Llama 3.3 70B Instruct vs MiniMax M3

Short answer

Cost: MiniMax M3 is cheaper in all three workloads, by 2–66%.
Speed (measured): Llama 3.3 70B Instruct 0.62 s to first token and 98 tok/s; MiniMax M3 1.3 s and 72 tok/s.

Side by side

	Llama 3.3 70B Instruct	MiniMax M3
Served by	Inference APIs	Inference APIs
Model author	Meta	MiniMax
Model id	`meta-llama/Llama-3.3-70B-Instruct-Turbo`	`MiniMaxAI/MiniMax-M3`
Input price	$1.35 / 1M tokens	$0.39 / 1M tokens
Output price	$1.35 / 1M tokens	$1.56 / 1M tokens
Context window	131K tokens	512K tokens
Capabilities	Chat, Tool calling, JSON mode, Multilingual	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	0.62 s	1.3 s
Output speed	98.2 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	Llama 3.3 70B Instruct	MiniMax M3	Difference
Chat assistant — 10,000 turns of 800 tokens in, 300 out	$14.85	$7.80	47%
Long prompts or RAG — 10,000 requests of 8,000 in, 500 out	$115	$39.00	66%
Generation-heavy — 10,000 requests of 500 in, 2,000 out	$33.75	$33.15	2%

MiniMax M3 is cheaper in all three workloads, by 2–66%. 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 Llama 3.3 70B Instruct if

Time to first token matters: 0.62 s against 1.3 s in our measurement
You stream long answers: 98 tokens per second against 72
Your prompts, evals or output formats were tuned on Llama 3.3 70B and you do not want to re-tune
You were on Groq: the llama-3.3-70b-versatile id is accepted unchanged

Choose MiniMax M3 if

You need more than 131K tokens of context (it takes 512K)
Cost is the deciding factor; it is cheaper in every workload above
You want a large-context agent model priced near the Flash tier
You are starting fresh with no Llama-tuned prompts to preserve

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: Llama 3.3 70B Instruct began answering after 0.62 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:

cURL

for MODEL in "meta-llama/Llama-3.3-70B-Instruct-Turbo" "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'
done

FAQ

Which is cheaper, Llama 3.3 70B Instruct or MiniMax M3?

MiniMax M3 is cheaper in all three workloads, by 2–66%. The table above uses list prices: Llama 3.3 70B Instruct $1.35 / 1M tokens input, $1.35 / 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 meta-llama/Llama-3.3-70B-Instruct-Turbo to MiniMaxAI/MiniMax-M3 and nothing else.

Llama 3.3 70B Instruct API MiniMax M3 API Playground

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