GLM 5.3 Flash
- Input / 1M tokens
- $0.04
- Output / 1M tokens
- $0.08
- Providers
- 37
- Uptime, 24 h
- 100%
API models · Side by side
GLM 5.3 Flash vs Kimi K3
GLM 5.3 Flash and Kimi K3 both accept text, images and video through the compared APIs. Both also have released weights, giving teams a choice between renting access and operating their own deployment. GLM’s lower token rates make the budget difference clear; deciding whether Kimi is worth more requires a closer look at your actual work.
Kimi K3
- Input / 1M tokens
- $0.19
- Output / 1M tokens
- $16.00
- Providers
- 18
- Uptime, 24 h
- 100%
The short answer
Start with GLM when API cost is the main constraint and it completes the task acceptably. Consider Kimi where its answers save enough editing or retries to justify the extra spending. For self-hosting, compare the exact checkpoints, runtime support and release terms separately from API prices.
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$3.57per month
Kimi K3$251.52per 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 | Kimi K3 |
|---|---|---|
| Input formats | image, text, video | image, text, video |
| Output formats | text | text |
| Context / position limit | 1,048,576 | 1,048,576 |
| Endpoint output limit (tokens) | 131,072 | 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 | $0.03 | $0.18 |
| USD input / 1M tokens | $0.04 | $0.19 |
| USD output / 1M tokens | $0.08 | $16.00 |
| Available API providers | 37 | 18 |
| Best provider uptime, last 24 h | 100% | 100% |
| Price range across providers (per 1M tokens) | Input $0.040–$0.22, output $0.078–$0.75 across 37 providers | Input $0.18–$4.50, output $9.50–$22.50 across 18 providers |
| Model identifier | z-ai/glm-5.3-flash | moonshotai/kimi-k3 |
API pricing
Prices are in USD per million tokens. Input is what you send; output is what the model generates. Chat subscriptions and API usage 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 | Kimi K3 |
|---|---|---|
| 1,000 short requestsPer request: 2,000 input + 500 output tokens | $0.12 | $8.38 |
| 100 document summariesPer request: 20,000 input + 1,000 output tokens | $0.09 | $1.98 |
| 10 long-document requestsPer request: 300,000 input + 2,000 output tokens | $0.12 | $0.90 |
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
The price gap grows with generated output
The API rates here are lower for GLM on both input and output. For 1,000 requests containing 2,000 input tokens and 500 output tokens each, GLM costs about $0.55 and Kimi about $10.30. These are batch totals for identical token quantities, excluding tools and extra reasoning. A report-writing feature should pay particular attention to the output rate because a short instruction can generate a lengthy, more expensive answer.
The same formats make a direct comparison easier
Both endpoints accept text, images and video and return text. You can therefore compare the same screenshot or recording without first converting it for only one model. Keep the prompt and required answer format consistent. For a support workflow, useful checks include whether the model notices the relevant error and whether its suggested next step follows the evidence in the recording.
Similar context allowances do not settle the decision
Both hosting endpoints offer 1,048,576 tokens of context. That removes one capacity difference for a long document collection, but it does not show how reliably either model finds the right passage. Include questions whose answers depend on details in different parts of the material. Check whether an answer is supported, not merely whether the whole upload was accepted.
Reasoning defaults deserve a deliberate choice
GLM’s model instructions support low, high and max reasoning effort, with max as the default. For a simple extraction task, allowing the largest thinking budget may spend more than the result warrants. Set the option explicitly where your service supports it, then check that shorter reasoning still completes the job. Do not assume an identically named setting gives Kimi the same budget or behaviour.
Self-hosting is a different cost comparison
GLM has 320 billion total parameters and 18 billion active parameters. Kimi has 2.8 trillion total parameters and 104 billion active parameters. Active counts concern the computation used for a token; they do not describe the full memory a deployment must hold. Compare the actual precision, model files and serving configuration before choosing GPUs. A lower API bill does not calculate a self-hosted server bill.
Compare the cost of an accepted result
A classification feature can check valid fields and correct labels; a coding agent can check whether a change meets its requirements. Count retries and human corrections alongside token spending. That gives Kimi a fair chance to justify its higher rate without treating its size as proof of better results, and gives GLM a clear standard beyond being inexpensive.
Common questions
Do GLM and Kimi have the same release terms?
No. GLM 5.3 Flash uses the MIT license, while Kimi K3 uses its own Kimi K3 license. Check the exact release terms when planning to distribute or operate the weights.
Does the output ceiling guarantee a huge answer?
No. The table shows hosting endpoint limits, not a promise of a particular reply length. Context, generation settings and the serving configuration still constrain a request.