# Gemini 3.1 Flash-Lite vs Llama 3.3 70B cost > These two cross at an output-to-input ratio of 0.48, which sits inside the range real features occupy. Routing per feature beats picking one model. URL: https://getculpa.com/gemini-3-1-flash-lite-vs-llama-3-3-70b-versatile-cost Last reviewed: 2026-08-02 ## Answer Gemini 3.1 Flash-Lite costs less than Llama 3.3 70B on Groq below an output-to-input ratio of 0.48, and more above it. That crossover sits inside the range real features occupy, so one product often has work on both sides of it. Culpa, a local-first LLM cost, margin, and forecast ledger, measures the ratio per feature and prices each against both. ## Why this happens Most published crossovers are trivia, because they sit at a ratio nobody runs. This one doesn't. Retrieval lands near 0.1, chat near 0.4 and code generation past 1.0, so a switch point at 0.48 falls in the middle of a normal product's spread of features. That changes the question. There's no single cheaper model here, and picking one is a decision to overpay on half your traffic. The useful move is to route each feature to whichever side of 0.48 it sits on, which needs a ratio per feature rather than one per account. ## What this usually looks like - One model serves every feature because the comparison was done once, at the account level. - Your features have very different shapes and nobody has priced them separately. - A model migration saved money on one feature and cost money on another. - Your account-wide ratio is an average that describes none of your actual features. ## Common mistakes - Comparing models at the account level. Why it hurts: An account-wide ratio averages features that sit on opposite sides of the crossover. Do instead: Compute the ratio per feature. That's the unit the decision is actually made in. - Treating a crossover as a tie-breaker rather than as a router. Why it hurts: You pick one winner and keep paying the loser's price on everything past the switch point. Do instead: Send each feature to the cheaper side. Two models is a valid answer. - Assuming routing costs more to run than it saves. Why it hurts: The saving modelled here is 13% of the best single choice, which beats most prompt tuning. Do instead: Price the split against your best single model before deciding it isn't worth the wiring. ## Self-check - List your features and compute output divided by input for each one separately. - Mark each feature as above or below 0.48. - If they all land on one side, pick that model and stop. - If they straddle it, price the split against your best single choice. - Re-check whenever a prompt or an output format changes, because that moves the ratio. ## Two features, opposite sides, and what the split is worth (illustrative) Gemini 3.1 Flash-Lite at real rates of $0.25 per million input and $1.50 output, against Llama 3.3 70B Versatile on Groq at $0.59 and $0.79, both effective 2026-07-02 and re-verified on the provider pages 2026-08-02. Volumes are modelled. Crossover: ($0.59 - $0.25) / ($1.50 - $0.79) = 0.48 output tokens per input token Summarisation, 200M input and 30M output, a ratio of 0.15 Flash-Lite: (200 x $0.25) + (30 x $1.50) = $50 + $45 = $95.00 Llama 3.3 70B: (200 x $0.59) + (30 x $0.79) = $118 + $23.70 = $141.70 Code generation, 50M input and 60M output, a ratio of 1.20 Flash-Lite: (50 x $0.25) + (60 x $1.50) = $12.50 + $90 = $102.50 Llama 3.3 70B: (50 x $0.59) + (60 x $0.79) = $29.50 + $47.40 = $76.90 Best single model for both features: Flash-Lite at $95.00 + $102.50 = $197.50 Each feature routed to its cheaper side: $95.00 + $76.90 = $171.90, a 13% saving Neither model wins this product. The best single choice costs $197.50 and routing each feature to its cheaper side costs $171.90, so 13% is what knowing the ratio per feature is worth here. ## Cost figures Every figure carries its confidence and its source. No figure on this site is provider-reported. - 0.48 — output-to-input ratio at which these two models cost the same [calculated] Source: ($0.59 - $0.25) divided by ($1.50 - $0.79), using real Gemini 3.1 Flash-Lite and Groq Llama 3.3 70B Versatile rates per million from the price book, effective 2026-07-02 and re-verified on both provider pages 2026-08-02. - 13% — modelled saving from routing two features to opposite models rather than picking the best single one [calculated] Source: $197.50 minus $171.90, divided by $197.50, from the teardown arithmetic. - $76.90 to $141.70 — modelled monthly cost across two features 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: Which is cheaper, Gemini 3.1 Flash-Lite or Llama 3.3 70B? A: It depends on the feature rather than on the product. Below an output-to-input ratio of 0.48 Flash-Lite wins and above it Llama 3.3 70B wins. Retrieval and summarisation sit below. Code generation and long drafting sit above. Q: Is running two models worth the extra wiring? A: On the split modelled here it saves 13% against the best single choice, which compares well with most prompt tuning. Price your own split first, because the answer scales with how far your features sit from 0.48. Q: Why does an account-wide average mislead? A: Because it averages features sitting on both sides of the switch point, so it can land near 0.48 while none of your features do. A ratio that describes no real feature can't pick a model for any of them. ## 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-3-1-flash-lite-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.