GLM or DeepSeek via API: comparing price, context and strength index

Compare GLM API pricing vs DeepSeek by the price list and the picture is not obvious: GLM has one price for input and output, while DeepSeek charges noticeably more for output than for input. As a result the winner depends on the shape of the task, not on general cheapness. Below are Zukko prices as of October 18, 2026, a calculation for two kinds of workload, and the limits worth knowing up front: GLM's 128K window versus DeepSeek's 1M.

GLM API pricing vs DeepSeek: what we compare

The catalog has two models per family. Zukko prices, $ per 1M tokens, cache billed separately. "Official" is the vendor's price from the same table.

The strength index is the Artificial Analysis Intelligence Index. It helps weed out clearly unsuitable options but does not replace a test on your data. Note that GLM has the higher index in this table, DeepSeek has a window almost eight times larger, and deepseek-v4-pro accepts image input.

For completeness, here is Qwen, since it is often asked about alongside DeepSeek: qwen3.8-flash costs 0.038 / 0.075 with a 1M window and index 40, and qwen3.8-max costs 0.088 / 0.238, 1M window, images yes, index 45. A separate Qwen versus GLM comparison is in the article Qwen versus GLM↗.

Cache for these models

The table shows that the cache-read price for GLM and DeepSeek equals the input price. So, unlike Claude, the calculation is simpler: count all input at the input price, and cache does not change the total. They have no separate cache-write charge. Whether cache reads are recorded depends on whether the vendor reports them in the response. The cache rules are described in the docs on key limits and cache↗.

Scenario 1. Lots of output text

Generating descriptions, rewriting, chat replies: 1,000 requests with 1,000 input and 1,000 output tokens each. Input is 1M, output 1M.

Among the larger models glm-5.3 is cheaper, because its output costs less than half of deepseek-v4-pro's. Among the smaller ones deepseek-v4.1-flash is cheaper.

Scenario 2. Lots of input text

Classification, summarization, questions over a document: 1,000 requests with 50,000 input and 300 output tokens each. Input is 50M, output 0.3M.

Here the picture reverses: deepseek-v4-pro is cheaper than glm-5.3, and deepseek-v4.1-flash costs about a third of glm-5.3-flash (0.66 versus 2.01 in the calculation above).

Where the line falls

For the glm-5.3 and deepseek-v4-pro pair you can find the point where prices meet. Let I be input tokens and O output tokens, in millions. GLM costs I · 0.1 + O · 0.1, DeepSeek I · 0.075 + O · 0.225. Setting them equal: GLM is more expensive on input by 0.1 − 0.075 = 0.025 per million and cheaper on output by 0.225 − 0.1 = 0.125 per million. The prices meet when I × 0.025 = O × 0.125, so I = 5 × O. As a rule of thumb: if input exceeds output by more than five times, deepseek-v4-pro is cheaper; if less, glm-5.3 is cheaper. Check: at I = 5 and O = 1 both models cost $0.60.

The smaller models have no crossover: deepseek-v4.1-flash input (0.013) and output (0.038) are both below glm-5.3-flash (0.04 and 0.04), so by price list it is cheaper at any ratio. But "cheaper on the price list" does not mean "suitable": GLM Flash has index 42, DeepSeek V4.1 Flash 39.

Context: 128K versus 1M

This limit matters more than the cents. If a request (documents, chat history, agent files) does not fit into 128K tokens, GLM will simply not accept it, and the choice is made for you. Agent sessions grow fast because the whole accumulated history is sent at each step. If you are not sure how many tokens you have, count them in advance with count_tokens↗ and leave headroom.

For images, only deepseek-v4-pro of the four works. The others take text only.

How to choose in an hour

1. Collect 20-30 real requests and expected answers.
2. Run them through two or three models from the table.
3. Compute cost from the real usage in responses, not from estimates.
4. Pick the cheapest model among those that pass your criteria.

An example of switching between models with one parameter (OpenAI format):

from openai import OpenAI

client = OpenAI(base_url="https://api.zukko.pro/v1", api_key="fetch_your_key")

for model in ["glm-5.3-flash", "deepseek-v4.1-flash", "glm-5.3", "deepseek-v4-pro"]:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Pick out three key ideas: ..."}],
        max_tokens=300,
    )
    print(model, r.usage.prompt_tokens, r.usage.completion_tokens)

More on the smaller DeepSeek model: DeepSeek V4.1 Flash via API↗. The current list and prices: GET https://api.zukko.pro/v1/models or the "Models" page in the dashboard.

FAQ

Which is cheaper, GLM or DeepSeek?

It depends on the input-to-output ratio. Among the larger models glm-5.3 is cheaper when output is more than a fifth of input, and deepseek-v4-pro is cheaper with large input and a short answer. Among the smaller ones deepseek-v4.1-flash is cheaper.

Qwen API vs DeepSeek: which is the better deal?

By price list deepseek-v4.1-flash (0.013 / 0.038) is cheaper than qwen3.8-flash (0.038 / 0.075). qwen3.8-flash has index 40 versus 39, and qwen3.8-max has 45 versus 36 for deepseek-v4-pro. Choose by running your own tasks.

Is GLM's 128K window enough?

For short tasks (chat, classification, editing a small file), yes. For long documents and long agent sessions, take a model with a 1M window.

Can I pay for these models from Russia?

Yes: access from Russia goes through the gateway, with payment by SBP (the payment page adds an 8.5% fee on top), MasterCard or crypto. Top-ups start at $1.

To compare the models on your own data, create a key in the dashboard↗: all four models are available on one balance.