Llama 4 Maverick vs MAI-1-preview
Meta · US | Microsoft · US · Updated June 2026
Quick verdict
Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding. Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Choose Llama 4 Maverick if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
Llama 4 Maverick (Meta) and MAI-1-preview (Microsoft) are two of the models people most often weigh against each other in 2026. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Cost model: Llama 4 Maverick ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-1-preview is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Llama 4 Maverick holds 7.8× more — 1M (~1,500 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: MAI-1-preview is the newer model by about 5 months (released August 28, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Llama 4 Maverick | MAI-1-preview |
|---|---|---|
| Provider | Meta (US) | Microsoft (US) |
| Released | April 2025 | August 28, 2025 |
| Context window | 1M (~1,500 pages) | 128K (~192 pages) |
| Price (in/out) | Open weight (self-host / free) | Not published |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open weights, 1M context
Llama 4 Maverick
Its 1M window holds about 7.8× more than MAI-1-preview's 128K in a single prompt.
Strong image + text understanding
Llama 4 Maverick
Meta's open-weight 1M-context multimodal model for self-hosted deployments — and it carries the larger 1M context.
Self-hostable
Llama 4 Maverick
Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI
MAI-1-preview
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs — and it is the newer of the two.
Ranked in the top 15 on LM Arena at launch
MAI-1-preview
MAI-1-preview lists ranked in the top 15 on LM Arena at launch among its strengths; Llama 4 Maverick does not.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment
MAI-1-preview
MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; Llama 4 Maverick does not.
Largest single-prompt input
Llama 4 Maverick
Its 1M window is about 7.8× larger than MAI-1-preview's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Llama 4 Maverick
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Llama 4 Maverick
Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
It is specifically built for that.
Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai
→ MAI-1-preview
That is its strongest area.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs are real: needs serious hardware to self-host, and trails closed frontier on reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MAI-1-preview: where it fits
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.
Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.
The bottom line for this matchup
The defining split here is open vs. closed. Llama 4 Maverick gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-1-preview gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Llama 4 Maverick and MAI-1-preview without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Llama 4 Maverick or MAI-1-preview better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Llama 4 Maverick leans toward open weights, 1m context while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Maverick or MAI-1-preview?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-1-preview is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Llama 4 Maverick — 1M vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Maverick and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Maverick, MAI-1-preview and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Llama 4 Maverick or MAI-1-preview?
MAI-1-preview — released August 28, 2025, about 5 months after Llama 4 Maverick.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.