Llama 3.3 70B Instruct vs Qwen3-VL 235B

Short answer

Cost: Qwen3-VL 235B is cheaper in all three workloads, by 28–77%.
Speed (measured): Llama 3.3 70B Instruct 0.62 s to first token and 98 tok/s; Qwen3-VL 235B 1.28 s and 11 tok/s.

Side by side

	Llama 3.3 70B Instruct	Qwen3-VL 235B
Served by	Inference APIs	Inference APIs
Model author	Meta	Alibaba Qwen
Model id	`meta-llama/Llama-3.3-70B-Instruct-Turbo`	`Qwen/Qwen3-VL-235B-A22B-Instruct`
Input price	$1.35 / 1M tokens	$0.26 / 1M tokens
Output price	$1.35 / 1M tokens	$1.15 / 1M tokens
Context window	131K tokens	262K tokens
Capabilities	Chat, Tool calling, JSON mode, Multilingual	Vision, OCR, Chat, 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	1.28 s
Output speed	98.2 tokens / s	10.7 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	Qwen3-VL 235B	Difference
Chat assistant — 10,000 turns of 800 tokens in, 300 out	$14.85	$5.53	63%
Long prompts or RAG — 10,000 requests of 8,000 in, 500 out	$115	$26.55	77%
Generation-heavy — 10,000 requests of 500 in, 2,000 out	$33.75	$24.30	28%

Qwen3-VL 235B is cheaper in all three workloads, by 28–77%.

When to choose which

Choose Llama 3.3 70B Instruct if

Time to first token matters: 0.62 s against 1.28 s in our measurement
You stream long answers: 98 tokens per second against 11
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
Text-only work; the text models here are cheaper

Choose Qwen3-VL 235B if

You need more than 131K tokens of context (it takes 262K)
You need to send images; Llama 3.3 70B Instruct is text-only
Cost is the deciding factor; it is cheaper in every workload above
Reading screenshots, invoices, receipts and scanned pages into text or JSON
Agents that need to look at an image and then call a tool
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 Qwen3-VL 235B after 1.28 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" "Qwen/Qwen3-VL-235B-A22B-Instruct"; 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 Qwen3-VL 235B?

Qwen3-VL 235B is cheaper in all three workloads, by 28–77%. The table above uses list prices: Llama 3.3 70B Instruct $1.35 / 1M tokens input, $1.35 / 1M tokens output; Qwen3-VL 235B $0.26 / 1M tokens input, $1.15 / 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 Qwen/Qwen3-VL-235B-A22B-Instruct and nothing else.

Llama 3.3 70B Instruct API Qwen3-VL 235B API Playground

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