GLM 5.3 Flash
- Input / 1M tokens
- $0.04
- Output / 1M tokens
- $0.90
- Providers
- 29
- Uptime, 24 h
- 100%
API models · Side by side
GLM 5.3 Flash vs DeepSeek V4.1 Flash
GLM 5.3 Flash from Z.ai and DeepSeek V4.1 Flash are the two most-used budget models on OpenRouter right now. Both are open-weight, MIT-licensed and can read up to about a million tokens. The differences are in what they accept, how they're built and what it takes to run them yourself.
Prices checked 15 October 2026 from the OpenRouter API.
DeepSeek V4.1 Flash
- Input / 1M tokens
- $0.01
- Output / 1M tokens
- $0.89
- Providers
- 30
- Uptime, 24 h
- 100%
The short answer
Both cost a small fraction of closed models like Claude Sonnet or GPT-6 Sol. Each is served by around 30 providers, so the exact price depends on where you run it: use the live price range and calculator on this page. GLM 5.3 Flash also accepts video; DeepSeek takes text and images only. DeepSeek is built to make long inputs cheap, while GLM's weights are less than half the size, which matters if you host it yourself.
Estimate your monthly bill
Enter your own usage. We apply each model's current rates, including long-prompt pricing, for a 30-day month.
Try:
GLM 5.3 Flash$15.66per month
DeepSeek V4.1 Flash$14.19per month
A token is roughly three-quarters of an English word. Cached input, reasoning tokens, tools and media are not included.
Compare the details
Compare context, supported inputs and usage costs. Highlighted rows show a difference.
| Feature | GLM 5.3 Flash | DeepSeek V4.1 Flash |
|---|---|---|
| Input formats | image, text, video | image, text |
| Output formats | text | text |
| Context / position limit | 1,048,576 | 1,048,576 |
| Maximum output tokens | 943,718 | 943,718 |
| Developer features | frequency penalty, Reasoning output, logit bias, logprobs, Output limit, min p, parallel tool calls, presence penalty, Reasoning controls, Thinking effort, repetition penalty, Response format, Seed, Stop sequences, Structured output, Temperature, Tool selection, Tool calling, top k, top logprobs, Sampling controls | frequency penalty, Reasoning output, logit bias, logprobs, Output limit, min p, presence penalty, Reasoning controls, Thinking effort, repetition penalty, Response format, Seed, Stop sequences, Structured output, Temperature, Tool selection, Tool calling, top k, top logprobs, Sampling controls |
| Cached input / 1M tokens | $0.01 | $0.003600 |
| USD input / 1M tokens | $0.04 | $0.01 |
| USD output / 1M tokens | $0.90 | $0.89 |
| Providers on OpenRouter | 29 | 30 |
| Best provider uptime, last 24 h | 100% | 100% |
| Price range across providers (per 1M tokens) | Input $0.036–$0.22, output $0.15–$1.60 across 29 providers | Input $0.014–$0.45, output $0.18–$1.80 across 30 providers |
| Model identifier | z-ai/glm-5.3-flash | deepseek/deepseek-v4.1-flash |
API pricing
Prices are in USD per million tokens. Input is what you send; output is what the model generates. Claude and ChatGPT subscriptions are billed separately.
Tools, images and additional reasoning can add charges. The examples below cover input and output tokens only.
Compare local modelsWhat those prices mean in practice
Each example uses the same token budget for both models. These are calculations, not measured workloads. Cache discounts, media, tools and extra reasoning are excluded.
| Workload | GLM 5.3 Flash | DeepSeek V4.1 Flash |
|---|---|---|
| 1,000 short requestsPer request: 2,000 input + 500 output tokens | $0.52 | $0.47 |
| 100 document summariesPer request: 20,000 input + 1,000 output tokens | $0.16 | $0.12 |
| 10 long-document requestsPer request: 300,000 input + 2,000 output tokens | $0.13 | $0.06 |
Estimate = requests × (input tokens × input price + output tokens × output price) ÷ 1,000,000. Long-prompt rates are applied where the pricing data provides them. Real requests can use different amounts of output.
Beyond the price tag
Video input is the clearest difference
GLM 5.3 Flash was built to understand video as well as text and images. DeepSeek V4.1 Flash takes text and images only. If your app needs to look at screen recordings or short clips, GLM saves you a separate frame-extraction step. Neither accepts audio or file uploads directly here.
Long inputs are DeepSeek's focus
DeepSeek says V4.1 Flash uses only about 8B active parameters while reading your input and 16B while writing, a design aimed at input-heavy agent work. That's why some providers list very low input prices for it. If your app sends large prompts and gets short answers back, start your tests with DeepSeek.
Running it yourself
Both models can be downloaded for free under the MIT licence. GLM 5.3 Flash has about 320B parameters (18B active per token). DeepSeek V4.1 Flash's published weights total about 763B. Either needs a multi-GPU server, but GLM's smaller size means less memory and fewer GPUs. Our model hardware calculator can size a setup for you.
Which one should you pick?
Choose GLM 5.3 Flash if you need video input, want to self-host on smaller hardware, or rely on parallel tool calls, which OpenRouter lists only for GLM. Choose DeepSeek V4.1 Flash for input-heavy, long-context text and image work. For hosted use, pick a provider first, since price, reply limit and uptime vary, then test both on your own prompts.
Common questions
Which is cheaper?
It depends on the provider and your usage. Both are served by dozens of providers at different prices, and the cheapest one changes often. Check the price range row and try your own numbers in the calculator.
Can I run them on my own server?
Yes. Both are free to download under the MIT licence, but they're large: plan for a multi-GPU server. GLM 5.3 Flash needs considerably less memory than DeepSeek V4.1 Flash.
Does 'Flash' mean they're fast?
It's each company's name for its efficient, lower-cost model. It doesn't promise a particular speed. Response time depends on the provider, your settings and the size of each request.