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Welcome to the ROI chatbot arena. Adjust the sliders below to see which model actually wins when it comes to your monthly API bill and production speed. Navigating the unit economics for LLMs of modern LLMs requires a granular look at how DeepSeek V3.2 stacks up against Meta AI Llama 4 Scout in real-world enterprise AI stacks. While Llama 4 Scout is widely regarded for its GPQA reasoning scores, DeepSeek V3.2 offers a massive 78% reduction in input costs, making it the superior choice for high-volume enterprise AI stacks where unit economics for LLMs is the primary KPI. Our 2026 analysis provides the data-driven insights you need to optimize your enterprise AI stacks without overpaying for unused GPQA reasoning scores.
List $/1M tokens, context limits, and estimated monthly bill for the same workload you configure below—API list math for the first two models in this calculator.
DeepSeek V3.2
DeepSeek
Llama 4 Scout
Meta AI
Longer bar = higher list spend for the sliders below. Cheaper run for this scenario is highlighted.
Open the full workspace—dial tokens, requests, vision, batch & agency, then line up up to four models on that exact scenario. You get true monthly list cost, heuristic performance, and a Final Verdict ranking built for your numbers—not a generic blog table.
Sliders, charts & compare
This page's two models are pre-selected. Add up to four models—sliders and toggles below apply the same usage to every model in the list.
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.
No compared model uses a batch-eligible provider in this catalog.
Get notified when cost exceeds limit
DeepSeek V3.2
DeepSeek
Input (list)
$0.25 / 1M
Output (list)
$0.38 / 1M
Effective input / output (this scenario)
$0.252 / 1M in · $0.378 / 1M out
Cached input
~$0.070 / 1M available — turn on "Use Cached Pricing" to apply
Batch pricing
Not batch-eligible for this provider in our catalog
Vision
No vision in catalog for this model
Input $
$10.08
Output $
$3.78
Vision $
$0.00
Llama 4 Scout
Meta AI
Input (list)
$0.080 / 1M
Output (list)
$0.30 / 1M
Effective input / output (this scenario)
$0.080 / 1M in · $0.300 / 1M out
Cached input
No cached input rate in catalog for this model
Batch pricing
Not batch-eligible for this provider in our catalog
Vision
Up to $0.0070 per image when vision is on
Input $
$3.20
Output $
$3.00
Vision $
$0.00
Stacked spend by model — input, output, and vision from your sliders.
Input tokens
8K
per request
Output tokens
2K
per request
Images
—
vision off
All selected models — same workload & toggles
Up to $7.66/mo ($91.92/yr) less with Llama 4 Scout vs DeepSeek V3.2 for this workload.
DeepSeek
DeepSeek V3.2
per month
Per request
$0.000000
Per 1K tokens
$0.0006
Meta AI
Llama 4 Scout
Cheapest for this workload — same sliders & toggles as above; lowest projected monthly cost in your compare list.
per month
Per request
$0.000000
Per 1K tokens
$0.0004
At a glance
Context max
131,072 tokens
Catalog limit per request
TPS index
45/100
~99 TPS est. (illustrative)
Vision
No
Multimodal detail below
TPS index (0–100)
Higher fill = snappier catalog proxy for comparisons, not your measured TPS.
45/100
Context vs reference
Bar vs 2M-token reference — headroom for long prompts.
~6.6% of reference — heuristic scale for long-document headroom.
Deployment and API surface
Deployment
Open-weight lineage (may be self-hostable — verify license)
Architecture
MoE
Tools
Tools / function calling (Standard)
Documents and multimodal
Audio (no). Text-first API usage (typical chat / documents via text)
JSON mode
Yes (typical API)
At a glance
Context max
327,680 tokens
Catalog limit per request
TPS index
0/100
~25 TPS est. (illustrative)
Vision
Yes (catalog)
Multimodal detail below
TPS index (0–100)
Higher fill = snappier catalog proxy for comparisons, not your measured TPS.
0/100
Context vs reference
Bar vs 2M-token reference — headroom for long prompts.
~16.4% of reference — heuristic scale for long-document headroom.
Deployment and API surface
Deployment
Open-weight lineage (may be self-hostable — verify license)
Architecture
Dense
Tools
Tools / function calling (Standard)
Documents and multimodal
Audio (no). Multimodal text + images (vision-capable in catalog)
JSON mode
Yes (typical API)
Values come from provider-family profiles in this tool, not live pings from your network or a live inventory of your stack. Use vendor observability and contracts for latency SLOs and regional behavior.
Your custom ranking based on your specific token volume. We calculate the exact ROI by dividing catalog benchmarks by your live estimated monthly cost for the US, Canadian, and Australian markets.
Custom ROI Ranking
Bars are sorted best → rest. Eligible value leaders scale to 100% within the set. Non-native "thinking" picks are capped at 15% width and muted so they never look like a full-value winner.
Brand colors = eligible rows; muted slate = not a value pick for current toggles.
DeepSeek V3.2
Highest quality-per-dollar for your numbers below. Others are shown as a % of this leader.
Model DNA radar
Seven pillars per model — each axis is 0–100 on a catalog-wide absolute scale (not min–max within this lineup). Price favors lower list cost at the 500K in / 100K out sample; logic/coding/speed pull from the same heuristics as the value score (catalog benchmarks).
Axes: Price · Logic · Coding · Context · Speed · Multimodal · Openness. Openness = rough “how open/hostable” hint from provider family, not a license statement.
Four angles on the same compare — choose the story that matches what you optimize for.
Lowest cost
Top quality
Largest context
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Best for: Cost-effective coding and open-source deployment
Best for: Enterprise fine-tuning and local deployment
DeepSeek V3.2 is offered by DeepSeek as part of the hosted API lineup. List prices here are $0.14 per million input tokens and $0.28 per million output tokens. In this catalog it is set up as text-in, text-out. If you repeat the same long system prompt, cached input can drop toward about $0.07 per million tokens in our catalog snapshot (enable “Use Cached Pricing” above to model it). On our catalog benchmarks (0–100, not official vendor scorecards): composite 91/100, coding 92/100, logic/reasoning 90/100, math 95/100, and instruction following 88/100. For UX speed orientation we show a speed score of 45/100 and call it “Moderate / variable”—Self-hosted latency is determined by your infra. Context window is 640,000 tokens (Strong for long reports, transcripts, and mid-size repos.). Large single-shot context — fewer chunks for long PDFs / repos (still extract text per API rules) Tools: Strong — standard tool/function patterns on hosted API. JSON outputs: Usually yes on major hosted APIs; validate on your stack. Prompt caching: Yes — ~$0.07/M cached input. Catalog Benchmarks (0–100). Manually maintained model-level scores; verify on your own evals.
Meta AI Llama 4 Scout is offered by Meta AI as part of the hosted API lineup. List prices here are $0.65 per million input tokens and $2.75 per million output tokens. In this catalog it is set up as text-in, text-out. On our catalog benchmarks (0–100, not official vendor scorecards): composite 0/100, coding 0/100, logic/reasoning 0/100, math 0/100, and instruction following 0/100. For UX speed orientation we show a speed score of 0/100 and call it “Moderate / variable”—Self-hosted latency is determined by your infra. Context window is 128,000 tokens (Standard for chat + medium docs; chunk bigger sources.). Typically text-in via your ingestion pipeline; size to context limit Tools: Varies — host/SDK dependent for open-weight routes. JSON outputs: Usually yes on major hosted APIs; validate on your stack. Prompt caching: Depends on provider — use catalog cached rate when shown. Benchmark scan pending — live OpenRouter pricing is synced; scores populate after autonomous research.
Price + performance hints
Choosing between DeepSeek V3.2 and Meta AI Llama 4 Scout affects your monthly API bill and how snappy your app feels. Skip the hype. Use the calculator above for dollars, then use this page for context limits, caching, and our plain-language hints on speed (45/100 vs 0/100) and rough “smarts” (91/100 vs 0/100). Those hints come from catalog + provider family signals—they are not lab benchmarks—so still try both on real tasks.
API latency and failover paths depend on where you host and which provider region you call. Teams in Australia often verify Sydney (ap-southeast-2) or Singapore edges; US buyers standardize on us-east-1 / us-west-2; Canada frequently maps to the same US regions or dedicated CA endpoints where offered. Our list prices are global list rates—map the model to your closest allowed region in the provider console, then re-run the workspace above with your real traffic split so CFOs and CTOs see numbers tied to production, not a generic blog table.
DeepSeek V3.2
DeepSeek
Meta AI Llama 4 Scout
Meta AI
Performance snapshot (hints, not benchmarks)
For “how quick it usually feels” in our rough scale, DeepSeek V3.2 sits a little higher (45/100 vs 0/100). That is not a live benchmark—just a hint from model family and catalog signals. For overall quality hints, DeepSeek V3.2 edges ahead (91/100 vs 0/100). For coding-style strength hints, DeepSeek V3.2 is a bit higher (92/100 vs 0/100). Always run a few real prompts that matter to you.
| DeepSeek V3.2 | Meta AI Llama 4 Scout | |
|---|---|---|
| Speed hintrough latency vibe | 45/100 | 0/100 |
| Tier labelhow we bucket it | Moderate / variable | Moderate / variable |
| Overall smartsnot official scores | 91/100 | 0/100 |
| Coding hintheuristic | 92/100 | 0/100 |
Catalog Benchmarks (0–100). Manually maintained model-level scores; verify on your own evals. Same idea applies to both sides—use these rows as a starting point, not a verdict.
Every prompt, document, and system message costs input tokens. DeepSeek V3.2 is $0.14 per million input tokens. Meta AI Llama 4 Scout is $0.65. For read-heavy workloads, DeepSeek V3.2 wins. If you process huge documents daily, that gap adds up fast—pick DeepSeek V3.2 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. DeepSeek V3.2 charges $0.28 per million output tokens; Meta AI Llama 4 Scout charges $2.75. For long answers, code, or reports, favor DeepSeek V3.2. 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. DeepSeek V3.2 allows up to 640,000 tokens. Meta AI Llama 4 Scout allows up to 128,000. DeepSeek V3.2 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: Strong for long reports, transcripts, and mid-size repos. For the other side: Standard for chat + medium docs; chunk bigger sources.
DeepSeek V3.2 is text-only here. Llama 4 Scout is text-only here. Resize images before the API when you can—it lowers token load and cost.
Reusing the same long context? Caching can slash input cost. DeepSeek V3.2 lists cached input around $0.07 per million tokens. Llama 4 Scout 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—DeepSeek V3.2 is usually safer for high-volume chat. On our speed hints, DeepSeek V3.2 is 45/100 (Moderate / variable) and Meta AI Llama 4 Scout 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 DeepSeek V3.2 and Meta AI Llama 4 Scout 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 DeepSeek V3.2 at 92/100 and Meta AI Llama 4 Scout at 0/100, with broader “smarts” hints at 91/100 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 DeepSeek V3.2 for long patches.
System prompts ride along on every call. Example: 1,000 tokens × 100,000 requests per day ≈ 100M input tokens daily. At $0.14 per million for DeepSeek V3.2, that is about $14.00 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 DeepSeek V3.2 or Meta AI Llama 4 Scout. 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 DeepSeek V3.2 or Meta AI Llama 4 Scout 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 DeepSeek V3.2 or Meta AI Llama 4 Scout. 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. DeepSeek V3.2 carries a speed hint of 45/100 (Moderate / variable); Llama 4 Scout is 0/100 (Moderate / variable).Self-hosted latency is determined by your infra. Self-hosted latency is determined by your infra. 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 DeepSeek V3.2 and Llama 4 Scout. 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 DeepSeek V3.2 vs Llama 4 Scout experience white-label, capture emails with PDF reports, and turn pricing questions into leads—free with LeadsCalc.
Dollar figures reflect catalog pricing; speed and “smarts” rows are in-house hints, not vendor benchmarks. Confirm rates and run your own latency tests before you commit.
“Final Verdict: If your inference architecture is cost-sensitive and volume-heavy, DeepSeek V3.2 is the logical choice to maximize your token economics. Reserve Llama 4 Scout for the 5% of tasks that require absolute latency profiles.”
While traditional chatbot arenas measure human preference (vibes), the LeadsCalc arena measures hard ROI. We pit models against each other based on cost-per-1M tokens, context windows, and latency.
More side-by-side API pricing calculator pages (for people and search). Each link opens an interactive cost calculator with the same breakdown style as this page. Use our calculator to evaluate different models and price tiers.
Pricing, speed hints, and rough “smarts” scores for DeepSeek V3.2 vs Meta AI Llama 4 Scout
For startups scaling on a budget, DeepSeek V3.2 is the clear winner for ROI optimization, offering significantly lower entry costs. However, if your app requires maximum benchmark parity, the premium for Llama 4 Scout may be justified by its higher accuracy.