DeepSeek V3.2 vs Llama 4 Scout
DeepSeek V3.2 and Llama 4 Scout sit at opposite ends of the design tradeoff: one is a dense-reasoning model tuned for coding and tool use, the other is a mixture-of-experts model built for scale and reach. V3.2 uses sparse attention to stay efficient at 131K context; Scout activates 17B of its 109B parameters and stretches to a 10M-token context window with native image input. The gap shows up clearly in both pricing and Design Arena benchmarks below.
Spec vs spec
| Spec | DeepSeek V3.2 | Llama 4 Scout |
|---|---|---|
| Context window | 131K | 10M |
| Max output | 64K | 16K |
| Input modalities | text | text, image |
| Output modalities | text | text |
| Knowledge cutoff | — | Aug 31, 2024 |
| Released | Dec 1, 2025 | Apr 5, 2025 |
| Reasoning | optional | — |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
deepseek/deepseek-v3.2
Input · 1M tokens
$0.229 + 3%$0.236
Output · 1M tokens
$0.343 + 3%$0.353
Cache read · 1M tokens
$0.023 + 3%$0.024
FEE — FLAT, EVERY MODEL3%
meta-llama/llama-4-scout
Input · 1M tokens
$0.100 + 3%$0.103
Output · 1M tokens
$0.300 + 3%$0.309
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
deepseek/deepseek-v3.2
$3.06
$2.97 provider + 3%
meta-llama/llama-4-scoutCheaper
$1.65
$1.60 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| DeepSeek V3.2 | Llama 4 Scout | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| Code | 1213 | #48 | 839 | #106 |
| Data viz | 1203 | #48 | 940 | #96 |
| Game dev | 1197 | #50 | 838 | #105 |
| UI components | 1203 | #47 | 824 | #100 |
| Websites | 1217 | #46 | 793 | #112 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
On OpenKey, price is provider list price × 1.03 (the flat 3% fee). DeepSeek V3.2 comes in at $0.2288/M input and $0.3432/M output from the provider, which becomes $0.235664/M input and $0.353496/M output on OpenKey. Llama 4 Scout is cheaper at $0.10/M input and $0.30/M output provider-side, or $0.103/M input and $0.309/M output on OpenKey.
Run a 10M-input / 2M-output workload and the difference is concrete: DeepSeek V3.2 costs $2.97, Llama 4 Scout costs $1.60. That's roughly 1.86x more for V3.2 on this exact workload, driven mostly by input tokens — V3.2's input price is 2.29x Scout's. If you're processing high volume with light reasoning demands, Scout's cost adds up to real savings.
Coding and design benchmarks
Design Arena data is the clearest signal here. On codecategories, DeepSeek V3.2 scores 1213 Elo (rank 48, 49.7% win rate) vs. Llama 4 Scout's 839 Elo (rank 106, 26.6% win rate). On website generation, V3.2 hits 1217 Elo (rank 46) against Scout's 793 Elo (rank 112). Gamedev, dataviz, and uicomponent categories show the same pattern — V3.2 leads by 300-400 Elo points in every shared category.
Separately, Artificial Analysis scores Llama 4 Scout at an intelligence index of 10, coding index of 8.2, and agentic index of 1.1 — no comparable Artificial Analysis numbers exist for V3.2 in this data, but the Design Arena gap alone is decisive for anything code- or UI-generation heavy.
Context window and modality
Llama 4 Scout's 10,000,000-token context window dwarfs DeepSeek V3.2's 131,072 — a ratio where V3.2 has roughly 1% of Scout's context. If your job is ingesting an entire codebase, a long legal document set, or a big log dump in a single call, Scout is the only one of the two that can physically do it in one pass. Scout also accepts image input alongside text (text+image->text), while V3.2 is text-only in both directions.
Max output differs too: V3.2 allows up to 64,000 completion tokens, Scout caps at 16,384. So for long-form generation in a single response, V3.2 has more headroom even though its input context is smaller.
Tool use and reasoning controls
DeepSeek V3.2 supports a `reasoning` parameter and `include_reasoning`, letting you toggle a reasoning mode on top of tool calling — useful for agentic pipelines where you want visible intermediate steps. Llama 4 Scout's parameter list doesn't include reasoning controls at all; it's a more direct instruct model. Both support `tools` and `tool_choice`, so basic function calling works on either, but V3.2's parameter set is built with agentic workflows more explicitly in mind, consistent with its stronger showing on codecategories in Design Arena.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Agentic coding / codegen | DeepSeek V3.2 | 1213 Elo vs. 839 Elo on Design Arena codecategories |
| Website / UI generation | DeepSeek V3.2 | 1217 Elo vs. 793 Elo on the website category, largest gap of any category |
| High-volume, cost-sensitive batch jobs | Llama 4 Scout | $1.60 vs. $2.97 on a 10M-in/2M-out workload |
| Ingesting very long documents or repos in one call | Llama 4 Scout | 10,000,000-token context vs. 131,072 for V3.2 |
| Image + text input tasks | Llama 4 Scout | only Scout supports image input (text+image->text modality) |
| Long single-response generation | DeepSeek V3.2 | 64,000 max completion tokens vs. Scout's 16,384 |
Questions
- Which model is cheaper for a typical 10M-input/2M-output job?
- Llama 4 Scout, at $1.60 total on OpenKey vs. $2.97 for DeepSeek V3.2 — about 1.86x more expensive for V3.2 on identical token counts, mostly because its input price ($0.235664/M vs. $0.103/M) is 2.29x higher.
- Does either model support image input?
- Yes — Llama 4 Scout is text+image->text, so it accepts images natively. DeepSeek V3.2 is text->text only, with no image input or output support.
- How much bigger is Scout's context window?
- Llama 4 Scout supports 10,000,000 tokens of context vs. DeepSeek V3.2's 131,072 — V3.2's window is about 1% the size of Scout's, per the computed context ratio of 0.01.
- Which model wins on coding benchmarks?
- DeepSeek V3.2, decisively. On Design Arena's codecategories benchmark it scores 1213 Elo (rank 48) against Llama 4 Scout's 839 Elo (rank 106) — a roughly 374-point gap. Both models are available on OpenKey through one API key with a flat 3% fee over provider pricing.