DeepSeek V3.2 vs Llama 4 Maverick
DeepSeek V3.2 (released December 2025) and Llama 4 Maverick (April 2025, Meta's 17B-active MoE with 128 experts) sit at different points on the price/capability curve. V3.2 leans into sparse attention and reasoning support; Maverick leans into a huge 1M+ token context window and native image input. Both run on OpenKey with one API key and a flat 3% fee on top of provider list price — the comparison below is what actually changes when you pick one over the other.
Spec vs spec
| Spec | DeepSeek V3.2 | Llama 4 Maverick |
|---|---|---|
| Context window | 131K | 1.0M |
| 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-maverick
Input · 1M tokens
$0.150 + 3%$0.154
Output · 1M tokens
$0.600 + 3%$0.618
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-maverickCheaper
$2.78
$2.70 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| DeepSeek V3.2 | Llama 4 Maverick | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1210 | #41 | 976 | #91 |
| Code | 1213 | #48 | 929 | #102 |
| Data viz | 1203 | #48 | 926 | #100 |
| Game dev | 1197 | #50 | 903 | #103 |
| UI components | 1203 | #47 | 955 | #94 |
| Websites | 1217 | #46 | 914 | #105 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
Provider list price: DeepSeek V3.2 is $0.2288/M input and $0.3432/M output; on OpenKey that's $0.2288 × 1.03 = $0.235664/M input and $0.3432 × 1.03 = $0.353496/M output. Llama 4 Maverick is $0.15/M input and $0.60/M output provider-side, or $0.1545/M and $0.618/M on OpenKey after the 3% fee. On a 10M-input/2M-output workload, DeepSeek V3.2 costs $2.97 total and Llama 4 Maverick costs $2.70. Maverick's input tokens are cheaper (input price ratio 1.53x in DeepSeek's favor for input cost), but its output rate is nearly double DeepSeek's, so output-heavy jobs shift the math back toward DeepSeek.
Design and code quality (Design Arena)
Across every shared Design Arena category, DeepSeek V3.2 outranks Llama 4 Maverick: website (1217 Elo, rank 46 vs 914 Elo, rank 105), gamedev (1197 vs 903), UI components (1203 vs 955), data viz (1203 vs 926), code categories (1213 vs 929), and 3D (1210 vs 976). These aren't close calls — the gaps run 200-300 Elo points and 40-70 ranking positions in DeepSeek's favor. Maverick does have separate Artificial Analysis scores (intelligence index 14.3, coding index 16.3, agentic index 1.3), but on the head-to-head design/code benchmark, DeepSeek V3.2 is clearly ahead.
Context window and modality
Llama 4 Maverick's context window is 1,048,576 tokens against DeepSeek V3.2's 131,072 — roughly 8x larger, or a context ratio of 0.12 in DeepSeek's favor when expressed the other way. If you're feeding in full codebases, long transcripts, or multi-document RAG contexts without heavy chunking, Maverick has real headroom V3.2 doesn't. Maverick also takes image input (text+image->text) while DeepSeek V3.2 is text-only in and out. On max output, DeepSeek V3.2 can generate up to 64,000 tokens per response versus Maverick's 16,384 — useful if you need long single-shot completions rather than long input context.
When to pick each
Pick DeepSeek V3.2 when the job is code generation, UI/website building, or agentic tool use — its supported parameters include `reasoning` and `include_reasoning`, and its Design Arena scores back up the choice. Pick Llama 4 Maverick when you need to process large documents in one pass, need image understanding, or are running an output-light, input-heavy pipeline where its lower $0.1545/M input rate keeps costs down. Neither model's cache-write pricing is available; DeepSeek V3.2 does offer cache-read at $0.02288/M provider-side, which Maverick doesn't support at all.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Coding / UI generation | DeepSeek V3.2 | Leads Maverick by 200+ Elo in codecategories (1213 vs 929) and uicomponent (1203 vs 955) |
| Long-document analysis (500K+ tokens) | Llama 4 Maverick | 1,048,576 token context vs DeepSeek's 131,072 |
| Image-in, text-out tasks | Llama 4 Maverick | Only one of the two with image input modality |
| Agentic tool-calling workflows | DeepSeek V3.2 | Supports reasoning and include_reasoning parameters; Maverick has no reasoning field |
| High-input-volume, low-output jobs on a budget | Llama 4 Maverick | $0.1545/M input on OpenKey vs DeepSeek's $0.235664/M |
| Long single-response generation | DeepSeek V3.2 | 64,000 max completion tokens vs Maverick's 16,384 |
Questions
- Which model is cheaper for a typical 10M input / 2M output workload?
- Llama 4 Maverick, at $2.70 total on OpenKey pricing versus $2.97 for DeepSeek V3.2. The gap comes from Maverick's lower input rate ($0.1545/M vs $0.235664/M), even though its output rate ($0.618/M) is nearly double DeepSeek's ($0.353496/M).
- How much bigger is Llama 4 Maverick's context window?
- Maverick supports 1,048,576 tokens of context versus DeepSeek V3.2's 131,072 — about 8x larger. That's the context_ratio of 0.12 when expressed as V3.2's context divided by Maverick's.
- Does either model support image input?
- Only Llama 4 Maverick. Its modality is text+image->text; DeepSeek V3.2 is text->text only. If your pipeline needs vision, Maverick is the only option of the two.
- Which model scores better on Design Arena coding benchmarks?
- DeepSeek V3.2, by a wide margin. In codecategories it scores 1213 Elo (rank 48) against Maverick's 929 Elo (rank 102), and the pattern repeats across all six shared categories, including website (1217 vs 914) and gamedev (1197 vs 903).