LLM API PRICING & BENCHMARK HUB
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Planning to build an AI agent or application with Meta AI Llama 4 Scout in 2026? Understanding your enterprise AI stacks budget is critical. At $0.65 per 1M input tokens and $2.75 per 1M output tokens, this model offers GPQA reasoning scores suitable for Enterprise fine-tuning and local deployment. Our interactive tool below allows you to model your exact enterprise AI stacks, adjusting for prompt caching and batching to find the highest unit economics for LLMs for your production requirements.
Provider (9/12 · hover to remove)
Model (2 available)
Typical API, Heavy RAG, and Max context stress set monthly requests and how hard each call uses the token sliders—stress caps per request and trims calls so totals stay readable. Clears a use-case template on the right. Moving requests clears this row; moving input/output clears the tier.
Sets input, output, requests, and template value weights for the ROI read—touch a token slider and weights fall back to 50% / 50%. With Deep Reasoning, output is ×1.4 before pricing. Clears a volume preset on the left.
Off — no image fees for models that support vision.
Turn On to include image fees.
Applies cached input rates where this catalog lists them (OpenAI, Anthropic, Google, …). Models without a cached rate keep list pricing.
Add markup for client pricing
Model hidden reasoning / extended thinking charged like output tokens when enabled.
Enable for 50% off input & output
Get notified when cost exceeds limit
Estimated monthly
≈ $642.00 over 12 months if spend stayed flat (no growth or price changes).
List (catalog)
$0.65 in
$2.75 out
per 1M tokens
This scenario
$0.65 in
$2.75 out
effective $/1M
Share of this month
Bars use your current request and token settings. The right chart contrasts published list pricing with your effective rates after cache, batch, and related toggles.
By category
Input, output, and vision for this workload.
List vs optimized (monthly)
Total monthly at list ratecard vs your scenario.
Month n = n × estimated monthly bill — no seasonality or usage growth.
MMMU 69.4% and ChartQA 88.8% map to 70 multimodal. Lacking SWE-bench or GPQA, coding and logic are inferred (65-70) for this 17B-active lightweight MoE tier. Speed is high (85) due to small active parameter count.
Composite
Catalog benchmark · 0–100 per row
General knowledge & logic (MMLU-style)
Broad reasoning proxy for comparing model families — not a literal MMLU leaderboard value.
Coding & agents (HumanEval-style)
Coding and tool-use suitability from provider tier and model-id hints, not a fresh code benchmark.
Instruction following
How tightly the model tends to follow complex instructions in our catalog benchmark.
Math & reasoning depth
Numeric and reasoning tilt; boosted for reasoning-first ids in the catalog where applicable.
Same model as above, shown as a radar with a grey industry-average shadow. Axes are normalized in this view, not absolute benchmark percentiles.
Model DNA radar chart for selected models
Axes: Price · Logic · Coding · Context · Speed · Multimodal · Openness. Openness = rough “how open/hostable” hint from provider family, not a license statement.
Catalog Benchmarks (0–100). Manually maintained model-level scores; verify on your own evals.
Prompt vs catalog window
8,000 input tokens of 128,000 max. Confirm hard output caps in the vendor console.
6.3% of catalog window
TPS speed index
85 /100
≈ 165 TPS display estimate — not measured from your traffic.
US = 100 baseline. Values are a deterministic illustration from model id and provider tier, not ping or routing from your network.
United States
Baseline edge (illustrative)
Canada
Typical North America variance
Australia
Long-haul hint vs US edge
Architecture
Dense
MoE vs dense inferred from catalog / id.
Deployment
Open-weight lineage (may be self-hostable — verify license)
Tools and modalities
Tools / function calling (Standard)
Text-first API usage (typical chat / documents via text)
JSON mode
Yes (typical API)
Audio (id hint)
No strong id hint
Nothing here is a live latency measurement, SLO, or inventory of your deployment. Use vendor dashboards and your own traces for TTFT, tokens per second under load, and regional routing.
Est. API spend
/ month at these sliders
Strongest scenario
Chatbot Arena
Highest fit index right now
Evaluate if Llama 4 Scout meets your production requirements based on your token volume and active features above. What follows folds those same sliders into pricing and capability signals—value for spend, a concise ROI read, and four mapped scenarios—so you can stress-test this pick without re-entering inputs.
ROI Verdict: Llama 4 Scout — At your effective token prices this scenario sits in a mid-market band. On the same catalog benchmark 0–100 axes as the Model DNA chart, Llama 4 Scout reads as balanced general-purpose performance without a single dominant pillar. Stress-test against enterprise fine-tuning and local deployment if that mirrors your product.
Value for spend
Higher usually means more catalog intelligence per dollar at your effective token prices — for comparisons inside this tool only.
Our one-line read
ROI Verdict: Llama 4 Scout — At your effective token prices this scenario sits in a mid-market band. On the same catalog benchmark 0–100 axes as the Model DNA chart, Llama 4 Scout reads as balanced general-purpose performance without a single dominant pillar. Stress-test against enterprise fine-tuning and local deployment if that mirrors your product.
Figures mirror the calculator above. Treat as orientation: confirm with your own benchmarks, regions, and contract discounts before you commit budget.
Each card shows a fit score (0–100) for a typical workload shape. Scan the bars, then read the lane that sounds like your product.
73
fit
Llama 4 Scout in chatbot arena matchups
Self-hosted latency is determined by your infra. For chatbot arenas, pricing on output tokens matters most when replies are long — Llama 4 Scout is usable across tiers if you cap completion length.
54
fit
Llama 4 Scout in coding & agent workflows
Llama 4 Scout handles coding workloads with a moderate coding index (65/100 on the same heuristic axis as the DNA radar) — Enterprise fine-tuning and local deployment
39
fit
Llama 4 Scout on long documents & RAG
Context window 128,000 tokens frames how much Llama 4 Scout can hold per call — pair chunking with enterprise fine-tuning and local deployment.
63
fit
Llama 4 Scout on structured extraction
Heuristic math/logic blend suggests Llama 4 Scout for numeric-heavy extraction — always validate on your schema.
Fit indices mix catalog intelligence with your effective prices; incompatible Vision or non-native Deep reasoning toggles zero or heavily discount lanes, matching the compare value engine. The efficiency ring blends the same template weights — orientation only, not a vendor benchmark.
Workload: Custom Configuration
75
Overall Intelligence Score
Scores in the 70–80 band are workable for many organizations — pressure-test latency, accuracy, and total cost of ownership against representative production traffic.
Monthly spend mix — use the split to prioritize where you optimize first.
Pros
Cons
Validate pricing and capability
Fit is acceptable but not decisive. Stress‑test Llama 4 Scout against nearby models on reasoning quality, long‑context needs, and per‑million token pricing for your exact input/output mix.
Use the options below to explore alternatives with clearer ROI, or compare head‑to‑head to narrow the field before scaling spend.
Best for: High-volume simple chat, drafting, and cost experiments
Best for: Hard problems where correctness beats speed
Best for: Research, fine-tuning, and self-hosted inference
Best for: Massive context RAG, long video/text analysis, and research dumps
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Optimization playbook
This part is here to help you use Meta AI Llama 4 Scout on Meta AI without surprises. We use the same simple numbers you see in the calculator above. We are not your lawyer or your security team — grown-ups on your side still need to check contracts and privacy rules.
A token is a small piece of text the computer counts. It is not always one word — short words can share a token, long words can use more than one. That is OK. What matters is: more tokens → more money, just like more minutes on a phone plan.
When you move the input and output sliders on this page, you are really saying "my question is this long" and "I want an answer about this long." The bill grows when either side grows.
For Meta AI Llama 4 Scout, our list says about $0.65 for every 1 million input tokens and about $2.75 for every 1 million answer tokens.
Those prices are list prices from our catalog. Your real bill can go up or down when you turn on batch mode, caching, vision, or special "think longer" modes — use the toggles above to see that story for your own app.
Picture the AI living in a data center. If your users are in Australia but you always call a far-away region, answers can feel slower and routing can get fussier. Picking a closer home base is like picking a playground near your house instead of across town.
Teams in Australia often test Sydney (ap-southeast-2) or Singapore. Teams in the US often pick us-east-1 or us-west-2. Canada often maps to the same US zones or a Canada-only route if Meta AI offers one.
After you pick a region in the real Meta AI console, come back here and plug in the traffic you expect. Then the money line matches what your users will feel in production.
Llama 4 Scout
Meta AI
The three boxes above are your quick cheat sheet for Meta AI Llama 4 Scout: Input is what you pay to send stuff in, Output is what you pay to get answers back, and Context is how big one combined message can be (128,000 tokens in our catalog).
Performance snapshot (hints, not benchmarks)
Think of this as a report card for Meta AI Llama 4 Scout inside LeadsCalc — not a race you won in real life. The numbers come from our catalog, not from timing your app today.
Speed score 85/100 means we guess it feels pretty quick for most apps (balanced). Smarts score 70/100 blends logic, coding, listening, and math hints into one line so you can compare models without a PhD.
One more plain note: Self-hosted latency is determined by your infra. In kid words: the table is a guess from our catalog, like a weather forecast — your real app might feel a little different.
| Meta AI Llama 4 Scout | |
|---|---|
| Speed hinthow snappy it may feel | 85/100 |
| Speed bucketwe group models like this | Balanced |
| Overall smartsone blended score from our catalog | 70/100 |
| Logic & tricky puzzleshard questions, not just small talk | 70/100 |
| Coding hintgood for code or not | 65/100 |
| Following instructionsdoes it listen well | 75/100 |
| Math-style thinkingnumbers and logic | 70/100 |
| Room for one big askStandard for chat + medium docs; chunk bigger sources. | ~128K tokens |
Catalog Benchmarks (0–100). Manually maintained model-level scores; verify on your own evals.
Our price list for this model does not show a special cached rate yet. That does not mean caching never exists — it just means you should read Meta AI's own page, then type the discount you really get into your own spreadsheet.
Catalog hint: Not listed in catalog — assume full input rate
The system prompt is the quiet voice that tells the model how to behave. Every word there is counted on every chat turn — like paying a cover charge at the door again and again.
Keep the rules short and sweet. Put long examples in a file your app reads once, or pull facts with search ( RAG ) instead of pasting huge walls of text. Then slide the input knob above and watch the month total shrink for Meta AI Llama 4 Scout.
Feature hint: Depends on provider — use catalog cached rate when shown
Some apps need the model to call tools (like a calculator or a database) or return neat JSON for your code to parse. Think of tools like extra hands the model can borrow — super useful, but each call can add more steps and more tokens.
Meta AI Llama 4 Scout can hold a long story in one go — up to about 128,000 tokens in our catalog. That is like a very big book, but you still pay more when you stuff more text in each ask.
RAG is a fancy way to say "search my files first, then ask the model with only the best bits." That is cheaper than dumping a whole library into one prompt, and it often answers better too.
Our catalog caps one combined message around 128K tokens for this model — still huge, but not infinite. Split giant PDFs into chunks, only paste the top matches, and cap how long each chunk can be.
Files & docs hint: Typically text-in via your ingestion pipeline; size to context limit
For this model we treat the main path as text-first in our UI. If your vendor adds image input later, flip vision on in the calculator when it matches your bill.
Catalog line: No — text API focus in this catalog
Batch is like mailing letters in one big bag at the end of the day instead of hand-delivering each one. The answer might arrive later, but the stamp can cost a lot less — many vendors advertise roughly half off list for batch-style tiers when they apply.
If your job is not urgent overnight reports, exports, or backfills try the batch toggle in the calculator and compare the monthly line for Meta AI Llama 4 Scout.
The calculator may still show a "reasoning" style toggle for some setups. Treat it as maybe extra output tokens until your billing team confirms the exact meter on Meta AI.
Speed story: Balanced — Self-hosted latency is determined by your infra.
Chat apps love long friendly replies. Long replies mean more output tokens, and output tokens are money walking out the door.
Meta AI Llama 4 Scout can work well for assistants if you set a max answer length, cache the boring repeated rules, and trim empty chit-chat.
Our one-line vibe check: Enterprise fine-tuning and local deployment
Extraction means "read this messy pile, give me clean rows." You want short answers (like tight JSON) so you do not pay for a poem nobody asked for.
Put repeating examples in a cached block when you can, split monster spreadsheets into smaller jobs, and use batch pricing when the work can wait. Slide the output tokens down in the calculator to see how sensitive your bill is.
JSON hint: Usually yes on major hosted APIs; validate on your stack
Meta AI Llama 4 Scout can still help with code, but our hints say another family might be the specialist for raw coding speed. Use this model where its strengths shine, and switch when the task is mostly boilerplate generation.
Catalog coding score: 65/100 (same 0–100 toy scale as the table above — not a promise about your private repo).
Open-weight model (can be self-hosted)
This website is a calculator — we are not your security team. Meta AI decides what they log, how long they keep it, and which countries hold the data. If you handle health or school records, grown-ups need signed papers (things like BAAs / DPAs) — not just vibes.
Before any secret leaves your building, ask: "Would I be OK if this text was on a billboard?" If not, strip names, addresses, and passwords before you call Meta AI Llama 4 Scout.
Vendor note: Training / retention / regions are vendor-specific — confirm in enterprise agreements.
Some teams run Meta AI Llama 4 Scout-style weights on their own machines. That can change the math: fewer token bills, more electricity and nerds on call at 3 a.m. Compare that to letting Meta AI host everything for you.
Story from our catalog: Meta — Llama open weights; you control infra and data flow — Open-weight (often self-hostable)
Model prices bounce around like airplane tickets when airlines compete. New "mini" or "flash" models often push older prices down — good for buyers, noisy for budgets.
Save a PDF from this page when finance asks for proof, and peek at Meta AI's release notes when you renew a contract. The sliders above stay the fastest way to ask "what if traffic doubles?"
Live hint: Adjust sliders above for your tokens, requests, vision, cache, and batch — totals update live.
If you run an agency, you can embed the same sliders your visitors used here — with your colors, your logo, and a form that sends leads to your email or CRM. You skip rebuilding giant price tables by hand.
Jump straight into a head-to-head pricing view with Llama 4 Scout first in the comparison slug, matching how the rest of LeadsCalc orders model battles.
See token pricing, context windows, and quick qualitative notes for Llama 4 Scout against GPT-4o in one layout.
Compare Llama 4 Scout vs GPT-4o API PricingSee token pricing, context windows, and quick qualitative notes for Llama 4 Scout against Claude 3.5 Sonnet in one layout.
Compare Llama 4 Scout vs Claude 3.5 Sonnet API PricingSee token pricing, context windows, and quick qualitative notes for Llama 4 Scout against DeepSeek V3 in one layout.
Compare Llama 4 Scout vs DeepSeek V3 API PricingShort answers grounded in the catalog fields used by this calculator. Adjust assumptions in the tool above for your real traffic mix.
Based on our catalog benchmarks, Llama 4 Scout is evaluated across coding, logic, math, and instruction following. Use the performance radar chart above to see its exact strengths, or visit our comparison hub to see head-to-head win rates against models like GPT-4o and Claude 3.5 Sonnet.
For Meta AI Llama 4 Scout, this calculator uses $0.65 per 1M input tokens and $2.75 per 1M output tokens as baseline API pricing. Rates can vary by region, commitment tier, and batch endpoints—use the calculator above to stress-test your workload.
Llama 4 Scout is listed with a 128,000-token context window for a single request in our catalog. Very long prompts still increase cost linearly with tokens, so pair window size with caching and retrieval when possible.
Llama 4 Scout is listed here without vision; confirm multimodal support with your provider if you need images or PDFs.
Use the comparison links in the section above for side-by-side pricing and context, or open the full comparison hub at https://www.leadscalc.com/calculators/ai/compare to explore more model pairs.
Llama 4 Scout is offered under Meta AI in this catalog. Wire your keys and endpoints per their docs; this page focuses on token economics, not account setup.