GLM 5.3 vs Llama 3.3 70B Instruct

Short answer

Cost: Llama 3.3 70B Instruct is cheaper in all three workloads, by 34–73%.
Speed (measured): GLM 5.3 0.41 s to first token and 171 tok/s; Llama 3.3 70B Instruct 0.62 s and 98 tok/s.

Side by side

	GLM 5.3	Llama 3.3 70B Instruct
Served by	Inference APIs	Inference APIs
Model author	Z.ai	Meta
Model id	`zai-org/GLM-5.3`	`meta-llama/Llama-3.3-70B-Instruct-Turbo`
Input price	$1.82 / 1M tokens	$1.35 / 1M tokens
Output price	$5.72 / 1M tokens	$1.35 / 1M tokens
Context window	1M tokens	131K tokens
Capabilities	Chat, Reasoning, Tool calling, JSON mode	Chat, Tool calling, JSON mode, Multilingual
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	0.62 s
Output speed	170.6 tokens / s	98.2 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	Llama 3.3 70B Instruct	Difference
Chat assistant — 10,000 turns of 800 tokens in, 300 out	$31.72	$14.85	53%
Long prompts or RAG — 10,000 requests of 8,000 in, 500 out	$174	$115	34%
Generation-heavy — 10,000 requests of 500 in, 2,000 out	$124	$33.75	73%

Llama 3.3 70B Instruct is cheaper in all three workloads, by 34–73%. 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 GLM 5.3 if

You need more than 131K tokens of context (it takes 1M)
Time to first token matters: 0.41 s against 0.62 s in our measurement
You stream long answers: 171 tokens per second against 98
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
You are starting fresh with no Llama-tuned prompts to preserve

Choose Llama 3.3 70B Instruct if

Cost is the deciding factor; it is cheaper in every workload above
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
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 Llama 3.3 70B Instruct after 0.62 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 "zai-org/GLM-5.3" "meta-llama/Llama-3.3-70B-Instruct-Turbo"; 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, GLM 5.3 or Llama 3.3 70B Instruct?

Llama 3.3 70B Instruct is cheaper in all three workloads, by 34–73%. The table above uses list prices: GLM 5.3 $1.82 / 1M tokens input, $5.72 / 1M tokens output; Llama 3.3 70B Instruct $1.35 / 1M tokens input, $1.35 / 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 meta-llama/Llama-3.3-70B-Instruct-Turbo 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.

GLM 5.3 API Llama 3.3 70B Instruct API Playground

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