Llama 4 Maverick vs GPT-5.2 Pro
Llama 4 Maverick and GPT-5.2 Pro sit at opposite ends of the cost and design spectrum. Maverick is a mixture-of-experts model (128 experts, 17B active params) tuned for cheap, high-throughput multimodal inference with a 1,048,576-token context window. GPT-5.2 Pro is OpenAI's most advanced model, built around mandatory reasoning for agentic coding and long-context tasks, with a 400,000-token window and file input support Maverick lacks.
Spec vs spec
| Spec | Llama 4 Maverick | GPT-5.2 Pro |
|---|---|---|
| Context window | 1.0M | 400K |
| Max output | 16K | 128K |
| Input modalities | text, image | image, text, file |
| Output modalities | text | text |
| Knowledge cutoff | Aug 31, 2024 | — |
| Released | Apr 5, 2025 | Dec 10, 2025 |
| Reasoning | — | always on |
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-pro
Input · 1M tokens
$21.00 + 3%$21.63
Output · 1M tokens
$168.00 + 3%$173.04
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-pro
$562.38
$546.00 provider + 3%
Pricing math
Provider list price for Llama 4 Maverick is $0.15/M input and $0.60/M output tokens; on OpenKey that's $0.1545/M input and $0.618/M output after the flat 3% fee ($0.15 x 1.03, $0.60 x 1.03). GPT-5.2 Pro lists at $21.00/M input and $168.00/M output, or $21.63/M and $173.04/M on OpenKey. Run the numbers on a 10M-input/2M-output workload and Maverick costs $2.70 total while GPT-5.2 Pro costs $546.00 — a difference the computed input price ratio of 0.01 confirms (Maverick's input price is about 1% of GPT-5.2 Pro's). If your workload is cost-sensitive at scale, this gap dominates every other decision factor.
Context and modality
Maverick's context window is 1,048,576 tokens versus GPT-5.2 Pro's 400,000 — a ratio of 2.62x in Maverick's favor. That matters for ingesting large codebases, long transcripts, or multi-document RAG contexts without chunking. On modality, Maverick handles text and image input; GPT-5.2 Pro adds file input on top of text and image, which matters if your pipeline needs to hand PDFs or other file types directly to the model rather than pre-extracting text. Both output text only. Maverick's max completion is 16,384 tokens; GPT-5.2 Pro's is 128,000, useful for long generated outputs like full reports or large code diffs.
Coding and agentic benchmarks
Maverick has measured scores from Artificial Analysis: an intelligence index of 14.3, a coding index of 16.3, and an agentic index of 1.3 — modest numbers that reflect its role as a cheap, fast generalist rather than a frontier reasoner. It also has Design Arena rankings across six categories (e.g. rank 91 in 3D with a 976 elo and 40.2% win rate, rank 102 in code categories at 929 elo). GPT-5.2 Pro has no published benchmark data in this comparison, so its claimed strength in agentic coding and long-context reasoning rests on OpenAI's own positioning as its most advanced model, not on a documented score here.
When to pick each
Pick Llama 4 Maverick when you're processing high volumes of text or image input, need a large context window for long documents, and want a low, predictable per-token cost — it's built on a 128-expert MoE architecture with 17B active parameters, which keeps inference cheap. Pick GPT-5.2 Pro when the task genuinely needs OpenAI's mandatory reasoning (effort levels xhigh, high, or medium), you're feeding it files directly, or the task is complex enough that a 100x price premium on input tokens is a rounding error compared to the cost of a wrong answer. Both models run on OpenKey through one API key, billed at provider list price plus a flat 3% fee.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume batch text processing | Llama 4 Maverick | Costs $2.70 vs $546.00 for the same 10M-in/2M-out workload |
| Long-document analysis (500K+ tokens) | Llama 4 Maverick | 1,048,576-token context vs 400,000, a 2.62x ratio |
| Agentic coding with file inputs | GPT-5.2 Pro | Supports file modality and mandatory reasoning with xhigh effort |
| Multimodal image understanding on a budget | Llama 4 Maverick | $0.1545/M input on OpenKey vs $21.63/M for GPT-5.2 Pro |
| Large generated outputs (reports, big diffs) | GPT-5.2 Pro | 128,000 max completion tokens vs Maverick's 16,384 |
| Cost-capped prototyping | Llama 4 Maverick | Input price ratio of 0.01 means Maverick's input cost is about 1% of GPT-5.2 Pro's |
Questions
- How much does each model cost on OpenKey?
- Llama 4 Maverick is $0.1545 per million input tokens and $0.618 per million output tokens on OpenKey (provider price $0.15/$0.60 plus the flat 3% fee). GPT-5.2 Pro is $21.63 per million input and $173.04 per million output (provider price $21.00/$168.00 plus 3%).
- Which model has the bigger context window?
- Llama 4 Maverick supports 1,048,576 tokens of context, versus 400,000 for GPT-5.2 Pro — a 2.62x ratio. If your workload involves ingesting large codebases or long transcripts without chunking, Maverick's window is the deciding factor.
- Does GPT-5.2 Pro support image input?
- Yes. GPT-5.2 Pro accepts text, image, and file input and outputs text only. Llama 4 Maverick accepts text and image input but not file input directly, also outputting text only.
- What does a 10M-input/2M-output workload cost on each?
- Llama 4 Maverick costs $2.70 total for that workload on OpenKey. GPT-5.2 Pro costs $546.00 for the same workload — roughly 200x more, driven mainly by GPT-5.2 Pro's $168.00/M output price versus Maverick's $0.60/M.