Meta AILlama 4 ScoutvsGoogle GeminiGemma 4 31B: API Pricing Comparison & Performance Calculator

Every prompt, document, and system message costs input tokens. Meta AI Llama 4 Scout is $0.65 per million input tokens. Google Gemini Gemma 4 31B is $0.14. For read-heavy workloads, Google Gemini Gemma 4 31B wins. If you process huge documents daily, that gap adds up fast—pick Google Gemini Gemma 4 31B over Meta AI Llama 4 Scout when quality is similar. Use our calculator above to see exact input costs.

Output tokens are what the model generates. They are usually pricier than input. Meta AI Llama 4 Scout charges $2.75 per million output tokens; Google Gemini Gemma 4 31B charges $0.4. For long answers, code, or reports, favor Google Gemini Gemma 4 31B. Tight prompts ("answer in one paragraph") cut spend on either side. Our calculator helps you estimate these output costs accurately.

Context is how much text fits in one request. Meta AI Llama 4 Scout allows up to 128,000 tokens. Google Gemini Gemma 4 31B allows up to 256,000. Google Gemini Gemma 4 31B fits longer docs or repos—but you pay for every token you send, every turn. Do not max the window unless you need it. In plain words: Standard for chat + medium docs; chunk bigger sources. For the other side: Strong for long reports, transcripts, and mid-size repos.

Llama 4 Scout is text-only here. Gemma 4 31B supports vision (about $0.005 per image). Resize images before the API when you can—it lowers token load and cost.

Reusing the same long context? Caching can slash input cost. Llama 4 Scout does not show a cached rate in our data. Gemma 4 31B does not show a cached rate here. Great for chat over one big PDF or policy doc.

If you do not need instant replies, batch jobs often run at a steep discount (often around half off list price, depending on the provider). Ship a file of requests, get results within about a day. Ideal for summaries, translations, and backfills. Use the calculator toggles above to see how batch mode changes your estimate.

Chats repeat system prompts and history every turn. A short user message can still bill thousands of input tokens. Lower input price helps—Google Gemini Gemma 4 31B is usually safer for high-volume chat. On our speed hints, Meta AI Llama 4 Scout is 0/100 (Moderate / variable) and Google Gemini Gemma 4 31B is 0/100 (Moderate / variable). If one is clearly ahead on both price and speed hint, that is a nice combo for live chat—but slow networks or huge prompts can still swamp the difference, so try a realistic thread in your region.

Extraction needs accuracy and often a large context for messy PDFs. Try both Meta AI Llama 4 Scout and Google Gemini Gemma 4 31B on real samples. If quality matches, pick the cheaper input side—extraction is usually input-heavy.

Coding rewards reliability over saving a few cents. Bad output costs engineer time. Our coding-strength hints (again, heuristics) put Meta AI Llama 4 Scout at 0/100 and Google Gemini Gemma 4 31B at 0/100, with broader “smarts” hints at 0/100 vs 0/100. Between this pair, favor whichever passes your tests on your stack traces and style rules; if quality is a tie, output price leans toward Google Gemini Gemma 4 31B for long patches.

System prompts ride along on every call. Example: 1,000 tokens × 100,000 requests per day ≈ 100M input tokens daily. At $0.65 per million for Meta AI Llama 4 Scout, that is about $65.00 per day from the system prompt alone. Keep instructions short and reusable.

RAG sends retrieved chunks with each question. More chunks mean more input tokens to Meta AI Llama 4 Scout or Google Gemini Gemma 4 31B. Tighten retrieval: send only the best few passages, not whole folders.

Long prompts tax you every request. Fine-tuning costs upfront but can shorten prompts. Compare total cost in our calculator: long prompt + cheap base model vs short prompt + fine-tuned pricing if you use it.

Agents may call Meta AI Llama 4 Scout or Google Gemini Gemma 4 31B many times per user task. One workflow can equal dozens of normal chat turns. Cap steps, log spend, and alert on spikes.

Bill clients for API usage you resell. Use Agency Mode in the calculator for markup, client price, and margin—plus PDFs for proposals.

Flat plans get burned by power users on Meta AI Llama 4 Scout or Google Gemini Gemma 4 31B. Credits or BYOK (bring your own key) align revenue with cost.

Dashboards, alerts, and tools like Helicone or Langfuse show who burns tokens and which prompts bloat bills. Measure before you optimize.

Beyond this pair, consider OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, or Google Gemini Gemini 1.5 Pro for price or capability fit. Design your stack so you can swap models without a rewrite.

List prices keep falling, but workloads get heavier—bigger contexts, agents, more tools. Net spend can still climb. Keep a running estimate whenever you change models or traffic.

Cost is not everything. Llama 4 Scout carries a speed hint of 0/100 (Moderate / variable); Gemma 4 31B is 0/100 (Moderate / variable).Self-hosted latency is determined by your infra. Latency depends entirely on your hardware stack. In production you still want time-to-first-token and tokens per second on your prompts, region, and concurrency—especially for voice, typing indicators, or anything that feels “live.”

Check training, retention, and region rules for each provider behind Llama 4 Scout and Gemma 4 31B. Regulated data needs enterprise terms, not guesswork.

Proprietary APIs are simple but price-controlled. Open models (e.g. Llama family) add ops work but can cut unit cost at scale. Match the tradeoff to your team.

Consultants can embed this Llama 4 Scout vs Gemma 4 31B experience white-label, capture emails with PDF reports, and turn pricing questions into leads—free with LeadsCalc.