Llama 4 Maverick is a high-capacity Mixture-of-Experts (MoE) model from Meta, featuring 400B total parameters and 128 experts, while activating an efficient 17B parameters per inference. Engineered for peak performance, it excels at advanced multimodal tasks. Maverick natively supports text and image input, producing multilingual text and code. With a 1-million-token context window and instruction tuning, it is optimized for complex image reasoning and general-purpose assistant-like interactions. Released under the Llama 4 Community License, Maverick is ideal for research and commercial applications demanding state-of-the-art multimodal understanding and high throughput.
llama-4-maverick vs llama-4-scout
Output tokens cost $0.20 per million on llama-4-maverick and $0.20 per million on llama-4-scout. Input tokens cost $0.20 per million on llama-4-maverick and $0.20 per million on llama-4-scout. Cached input tokens are billed at $0.20 per million on llama-4-maverick and $0.20 per million on llama-4-scout. Context lengths are 1,048,576 tokens on llama-4-maverick and 131,000 tokens on llama-4-scout. Time to first token (TTFT) measured on AIHubMix is 0.2s on llama-4-maverick and 0.3s on llama-4-scout. Measured output throughput is 97.8 tok/s on llama-4-maverick and 2637.0 tok/s on llama-4-scout. On the LMArena coding leaderboard llama-4-maverick scores 1373 and llama-4-scout scores 1362.
Llama 4 Scout is a highly efficient Mixture-of-Experts (MoE) model from Meta, activating 17B out of 109B total parameters per inference. It natively supports multimodal input (text and image) and multilingual output (text and code) across 12 languages. Designed for assistant-style interaction and visual reasoning, Scout features a massive 10-million-token context window. It is instruction-tuned for tasks like multilingual chat and image understanding and is released under the Llama 4 Community License for local or commercial deployment.
Pricing & Specifications
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
Promotional prices show the discounted rate; see each model page for promotion windows.
Activity Past 30 Days
Daily traffic served through AIHubMix — how demand for each model is trending.
Tokens / day
Requests / day
Performance Past 3 Days
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
Throughput (tok/s)
TTFT (s)
Uptime (%)
LMArena Benchmarks
LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.
Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.
Cost calculator
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
FAQ
Which is cheaper: llama-4-maverick, llama-4-scout?
llama-4-maverick: $0.20/M output tokens; llama-4-scout: $0.20/M. Use the cost calculator above to estimate your own workload.
How do their coding arena scores compare?
llama-4-maverick: 1373; llama-4-scout: 1362 (LMArena coding leaderboard).
Which responds faster?
llama-4-maverick: 0.2s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
How large is each context window?
llama-4-maverick accepts 1,048,576 and llama-4-scout accepts 131,000 input tokens. Maximum output per request is 32,000 tokens on llama-4-maverick and 131,000 tokens on llama-4-scout.
Which one generates tokens faster?
llama-4-scout at 2637.0 tok/s and llama-4-maverick at 97.8 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
What inputs and capabilities does each model support?
llama-4-maverick accepts text and image input and supports tool calling, function calling and structured outputs; llama-4-scout accepts text and image input and supports tool calling, function calling and structured outputs.
Can I call llama-4-maverick and llama-4-scout with the same API key?
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
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