Llama 4 Maverick
Llama 4 Maverick is Meta's open-weight, natively multimodal mixture-of-experts AI model for text, image understanding, reasoning, and code generation. It has 17 billion active parameters, 128 experts, roughly 400 billion total parameters, and a one-million-token context window.
Maverick is often discussed beside Llama 4 Scout, but the two models target different constraints. Maverick allocates more total capacity to quality, while Scout is a smaller 109B-total model with a much larger advertised context window. The practical choice is therefore about workload, infrastructure, and license fit—not which name sounds more capable.
> Disclosure: This guide is published by Verdent. Third-party specifications and benchmark claims come from official public sources and may change. Test the exact checkpoint and runtime before production use.
Llama 4 Maverick vs Scout: Which to Choose
Both models activate about 17 billion parameters per token, but they have different expert counts, total sizes, and context targets.
| Decision factor | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|
| Architecture | 17B active, 128 experts, about 400B total | 17B active, 16 experts, about 109B total |
| Context window | 1M tokens | 10M tokens |
| Primary trade-off | More total model capacity | Smaller weights and extreme context length |
| Infrastructure reference | FP8 weights are described as fitting on a single H100 DGX host | Meta states Int4 can fit on one H100 GPU |
| Good evaluation target | Multimodal quality, reasoning, coding | Long-document or very-large-context retrieval |
Choose Maverick when model quality on a bounded multimodal or reasoning workload matters more than maximum context. Choose Scout when the workload genuinely benefits from very long context and the smaller total weight footprint is important.
Do not use context length as a proxy for repository understanding. Retrieval quality, prompt layout, KV-cache cost, and attention behavior all affect whether a long input produces a useful patch.
Multimodal Capabilities
Llama 4 uses early fusion for native multimodality. Maverick accepts multilingual text and image input, then generates text or code. Meta's model card lists use cases such as visual recognition, image reasoning, captioning, and visual question answering.
For software work, useful multimodal tests include:
- explaining a UI screenshot and locating likely implementation files;
- translating a wireframe into a component structure;
- reading a diagram and generating interface stubs;
- extracting acceptance criteria from an annotated bug screenshot;
- comparing a rendered page with a reference image.
The model card notes tested image-count boundaries, so a production system should not assume unlimited images. Compressing many screenshots into one prompt can also make it harder to identify which visual detail supports which code change.
Coding & SWE-bench Performance
Meta's launch materials compare Maverick across coding and reasoning benchmarks, but the official release does not establish Maverick as a dedicated SWE-bench coding agent. A base model benchmark also does not describe the behavior of every surrounding agent.
For repository evaluation, keep the scaffold fixed and score:
- file selection and repository evidence;
- correctness and scope of the diff;
- test, lint, and type-check outcomes;
- recovery after a failed command;
- long-context relevance rather than simple recall;
- total serving cost and hardware utilization.
If a public comparison cites “SWE-bench performance,” verify the exact Maverick checkpoint, harness, tool set, number of attempts, and test-time compute. Without those details, the figure should not drive deployment.
For an Apache-licensed family with more checkpoint sizes, see the Qwen3.5 guide.
Deployment Options
Maverick's open weights support self-deployment, but the 400B-total footprint makes it a server-class model. Meta provides BF16 and FP8-related artifacts, and the ecosystem supports serving through frameworks such as Transformers and vLLM. Exact compatibility depends on the checkpoint and software version.
Deployment paths include:
- a managed API or inference provider;
- a private endpoint on rented accelerators;
- an on-premises H100-class host;
- a quantized runtime after quality and latency validation.
Before self-hosting, estimate more than weight memory. Include KV cache for the intended context, runtime overhead, replication, batching, failover, observability, and engineer time. A one-million-token maximum can be technically supported while still being uneconomical for routine requests.
Use three decision gates before a trial:
| Gate | Question | Stop condition |
|---|---|---|
| License | Can the product comply with the Llama 4 Community License and acceptable-use terms? | The legal or distribution path is unresolved |
| Infrastructure | Can the serving plan support the full weight footprint, KV cache, concurrency, and failover? | The project assumes a normal consumer GPU |
| Context | Does the workload genuinely need a context window larger than smaller alternatives provide? | The repository can be handled with retrieval and a smaller model |
License
Llama 4 Maverick uses the Llama 4 Community License, not Apache 2.0. Meta calls the model open-weight, and the license permits commercial and research use subject to its terms and acceptable-use requirements.
Teams should review the exact license before deployment, especially attribution, redistribution, acceptable-use, and any scale-based provisions. “Weights available” and “open source under an OSI license” are not interchangeable claims.
A licensing review should record:
- the exact checkpoint and license version;
- whether the model or a derivative will be redistributed;
- whether it will be offered as a hosted service;
- what notices must be retained;
- whether the product's intended use fits the acceptable-use policy.
This page provides a technical summary, not legal advice. Use the license packaged with the selected checkpoint as the authoritative text.
Llama 4 Maverick in Verdent
Verdent does not currently list Llama 4 Maverick among its built-in models. OpenRouter may expose a compatible endpoint to a BYOK account, but that provider-dependent path is not a native Meta integration.
A controlled Verdent trial should confirm the provider model name and context limit, validate the key, run one representative multimodal repository task, and compare the result with a current built-in model under the same checks.
Once validated, a BYOK endpoint can be selected for chat or assigned to preset, worker, and review roles. Verdent excludes Smart Suggestions and automatic context compression from key-based provider calls, so long sessions need a separate context plan.
Sources: Review the Llama 4 Community License.
Frequently Asked Questions
When should a team choose Maverick instead of Llama 4 Scout?
Choose Maverick when the larger model's quality profile fits the acceptance suite and its one-million-token context is sufficient. Choose Scout when extreme context or a smaller active-parameter footprint matters more.
Does a 1M context window mean an entire repository should be loaded?
No. A maximum context window is a capacity limit, not a retrieval strategy. Loading irrelevant files increases memory, latency, and distraction, so repository search and scoped context still matter.
Is Llama 4 Maverick licensed under Apache 2.0 or an OSI license?
Neither. Maverick uses the custom Llama 4 Community License. Teams should review the license and use policy for attribution, redistribution, scale, and acceptable-use requirements.
Why is Maverick not a typical consumer-GPU model?
Its mixture-of-experts design activates only part of the model per token, but the complete weight set still needs to be available to the serving system. That creates a server-class memory and infrastructure requirement.
Evaluate Llama 4 Maverick in Verdent
Check Verdent’s current model options, then configure a provider-routed BYOK evaluation to validate Maverick’s endpoint, context limit, and multimodal performance on your own codebase.