9 min read · updated 2026-09-10 · reviewed

Baidu Qianfan vs DeepSeek vs GLM: choosing a Chinese model API

Baidu Qianfan, DeepSeek and GLM can all look like inexpensive OpenAI alternatives. The useful choice depends on API compatibility, token accounting, regional access, reliability and whether your intended commercial use is allowed.

Editorial note

Written by Model.sale Engineering and reviewed against first-party documentation on 2026-09-10. Comparisons use the billing rules visible on the linked sources and do not claim universal model equivalence.

The short answer

Choose DeepSeek when you need a focused, documented API and a small model set. Choose GLM when its model capabilities and Chinese-language quality match your workload. Choose Baidu Qianfan when you want a broader managed catalog and are prepared to work with its platform-specific account and billing model.

For a reseller or gateway, the cheapest model is not automatically eligible. Keep a candidate synthetic-only until its published terms, usage reporting and commercial proxy conditions are clear.

Compatibility is more than an endpoint

An OpenAI-compatible path can reduce integration work, but model IDs, tool calling, reasoning fields, streaming events and error schemas still differ. Test /models, JSON responses, SSE event ordering, malformed requests, 401, 429, timeout and cancellation with the exact client you plan to support.

Do not silently substitute a different model when one route fails. Return a controlled error and let the application choose another model explicitly.

Compare billing units before rates

Record whether each platform charges input and output separately, how cached input is treated, whether reasoning tokens are included in output, what minimum charge applies and when balance is deducted. A low per-million headline number is meaningless without those units.

Run a short, input-heavy and output-heavy synthetic workload. Save the reported usage and balance delta, then repeat after 24 hours. If usage is missing or cannot be reconciled, the route is not ready for customer traffic.

Reliability needs a repeatable scorecard

Measure success rate, stream completion, P50/P95 time to first token, total latency, 429 and 5xx frequency, disconnect behavior and usage completeness. Run at least 100 synthetic requests for a new candidate and keep prompts free of customer data.

A route that is cheaper but fails streams or requires frequent retries may cost more in practice. Keep per-model and per-endpoint results rather than assigning one provider-wide health number to every model.

Commercial and data-policy checks

Read the current terms, acceptable-use rules, data-retention statement and any channel-partner or reseller documentation. Confirm that your account type permits the planned proxy or resale. A personal key, browser session or shared account is not a production supply source.

Store the source URL, checked date, account region, model list and redacted validation report. Never put provider secrets, customer prompts or response bodies in a supplier scorecard.

A decision checklist

Before adding a provider, require a written eligibility record: published commercial-use permission, working usage or deterministic billing policy, success at least 98%, stream failure below 1%, 429 below 2%, P95 first-token latency under eight seconds and a projected margin above 50%.

Start with a small self-service balance and synthetic traffic. Keep the current route as the only customer route until the new candidate passes the same checks twice and its policy remains unambiguous.

Sources and further reading

Checked on 2026-09-10. These links support the factual comparisons in this article; pricing and terms may change.

Use the numbers on your own workload

Open the cost calculator, choose a live model and enter a representative input/output mix before deciding.

Open cost calculator
← All guides