Llama 4 Maverick vs GPT-5.2
Llama 4 Maverick and GPT-5.2 sit at opposite ends of the cost-vs-capability spectrum. Maverick is Meta's mixture-of-experts model (17B active params, 128 experts) built for cheap, high-throughput multimodal inference at a 1,048,576-token context window. GPT-5.2 is OpenAI's frontier model with adaptive reasoning effort and native agentic benchmarks, but a 400,000-token context and a list price roughly 12x higher per input token. Both are available on OpenKey with one API key and a flat 3% fee on provider list price.
Spec vs spec
| Spec | Llama 4 Maverick | GPT-5.2 |
|---|---|---|
| Context window | 1.0M | 400K |
| Max output | 16K | 128K |
| Input modalities | text, image | file, image, text |
| Output modalities | text | text |
| Knowledge cutoff | Aug 31, 2024 | — |
| Released | Apr 5, 2025 | Dec 10, 2025 |
| Reasoning | — | optional |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
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%
openai/gpt-5.2
Input · 1M tokens
$1.75 + 3%$1.80
Output · 1M tokens
$14.00 + 3%$14.42
Cache read · 1M tokens
$0.175 + 3%$0.180
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
meta-llama/llama-4-maverickCheaper
$2.78
$2.70 provider + 3%
openai/gpt-5.2
$46.87
$45.50 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| Llama 4 Maverick | GPT-5.2 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 976 | #91 | 1156 | #60 |
| Code | 929 | #102 | 1219 | #42 |
| Data viz | 926 | #100 | 1245 | #31 |
| Game dev | 903 | #103 | 1260 | #29 |
| UI components | 955 | #94 | 1243 | #32 |
| Websites | 914 | #105 | 1237 | #34 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
Provider list price: Maverick charges $0.15/M input and $0.60/M output; GPT-5.2 charges $1.75/M input and $14.00/M output. On OpenKey that becomes Maverick at $0.1545/M input and $0.618/M output (price x 1.03), and GPT-5.2 at $1.8025/M input and $14.42/M output (price x 1.03).
Run the numbers on a 10M-input / 2M-output workload — the kind of batch job you'd run for a document pipeline or eval sweep — and Maverick costs **$2.70** total versus **$45.50** for GPT-5.2. That's a 16.9x gap driven mostly by input price (Maverick is 0.09x GPT-5.2's input rate, per the computed ratio). If you're processing large corpora and don't need frontier reasoning, this difference compounds fast across millions of calls.
Coding and agentic performance
On Design Arena's shared categories, GPT-5.2 outscores Maverick everywhere both were tested: codecategories (1219 elo, rank 42 vs. 929 elo, rank 102), dataviz (1245 vs. 926), gamedev (1260 vs. 903), uicomponent (1243 vs. 955), website (1237 vs. 914), and 3d (1156 vs. 976). GPT-5.2 also has agent-specific benchmarks Maverick has no equivalent for — fullstack (1110 elo, rank 21), webapps (1156 elo, rank 19), mobileapps (1173 elo, rank 23), androidnative (1074 elo, rank 22), and godotgamedev (1183 elo, rank 13). Artificial Analysis puts Maverick's coding index at 16.3 and agentic index at just 1.3, reinforcing that Maverick is not built for multi-step agent tasks. If your workload is an autonomous coding agent or a fullstack build loop, GPT-5.2 is the only one of the two designed for it.
Context window and long documents
Maverick's context window is 1,048,576 tokens — 2.62x larger than GPT-5.2's 400,000 tokens, per the computed context ratio. For dumping an entire codebase or a stack of long PDFs into one call, Maverick has more headroom. GPT-5.2 compensates with a much larger max completion (128,000 tokens vs. Maverick's 16,384), so if the job is 'read a modest amount, write a long, structured answer,' GPT-5.2's output ceiling matters more than Maverick's input ceiling.
Modality and reasoning controls
Maverick accepts text and image input and supports classic sampling controls (`temperature`, `top_p`, `top_k`, `logit_bias`, `min_p`, `logprobs`) — useful if you need deterministic or fine-tuned decoding. GPT-5.2 adds file input on top of text and image, and exposes `reasoning` as a first-class parameter with five effort levels (`xhigh`, `high`, `medium`, `low`, `none`, defaulting to `medium`), letting you dial compute up for hard problems and down for cheap ones. GPT-5.2 has no knowledge cutoff listed in this data; Maverick's cutoff is 2024-08-31.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Bulk document processing / large corpora | Llama 4 Maverick | 1,048,576-token context and $2.70 for the 10M-in/2M-out workload vs $45.50 |
| Autonomous coding agents (fullstack, mobile, web) | GPT-5.2 | Only model with agent benchmarks — fullstack rank 21, webapps rank 19, mobileapps rank 23 |
| UI component / dataviz generation | GPT-5.2 | 1243 elo on uicomponent and 1245 elo on dataviz vs Maverick's 955 and 926 |
| Cost-sensitive image+text batch jobs | Llama 4 Maverick | Input price is 0.09x GPT-5.2's — roughly 17x cheaper per input token |
| Tasks needing file uploads directly | GPT-5.2 | Supports file input modality; Maverick only accepts text and image |
| Variable-compute reasoning tasks | GPT-5.2 | Exposes 5 reasoning effort levels (xhigh to none) for tuning cost vs quality per call |
Questions
- How much cheaper is Llama 4 Maverick than GPT-5.2?
- On OpenKey, Maverick costs $0.1545/M input and $0.618/M output; GPT-5.2 costs $1.8025/M input and $14.42/M output (both provider price x 1.03 for the 3% fee). On a 10M-in/2M-out workload that's $2.70 for Maverick versus $45.50 for GPT-5.2 — a 16.9x difference.
- Which model has the bigger context window?
- Maverick supports 1,048,576 tokens of context, 2.62x larger than GPT-5.2's 400,000 tokens. GPT-5.2 wins on output instead, with a 128,000-token max completion versus Maverick's 16,384.
- Is GPT-5.2 actually better at coding?
- Yes, by a wide margin on Design Arena: GPT-5.2 scores 1219 elo (rank 42) on codecategories versus Maverick's 929 elo (rank 102). Artificial Analysis also puts Maverick's coding index at just 16.3 and agentic index at 1.3, well below what agent workloads need.
- Can I use both models with the same API setup?
- Yes — both Llama 4 Maverick and GPT-5.2 are available on OpenKey through one API key, billed at provider list price plus a flat 3% fee, so you can switch between them per-request without separate accounts.