DeepSeek V4.1 Flash vs Llama 3.3 70B Instruct
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: Llama 3.3 70B Instruct is cheaper in all three workloads, by 54–68%.
- Speed (measured): DeepSeek V4.1 Flash 0.5 s to first token and 152 tok/s; Llama 3.3 70B Instruct 0.62 s and 98 tok/s.
Side by side
| DeepSeek V4.1 Flash | Llama 3.3 70B Instruct | |
|---|---|---|
| Served by | Inference APIs | Inference APIs |
| Model author | DeepSeek | Meta |
| Model id | deepseek-ai/DeepSeek-V4.1-Flash | meta-llama/Llama-3.3-70B-Instruct-Turbo |
| Input price | $0.40 / 1M tokens | $0.20 / 1M tokens |
| Output price | $1.60 / 1M tokens | $0.50 / 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 | 0.5 s | 0.62 s |
| Output speed | 151.6 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 | DeepSeek V4.1 Flash | Llama 3.3 70B Instruct | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $8.00 | $3.10 | 61% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $40.00 | $18.50 | 54% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $34.00 | $11.00 | 68% |
Llama 3.3 70B Instruct is cheaper in all three workloads, by 54–68%. 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
- You need more than 131K tokens of context (it takes 1M)
- You stream long answers: 152 tokens per second against 98
- You want a DeepSeek model that answers quickly: in our measurement it thought for a few seconds where V4 Flash thought for twenty, and generated almost three times as fast
- You want the newest DeepSeek Flash model with a 1M-token context
- You are starting fresh with no Llama-tuned prompts to preserve
- 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
- Price matters more than raw output speed
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: DeepSeek V4.1 Flash began answering after 3.45 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:
for MODEL in "deepseek-ai/DeepSeek-V4.1-Flash" "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'
doneFAQ
Which is cheaper, DeepSeek V4.1 Flash or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is cheaper in all three workloads, by 54–68%. The table above uses list prices: DeepSeek V4.1 Flash $0.40 / 1M tokens input, $1.60 / 1M tokens output; Llama 3.3 70B Instruct $0.20 / 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 deepseek-ai/DeepSeek-V4.1-Flash to meta-llama/Llama-3.3-70B-Instruct-Turbo and nothing else.
Why does DeepSeek V4.1 Flash 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.