# GPT-5.4 nano vs Llama 3.3 70B cost > GPT-5.4 nano beats Llama 3.3 70B on cost until output nearly equals input. The crossover sits at 0.85, which almost no workload reaches. URL: https://getculpa.com/gpt-5-4-nano-vs-llama-3-3-70b-versatile-cost Last reviewed: 2026-08-02 ## Answer GPT-5.4 nano costs less than Llama 3.3 70B on Groq until your output volume reaches about 0.85 of your input volume, which almost no real workload does. Culpa, a local-first LLM cost, margin, and forecast ledger, prices both against your own split, and here the crossover sits so high that the answer is settled for most traffic. ## Why this happens Not every crossover is a close call, and reporting one without its position is how a true fact becomes misleading advice. These two cross at 0.85, meaning Llama only wins once you write almost as many tokens as you read. Retrieval sits near 0.05. Chat sits near 0.4. Even a verbose assistant rarely passes 0.6. So the honest summary is that nano wins nearly everywhere on price, and the useful question moves to whether it wins on quality for your task. ## What this usually looks like - A crossover was quoted without saying where it sits relative to real workloads. - Nobody knows whether your ratio is anywhere near the switch point. - An open-weight migration is planned on cost grounds nobody has computed. - The comparison has never been separated into a price question and a quality question. ## Common mistakes - Quoting a crossover without saying where real workloads sit. Why it hurts: A switch point nobody reaches is a fact that reads as a decision and isn't one. Do instead: Put your own ratio beside the crossover. The distance between them is the whole answer. - Choosing the larger model to be safe. Why it hurts: At typical ratios it costs more here, and the size isn't what you're paying for. Do instead: Settle price first, since it's arithmetic, then spend the argument on quality. - Assuming a 70B model must beat a nano model on cost per unit of work. Why it hurts: Price shape decides that, not parameter count, and these two are shaped oppositely. Do instead: Compare the two rates against your ratio and ignore the size labels. ## Self-check - Compute output divided by input for your busiest feature. - Compare it against 0.85. Most workloads land far below. - If you're below, nano is cheaper and the price question is closed. - If you're anywhere near 0.85, price both at your exact volumes rather than trusting the rule. - Then run the quality comparison separately, on your own sample. ## A typical workload, and the rare one that flips it (illustrative) GPT-5.4 nano at real rates of $0.20 per million input and $1.25 output, against Llama 3.3 70B Versatile on Groq at $0.59 and $0.79, both effective 2026-07-02. Volumes are modelled. Crossover: ($0.59 - $0.20) / ($1.25 - $0.79) = 0.85 output tokens per input token Typical workload, 200M input and 50M output, a ratio of 0.25 GPT-5.4 nano: (200 x $0.20) + (50 x $1.25) = $40 + $62.50 = $102.50 Llama 3.3 70B: (200 x $0.59) + (50 x $0.79) = $118 + $39.50 = $157.50 Output-heavy workload, 50M input and 60M output, a ratio of 1.20 GPT-5.4 nano: (50 x $0.20) + (60 x $1.25) = $10 + $75 = $85 Llama 3.3 70B: (50 x $0.59) + (60 x $0.79) = $29.50 + $47.40 = $76.90 Nano is 35% cheaper on the typical workload and only loses once output passes 0.85 of input, which takes a shape most products never generate. The crossover is real and almost nobody reaches it. ## Cost figures Every figure carries its confidence and its source. No figure on this site is provider-reported. - 0.85 — output-to-input ratio at which these two models cost the same [calculated] Source: ($0.59 - $0.20) divided by ($1.25 - $0.79), using real GPT-5.4 nano and Groq Llama 3.3 70B rates per million from the price book, effective 2026-07-02. - $76.90 to $157.50 — modelled monthly cost across two workloads and two models, cheapest to dearest [estimated] Source: The four totals in the teardown arithmetic at real rates. A range because the token volumes are modelled. ## FAQ Q: Is GPT-5.4 nano cheaper than Llama 3.3 70B? A: For almost any real workload, yes. The two cost the same when output reaches about 0.85 of input, and typical retrieval sits near 0.05 while typical chat sits near 0.4. Llama only wins on unusually output-heavy traffic. Q: Does a crossover always mean it's a close call? A: No, and that's the trap. A crossover tells you the switch point exists, not that you're near it. Quote it with your own ratio beside it, because a switch point nobody reaches changes nothing. Q: Then why compare them at all? A: Because settling the price question in one line frees the real argument, which is quality on your specific task. Cost is arithmetic and takes a minute. Quality needs a sample of your own traffic and someone's judgement. ## Sources - OpenAI API pricing: https://developers.openai.com/api/docs/pricing - Groq pricing: https://groq.com/pricing Run the free Cost Leak Scan: https://app.getculpa.com/scan?source=pseo&slug=gpt-5-4-nano-vs-llama-3-3-70b-versatile-cost&cluster=model_compare Machine-readable index of every guide: https://getculpa.com/api/pages Human-readable index of every guide: https://getculpa.com/guides Site overview: https://app.getculpa.com/llms.txt Privacy: Culpa runs on your infrastructure. Your prompts and responses never leave it. Culpa counts calls to run your plan, and it fails open, so if it ever breaks your app keeps running.