GLM-5.3 vs DeepSeek V4 Pro pricing.
Both sit far below frontier pricing. The gap between them is still large enough to matter at volume.
- Input gap
- 2.1×
- Output gap
- 2.2×
- Cached gap
- 12×
- Rates verified
- 2026-10-02
Current-model note: Both models are current open-weight releases. DeepSeek rates shown are off-peak, from DeepSeek's official pricing page; DeepSeek charges double during peak hours.
| Current model | Input / 1M | Cached / 1M | Cache write / 1M | Output / 1M | Context |
|---|---|---|---|---|---|
| GLM-5.3Official pricing ↗ | $1.40 | $0.26 | $0.00 | $4.40 | 1,000,000 |
| DeepSeek V4 ProOfficial pricing ↗ | $0.66 | $0.022 | — | $1.98 | 1,000,000 |
Rates verified 2026-10-02. The browser may refresh them from the live catalogue. Provider pricing and tiers remain authoritative.
Rates shown are DeepSeek's off-peak prices. From 01:00 to 04:00 and 06:00 to 10:00 UTC, Monday to Friday (except Chinese public holidays), DeepSeek charges double.
DeepSeek V4 Pro: the public catalogue lists an outdated rate, so this rate comes from the official pricing page.
GLM-5.3: Provider-Calibrated UTF-8 Projection
DeepSeek V4 Pro: Provider-Calibrated UTF-8 Projection
PDF, DOCX, code, text, and data files are extracted locally and applied to both models.
GLM-5.3: unavailable
DeepSeek V4 Pro: unavailable
Prompt text, document contents, and image pixels stay in the browser.
Text, code, PDF, DOCX, images, audio, or video · 12 MB per file
Your input, measured across providers.
Choose a provider card to inspect every available model.
Workload & Scaling Planner +
What five real workloads actually cost
Headline per-token prices rarely decide a bill. Request shape does. These figures are calculated from the verified rates for GLM-5.3 and DeepSeek V4 Pro, including any long-context tier that applies once a request crosses its threshold.
| Workload | Input | Output | GLM-5.3 | DeepSeek V4 Pro | Difference |
|---|---|---|---|---|---|
| Short chat turn A typical assistant exchange. | 1,000 | 500 | $0.0036 | $0.0016 | 2.18x cheaper on DeepSeek V4 Pro |
| RAG answer Five retrieved chunks plus a question. | 12,000 | 800 | $0.020 | $0.0095 | 2.14x cheaper on DeepSeek V4 Pro |
| Code review A medium pull request with surrounding files. | 60,000 | 2,000 | $0.093 | $0.044 | 2.13x cheaper on DeepSeek V4 Pro |
| Whole-document analysis A long report or contract read in one call. | 300,000 | 4,000 | $0.438 | $0.206 | 2.13x cheaper on DeepSeek V4 Pro |
| Full-context load Filling most of a one-million-token window. | 900,000 | 4,000 | $1.28 | $0.602 | 2.12x cheaper on DeepSeek V4 Pro |
"(tier)" marks a request that crossed a long-context threshold, so it is billed above the headline rate. Output length is the assumption most worth changing for your own case: paste a real prompt into the calculator above to replace these with your numbers.
The cached-input rate is the number most comparisons miss
Agents, chat threads and RAG pipelines resend the same system prompt and context on every call. Those repeated tokens bill at the cached rate, not the headline rate, so a model can be cheaper on paper and more expensive in production. Here the gap is 11.8x: DeepSeek V4 Pro reads cached tokens at $0.022 per million.
| Model | Fresh input / 1M | Cached input / 1M | Discount | Break-even |
|---|---|---|---|---|
| GLM-5.3 | $1.40 | $0.260 | 81% | 1 read |
| DeepSeek V4 Pro | $0.660 | $0.022 | 97% | 1 read |
Break-even assumes a cache write costs about 1.25x the base input rate, which is what OpenAI and Anthropic currently document for a short time-to-live. It answers one question: how many times a cached prefix must be re-read before caching is cheaper than paying full price each call. Above that count, every further read saves the discount shown.
Cheap, but not equally cheap
It is easy to treat every open-weight model as interchangeable on price. They are not. Published 2026 comparisons put the spread between the cheapest and most expensive of Kimi K3, Qwen3.8-Max and GLM-5.2 at over $10 per million output tokens, which is more than the entire output price of several models in this class.
Off-peak, DeepSeek V4 Pro lists $0.66 per million input tokens and $1.98 output, about half of GLM-5.3's $1.40 and $4.40. During DeepSeek's peak hours V4 Pro doubles to $1.32 and $3.96, and the two cost almost the same. At a few thousand requests a month that is noise; at a few million, the hour you run in is a budget line.
Both are strong on code, which is where they are usually deployed
This class of model is most often adopted for code assistance, where output volume is high and the task is well specified. Several 2026 comparisons rank GLM, DeepSeek V4 and Qwen closely on coding evaluations.
Code workloads are unusually sensitive to output rate, because a model that writes a whole file emits far more tokens than one answering a question. Set the output slider to a realistic file length rather than a chat-sized answer.
Both are measured by projection here
Neither publishes a browser-runnable tokenizer, so both counts on this page use the same deterministic byte-length method and are labelled as estimates.
Because the same method applies to both, the ratio between them is more reliable than either absolute number. Use it to choose; use provider usage reports to reconcile.
Model the workload, not the marketing price.
Both are cheap enough that quality on your task decides. Use the computed table to size the difference, then evaluate both on held-out data.
Review calculation methodology →Measure against the model you use
Other comparisons worth running
Compare approaches, not just models
Frequently asked questions
Which is cheaper, GLM-5.3 or DeepSeek V4 Pro?+
Off-peak, DeepSeek V4 Pro is about half the price of GLM-5.3 on both input and output. During DeepSeek's peak hours its rates double and the two cost almost the same. The computed table on this page uses the off-peak rate.
Are open-weight models all similarly priced?+
No. Published 2026 comparisons put the spread between the cheapest and most expensive well-known open-weight models at over $10 per million output tokens. Treating the category as uniformly cheap is a common budgeting mistake.
Are these models good for coding?+
This class is most commonly deployed for code assistance and several 2026 comparisons rank them closely on coding evaluations. Code workloads are output-heavy, so set a realistic output length before comparing monthly cost.