MiniMax M3 vs Qwen3.8 Flash

Short answer

Cost: Qwen3.8 Flash is cheaper in all three workloads, by 63–68%.

Side by side

	MiniMax M3	Qwen3.8 Flash
Served by	Inference APIs	Inference APIs
Model author	MiniMax	Alibaba Qwen
Model id	`MiniMaxAI/MiniMax-M3`	`Qwen/Qwen3.8-Flash`
Input price	$0.39 / 1M tokens	$0.15 / 1M tokens
Output price	$1.56 / 1M tokens	$0.50 / 1M tokens
Context window	512K tokens	1M tokens
Capabilities	Chat, Reasoning, Tool calling, JSON mode	Chat, Reasoning, Tool calling, JSON mode, Cached input
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	1.3 s	—
Output speed	72 tokens / s	—

What three workloads cost

List prices applied to the same work. The cheaper side of each row is in bold.

Workload	MiniMax M3	Qwen3.8 Flash	Difference
Chat assistant — 10,000 turns of 800 tokens in, 300 out	$7.80	$2.70	65%
Long prompts or RAG — 10,000 requests of 8,000 in, 500 out	$39.00	$14.50	63%
Generation-heavy — 10,000 requests of 500 in, 2,000 out	$33.15	$10.75	68%

Qwen3.8 Flash is cheaper in all three workloads, by 63–68%. 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

Choose MiniMax M3 if

You want a large-context agent model priced near the Flash tier
You need vision; use Qwen3-VL 235B
You need the strongest coding model; GLM 5.3 and DeepSeek V4 Pro rank higher

Choose Qwen3.8 Flash if

You need more than 512K tokens of context (it takes 1M)
Cost is the deciding factor; it is cheaper in every workload above
You want a fast, inexpensive model with a 1M-token window and prompt caching
Long-document summarisation and extraction where the whole file goes in one request

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: MiniMax M3 began answering 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:

cURL

for MODEL in "MiniMaxAI/MiniMax-M3" "Qwen/Qwen3.8-Flash"; 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'
done

FAQ

Which is cheaper, MiniMax M3 or Qwen3.8 Flash?

Qwen3.8 Flash is cheaper in all three workloads, by 63–68%. The table above uses list prices: MiniMax M3 $0.39 / 1M tokens input, $1.56 / 1M tokens output; Qwen3.8 Flash $0.15 / 1M tokens input, $0.50 / 1M tokens output.

How do I switch between them?

They are on the same endpoint and key. Change the model field from MiniMaxAI/MiniMax-M3 to Qwen/Qwen3.8-Flash and nothing else.

Why does MiniMax M3 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.

MiniMax M3 API Qwen3.8 Flash API Playground

Spotted a price or limit that has changed? Tell us and we will re-check the provider page.