GLM 5.3 vs MiniMax M3
Two open-weight chat models on the same endpoint and API key, so switching between them is a one-word change. Below: what each costs for three realistic workloads, how they differ, and measured speed.
Inference APIs prices are read live from the price list.
- Cost: MiniMax M3 is cheaper in all three workloads, by 73–78%.
- Speed (measured): GLM 5.3 0.41 s to first token and 171 tok/s; MiniMax M3 1.3 s and 72 tok/s.
Side by side
| GLM 5.3 | MiniMax M3 | |
|---|---|---|
| Served by | Inference APIs | Inference APIs |
| Model author | Z.ai | MiniMax |
| Model id | zai-org/GLM-5.3 | MiniMaxAI/MiniMax-M3 |
| Input price | $1.82 / 1M tokens | $0.39 / 1M tokens |
| Output price | $5.72 / 1M tokens | $1.56 / 1M tokens |
| Context window | 1M tokens | 512K tokens |
| Capabilities | Chat, Reasoning, Tool calling, JSON mode | Chat, Reasoning, Tool calling, JSON mode |
| Weights | Open | Open |
| Status | Available | Available |
| Limits | No per-minute or per-day request or token caps; usage draws on a prepaid balance | No per-minute or per-day request or token caps; usage draws on a prepaid balance |
| Time to first token | 0.41 s | 1.3 s |
| Output speed | 170.6 tokens / s | 72 tokens / s |
What three workloads cost
List prices applied to the same work. The cheaper side of each row is in bold.
| Workload | GLM 5.3 | MiniMax M3 | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $31.72 | $7.80 | 75% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $174 | $39.00 | 78% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $124 | $33.15 | 73% |
MiniMax M3 is cheaper in all three workloads, by 73–78%. Reasoning models bill their thinking as output tokens, so real output counts run higher than the visible answer; treat the generation-heavy row as a floor for them.
When to choose which
- You need more than 512K tokens of context (it takes 1M)
- Time to first token matters: 0.41 s against 1.3 s in our measurement
- You stream long answers: 171 tokens per second against 72
- Coding agents and long multi-step tasks where GLM 5.3 Flash runs out of depth
- You want a large open-weight model with a 1M-token context, hosted in the United States
- Cost is the deciding factor; it is cheaper in every workload above
- You want a large-context agent model priced near the Flash tier
- High-volume or latency-sensitive work; GLM 5.3 Flash costs about a ninth as much
Price, context and speed are measurable; answer quality on your task is not something a table can settle. Both take the same request, so the honest test is to run your own prompts through each in the playground.
Measured speed
Medians of three streamed runs on 2026-09-17, public endpoint, a prompt that asks for about 120 words. Time to first token is the first token of any kind, reasoning included; output speed counts every generated token from that point. Reasoning models then think before the visible answer starts: GLM 5.3 began answering after 6.22 s and MiniMax M3 after 2.74 s at default settings, which you can shorten with the reasoning controls on each model page. Expect ±30% with time of day. Method and raw numbers: speed measurements.
Trying both
Same endpoint, same key. Change one string:
for MODEL in "zai-org/GLM-5.3" "MiniMaxAI/MiniMax-M3"; do
curl -s https://api.inferenceapis.com/v1/chat/completions \
-H "Authorization: Bearer $INFERENCE_API_KEY" -H "Content-Type: application/json" \
-d "{\"model\": \"$MODEL\", \"max_tokens\": 500, \"messages\": [{\"role\": \"user\", \"content\": \"Summarise the plot of Hamlet in two sentences.\"}]}" \
| jq -r '.model, .choices[0].message.content, .usage'
doneFAQ
Which is cheaper, GLM 5.3 or MiniMax M3?
MiniMax M3 is cheaper in all three workloads, by 73–78%. The table above uses list prices: GLM 5.3 $1.82 / 1M tokens input, $5.72 / 1M tokens output; MiniMax M3 $0.39 / 1M tokens input, $1.56 / 1M tokens output.
How do I switch between them?
They are on the same endpoint and key. Change the model field from zai-org/GLM-5.3 to MiniMaxAI/MiniMax-M3 and nothing else.
Why does GLM 5.3 use more output tokens than the visible answer?
It is a reasoning model: it thinks before it answers and the thinking is billed as output tokens. Budget for that in output-heavy workloads, and set max_tokens to a few hundred or more so the answer is not cut off.
Spotted a price or limit that has changed? Tell us and we will re-check the provider page.