# Gemini 2.5 Flash vs Llama 3.3 70B cost > Gemini 2.5 Flash wins on retrieval work and Llama 3.3 70B wins on chat. They cross at an output-to-input ratio of 0.17. Worked math inside. URL: https://getculpa.com/gemini-2-5-flash-vs-llama-3-3-70b-versatile-cost Last reviewed: 2026-08-02 ## Answer Gemini 2.5 Flash costs less on input and far more on output than Llama 3.3 70B on Groq, so they cross at an output-to-input ratio of about 0.17. Retrieval workloads sit below that line and chat workloads above it. Culpa, a local-first LLM cost, margin, and forecast ledger, prices both against your own split so the choice stops being a preference. ## Why this happens This is the hosted-versus-open-weight question, and it's usually argued on quality or principle when the cost half has an exact answer. The two price shapes are opposites. Gemini 2.5 Flash is cheap on input and charges eight times that on output. Llama 3.3 70B is dearer on input and charges only about a third more on output, the flattest ratio in this price book. So one is built for reading and the other for writing, and the crossover between them lands low enough that plenty of real workloads sit on each side. ## What this usually looks like - The open-weight versus hosted choice is argued on principle with no arithmetic attached. - One model serves both your retrieval and your chat features. - Nobody has computed the output share of your token volume. - A migration was justified on rate-card headlines rather than on your mix. ## Common mistakes - Comparing two models on the input rate alone. Why it hurts: These two differ far more on output, and output is where one of them is eight times the other. Do instead: Compare on both rates weighted by your own volumes, which is what the crossover does. - Treating open weights as automatically cheaper. Why it hurts: Below a 0.17 ratio the hosted model is cheaper here, and retrieval workloads sit well below it. Do instead: Let the ratio decide. It's a measurement, not a preference. - Applying one model to features with opposite shapes. Why it hurts: A single choice is wrong for whichever features sit on the far side of the crossover. Do instead: Route by ratio. Reading work one way, writing work the other. ## Self-check - Split last month's tokens into input and output for each feature. - Divide output by input per feature and write the ratio down. - Anything below 0.17 is cheaper on Gemini 2.5 Flash. - Anything above 0.17 is cheaper on Llama 3.3 70B. - Count how many features sit on each side before choosing one model for all of them. ## Retrieval and chat, priced on both models (illustrative) Gemini 2.5 Flash at real rates of $0.30 per million input and $2.50 output, against Llama 3.3 70B Versatile on Groq at $0.59 and $0.79, both effective 2026-07-02 and the Groq rates re-verified 2026-08-01. The workloads are modelled. Crossover: ($0.30 - $0.59) / ($0.79 - $2.50) = 0.17 output tokens per input token Retrieval workload, 400M input and 20M output, a ratio of 0.05 Gemini 2.5 Flash: (400 x $0.30) + (20 x $2.50) = $120 + $50 = $170 Llama 3.3 70B: (400 x $0.59) + (20 x $0.79) = $236 + $15.80 = $251.80 Chat workload, 100M input and 40M output, a ratio of 0.40 Gemini 2.5 Flash: (100 x $0.30) + (40 x $2.50) = $30 + $100 = $130 Llama 3.3 70B: (100 x $0.59) + (40 x $0.79) = $59 + $31.60 = $90.60 Gemini is 32% cheaper on the retrieval workload and 30% dearer on the chat one. Neither model is the cheap one, and a stack running both shapes on a single choice is overpaying on half its traffic. ## Cost figures Every figure carries its confidence and its source. No figure on this site is provider-reported. - 0.17 — output-to-input ratio at which these two models cost the same [calculated] Source: ($0.30 - $0.59) divided by ($0.79 - $2.50), using real Gemini 2.5 Flash and Groq Llama 3.3 70B rates per million from the price book, effective 2026-07-02. - $90.60 to $251.80 — 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 Llama 3.3 70B cheaper than Gemini 2.5 Flash? A: Above an output-to-input ratio of about 0.17, yes. Below it, no. Llama costs more on input and much less on output, so chat-shaped work favours it and retrieval-shaped work favours Gemini. Q: Why do these two cross at such a low ratio? A: Because their price shapes are opposites. Gemini 2.5 Flash charges roughly eight times more for output than input, while Llama 3.3 70B charges only about a third more, the flattest ratio in this price book. Q: Should I run both? A: If your features sit on both sides of the crossover, routing by shape is cheaper than any single choice. Reading-heavy work goes one way and writing-heavy work the other, and the ratio tells you which is which. ## Sources - Gemini API pricing: https://ai.google.dev/gemini-api/docs/pricing - Groq pricing: https://groq.com/pricing Run the free Cost Leak Scan: https://app.getculpa.com/scan?source=pseo&slug=gemini-2-5-flash-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. 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