Open weights — run on your own infrastructure or fine-tune
Balanced enough for many general workloads
Best option when vendor lock-in is a concern
Weaknesses
Quality depends heavily on deployment setup and hardware
No significant lead over hosted models in any single benchmark category
Real-world use cases
What people actually use Llama 4 Maverick for.
Running open-weight AI on self-hosted infrastructure with full data control
Fine-tuning for domain-specific use cases in regulated industries
General-purpose tasks in environments with strict data residency requirements
How Llama 4 Maverick compares
The nearest models people weigh against it, and what actually separates them.
vs Llama 3.1 70B Instruct — Against Llama 3.1 70B Instruct (Meta), Llama 4 Maverick costs about 64% more per token and takes 2x the context. Llama 3.1 70B Instruct is the one to check first if the price difference matters more than the ceiling.
vs Llama 3.2 11B Vision Instruct — Against Llama 3.2 11B Vision Instruct (Meta), Llama 4 Maverick costs about 69% more per token and takes 2x the context. Llama 3.2 11B Vision Instruct is the one to check first if the price difference matters more than the ceiling.
vs Claude 3.5 Haiku — Against Claude 3.5 Haiku (Anthropic), Llama 4 Maverick runs about 54% cheaper per token, takes 1.3x the context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
Price History
Llama 4 Maverick pricing over time
→0% since May 8
39 data points · tracked daily since May 8, 2026
Ready to try it?
Start using Llama 4 Maverick
Flexible self-hosted deployments and mixed general workloads. Start free — no card required.
Meta's Llama 3.1 70B Instruct is a open-weight large language model with 70 billion parameters, fine-tuned for instruction following across coding, reasoning, and general-purpose tasks. It offers a strong balance of capability and cost at $0.40/1M tokens for both input and output.
Verdict
The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing.
Quality score
65%
Pricing
$0.40/1M in
$0.40/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Pricing shown is via third-party API providers (e.g., OpenRouter, Together AI) — costs may vary. Meta releases Llama 3.1 weights publicly, enabling self-hosting at even lower cost. Not available directly from Meta as a hosted API.
Llama 3.2 11B Vision Instruct is Meta's open-weight multimodal model capable of understanding both text and images at an extremely low price point. It handles image captioning, visual question answering, and document analysis alongside standard text tasks.
Verdict
The go-to vision model when budget is the top constraint and good-enough accuracy is acceptable.
Quality score
57%
Pricing
$0.34/1M in
$0.34/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Available via multiple inference providers including Together AI, Fireworks, and OpenRouter. As an open-weight model, it can also be self-hosted for even lower marginal costs at scale. Part of Meta's Llama 3.2 family which also includes a 90B vision variant for heavier workloads.
Open-weightVisionBudgetMultimodalMeta
Best for
Budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.
Claude 3.5 Haiku is Anthropic's fastest and most affordable model in the Claude 3.5 family, designed for high-throughput tasks requiring quick responses without sacrificing Claude's core instruction-following quality. It handles a massive 200K context window while maintaining speed suitable for production pipelines.
Verdict
The fastest way to get Claude's quality in production — just don't confuse 'fast' with 'cheap'.
Quality score
64%
Pricing
$0.80/1M in
$4.00/1M out
Speed
Very fast
5/5 speed
Context
200k tokens
Output cost of $4/1M is notably higher than competing fast/mini models. Input cost at ~$0.80/1M is competitive. Best value emerges in input-heavy pipelines like document classification or RAG retrieval where output tokens are minimal.
High-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
Llama 4 Maverick costs $0.6 per million input tokens and $1.6 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $9.20 at list price, before any batch or caching discounts.
What is the context window of Llama 4 Maverick?
Llama 4 Maverick has a 128k tokens context window, with up to 8k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
What is the knowledge cutoff of Llama 4 Maverick?
Llama 4 Maverick's training data runs through August 2024, and the model was released on April 5, 2025. For anything after that date it needs web search or documents in the prompt.
What is Llama 4 Maverick best for?
Llama 4 Maverick is best for flexible self-hosted deployments and mixed general workloads. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid Llama 4 Maverick?
You want the strongest hosted answer quality — closed frontier models win on benchmarks.
What is a cheaper alternative to Llama 4 Maverick?
Llama 3.1 70B Instruct (Meta) at $0.40/1M/1M input against Llama 4 Maverick's $0.60/1M/1M — roughly 64% less per token all in. The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing. Compare it first if Llama 4 Maverick's pricing is the thing stopping you.
What is a faster alternative to Llama 4 Maverick?
Claude 3.5 Haiku — very fast against Llama 4 Maverick's fast, with 200k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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