is missing from the live card at check time, the correct action is to withhold commitment. Do not substitute a sibling model's figures to fill the gap, and do not average across families. These are the only numbers to lock in for a prepaid comparison under a single OpenAI-compatible inference router.

Common Mistakes

Pitfall 1: Treating the phrase "per request" as if it means the same thing for both models. For a concrete example, take the exact same OpenAI-style call and route it to DeepSeek under one model name, then route that same call to GLM under another model name on your single OpenAI-compatible inference router. If you only compare the "per request" wording on a summary page, you can easily miss that the billable unit itself may be labeled differently between the two. As pointed out by CloudZero, the billable unit must be confirmed before any total, so for this pair you must read the live charge line for each model name on your router's exact price sheet, not a general banner.

Pitfall 2: Extrapolating from a single test call. A common slip is to run one manual test with a repeated system prompt, see fewer prepaid credits deducted, and assume that ratio will hold for every call going forward. The issue is that the presence of a "cache hit" vs "cache miss" label is not guaranteed to appear the same way for DeepSeek and GLM on every router build. According to CloudZero, caching actually works, but how it is itemized can differ by setup. Because of that, you must check the invoice line item names for both models on your exact build, and if either one does not show those line names at all, do not guess at the split. Treat that missing label as a block to verify before committing any prepaid credits to either path.

Insider Tactics

One non-obvious strategy is to treat your router's model list as a live menu, not a fixed preference. Instead of assigning every call to the same model name, run a very small test batch of the exact same prompt through your single OpenAI-compatible inference router against both DeepSeek and GLM. For that batch alone, pull the billable line items straight from your router's usage view at the moment of the test. Do not copy a saved screenshot, as the line items shown are the only thing that can prove what will draw from your prepaid credits.

When running that test, do not stop at the model name. Force a check for the unit label next to each call in that batch. With DeepSeek alone, sources such as Puter note different model families like V4-Flash and V4-Pro, so the unit shown for one variant may not be the same as another. GLM may show a different unit label entirely. The goal of this strategy is to match by unit label, not by the family name, before scaling to a larger batch.

For a timing tip, do not assume the rate card you just looked at will stay the same for your next batch. As noted by LMMarketcap, DeepSeek API pricing can be updated without a broad announcement. This does not mean to wait for a specific moment, it means to re-check the live rate card at the exact moment before committing any batch larger than your test batch. If you cannot pull a live rate card at that moment, do not commit the larger batch.

Another timing tip is to split any batch you intend to run into at least two parts with a live check between them. Run part one, confirm the deduction and unit label from your router's usage view, then decide on part two based off of that fresh pull. This keeps your prepaid credits from being drawn all at once off of an option you only verified for a smaller amount.

Finally, keep a bare log for these checks: the exact model name used, the unit label shown, and the deduction shown for each test batch. This log is not for comparison charts, but to catch any shift between the option you intended to use and the option that actually drew from your prepaid credits before you commit more of your balance.

Comparison

This section alone compares options side by side with a winner. To do a like-for-like comparison under a single OpenAI-compatible inference router, the first check is to pull the live billable tables for the exact model names you intend to route at the moment of testing, not from an old screenshot. According to developer.puter.com, DeepSeek lists V4.1 Flash and V4 Pro with peak and off-peak rates, while avian.io lists GLM-5.1 among models billed by tokens used. Since neither of those sources provides a single fixed set of digits to copy here, the comparison must be built directly from those live tables for both options before any prepaid credit commitment.

For the side-by-side, run two identical test calls with the same prompt and same max output, one routed to DeepSeek and one routed to GLM. From the router response, record input tokens and output tokens for each call separately. As noted by lmmarketcap.com, DeepSeek costs are compared per million tokens against others, so convert both models to the same per-million-token basis for input and for output. Do not collapse them into one "per request" figure until both bases are known, and recompute the sum for each model from those converted values before moving to a decision.

With those sums, the winner is determined by traffic split, not by model name. If the majority of your test traffic is output heavy, the model with the lower output per-million-token rate on its live table can win for that burn, even if its input rate is higher. If the majority is input heavy, the opposite can win. This is a comparison of totals, not labels, and it must be done against your own token split from the test calls, not an assumed split.

Before locking in, verify the router's accounting for both routes. Confirm the router is charging against the exact token counts it reports for each model, and that both models remain available under your prepaid credit pool at the time of the test. Avian.io states pay only for the tokens you use, but availability is not guaranteed to be identical between the two routes. If either live table is missing any part needed to compute input and output separately, or if the router cannot report counts for a route, that route cannot be fairly compared and must not be chosen by default.

Based strictly on verifying the live, complete option before committing, the winner for your prepaid credit burn is the single model with the smaller computed total for your measured input and output split from the full live tables. If both full tables cannot be obtained at commit time, the correct comparison result is to not commit to a fixed split until they can be obtained, since a method-only comparison with complete data outranks any partial comparison.

Frequently Asked Questions

Will the router's label guarantee the $0.001 per request charge for DeepSeek?

No, the label shown by the router is not proof of the charge until you confirm the provider's live billing rule for that exact model.

What actually causes prepaid credit burn against a $50 prepaid cap when using GLM in 2026?

The provider that actually serves the request applies its own per-request pricing, and that provider-side deduction is what drives prepaid credit burn against your prepaid balance.

Can the OpenAI-compatible inference router apply its own deduction instead of the provider?

The router itself does not have to apply the same deduction as the provider.

Before committing credits, what must be verified for each exact model?

You must verify the live billing unit and rate card for each exact model before committing credits.

Is per-request pricing considered a router fee by default?

Per-request pricing is the charge rule a provider states for serving a request, not a router fee by default.

Why can't I assume a model is billed per-request just because it's routed through the same interface?

You must treat the per-token or per-request unit as something to confirm on each provider's live rate card rather than assume.

Quick answers

Also worth reading: Keep AI costs under control: $0.00114/M per-route cap keeps GLM-4.5-Air under $50 in 2026: Keep AI costs under control: · Prepaid AI API Spend: Cap DeepSeek and GLM Costs at $50/Month in 2026: Prepaid AI API Spend: Cap