Guides / gemini 3.1 flash lite vs llama 3.3 70b cost
Gemini 3.1 Flash-Lite vs Llama 3.3 70B cost
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.
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Mistakes that cost the most
| Mistake | Why it hurts | Do instead |
|---|---|---|
| Comparing models at the account level. | An account-wide ratio averages features that sit on opposite sides of the crossover. | 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. | You pick one winner and keep paying the loser's price on everything past the switch point. | Send each feature to the cheaper side. Two models is a valid answer. |
| Assuming routing costs more to run than it saves. | The saving modelled here is 13% of the best single choice, which beats most prompt tuning. | Price the split against your best single model before deciding it isn't worth the wiring. |
Run this check tonight
- 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 example
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.
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.
Every number, with its confidence and source
| Figure | What it means | Confidence | Source |
|---|---|---|---|
| 0.48 | output-to-input ratio at which these two models cost the same | calculated | ($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 | $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 | The four totals in the teardown arithmetic at real rates. A range because the token volumes are modelled. |
What a generic answer can’t know
Both rate cards are public and the crossover is arithmetic anyone can do. Which of your features sit above it isn't, because that needs your own input and output volumes split by feature rather than summed across an account. Culpa measures them on your infrastructure, keeps your prompts and responses there, and counts the calls to run your plan.
Questions founders ask next
Which is cheaper, Gemini 3.1 Flash-Lite or Llama 3.3 70B?
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.
Is running two models worth the extra wiring?
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.
Why does an account-wide average mislead?
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.
On your infrastructure
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.
How Culpa works
Find the culprit. Not just the total.
Your dashboard shows what you spent. It stops short of who spent it. Culpa shows the conversation, the user and the feature behind it.
Your prompts stay local.
Culpa runs on your own infrastructure. What you send to a model reaches us at no point.
Every dollar has a name.
Follow any charge to the conversation, the user, the feature and the customer behind it.
See the bill before it lands.
Cost your next feature before you ship it. You get the likely bill and the worst case, at best, median, p90 and p99.
Three steps to your first answer.
Change one base URL.
Or drop in the Python or TypeScript library.
Find your most expensive conversation.
In the first session, not the first week.
Cost your next feature before you ship it.
Why the bill went up
Example dashboardCalls traced
418,209
across 3 projects
Spend this week
$378.41
+ $182 vs last week
Failed calls
312
74% retried, and you paid for all of them
+ $182 this week traced to one culprit
Spend over 14 days
Most expensive users
Next week forecast
Graded against reality. Accuracy shown as results land.
Free, no card, no account
Run the free Cost Leak Scan
It shows your most expensive conversation before you install anything.
Keep reading
Sources: Gemini API pricing, Groq pricing. Last reviewed 2026-08-02. Plain text version.