Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding.
Gemma 4 (Google) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: Llama 4 Maverick holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Gemma 4 is the newer model by about 12 months (released April 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemma 4
Llama 4 Maverick
Provider
Google (US)
Meta (US)
Released
April 2, 2026
April 2025
Context window
256K (~384 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hosted, data-private deployment: Gemma 4 — A core design strength of Gemma 4.
Running locally or on edge devices: Gemma 4 — A core design strength of Gemma 4.
Fine-tuning on your own data: Gemma 4 — A core design strength of Gemma 4.
Open weights, 1M context: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Strong image + text understanding: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Self-hostable: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Largest single-prompt input: Llama 4 Maverick — Its 1M window is about 3.9× larger, 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.
Anyone whose priority is self-hosted, data-private deployment: Gemma 4 — It is specifically built for that.
Anyone whose priority is open weights, 1m context: Llama 4 Maverick — That is its strongest area.
Gemma 4: where it fits
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.
Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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: 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.
The bottom line for this matchup
Gemma 4 and Llama 4 Maverick overlap enough that the right pick depends on your specific job. Llama 4 Maverick holds the larger context; and each leads in its own area — Gemma 4 for self-hosted, data-private deployment, Llama 4 Maverick for open weights, 1m context. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Gemma 4 or Llama 4 Maverick 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, Gemma 4 leans toward self-hosted, data-private deployment while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or Llama 4 Maverick?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Llama 4 Maverick — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemma 4 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, Llama 4 Maverick 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, Gemma 4 or Llama 4 Maverick?
Gemma 4 — released April 2, 2026, about 12 months after Llama 4 Maverick.
Gemma 4 vs Llama 4 Maverick
Google · US | Meta · US · Updated June 2026
Quick verdict
Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding.
Gemma 4 (Google) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: Llama 4 Maverick holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Gemma 4 is the newer model by about 12 months (released April 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemma 4
Llama 4 Maverick
Provider
Google (US)
Meta (US)
Released
April 2, 2026
April 2025
Context window
256K (~384 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hosted, data-private deployment
Gemma 4
A core design strength of Gemma 4.
Running locally or on edge devices
Gemma 4
A core design strength of Gemma 4.
Fine-tuning on your own data
Gemma 4
A core design strength of Gemma 4.
Open weights, 1M context
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Strong image + text understanding
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Self-hostable
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Largest single-prompt input
Llama 4 Maverick
Its 1M window is about 3.9× larger, 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.
Anyone whose priority is self-hosted, data-private deployment
→ Gemma 4
It is specifically built for that.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
That is its strongest area.
Gemma 4: where it fits
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.
Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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: 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.
The bottom line for this matchup
Gemma 4 and Llama 4 Maverick overlap enough that the right pick depends on your specific job. Llama 4 Maverick holds the larger context; and each leads in its own area — Gemma 4 for self-hosted, data-private deployment, Llama 4 Maverick for open weights, 1m context. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Gemma 4 and Llama 4 Maverick 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.
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, Gemma 4 leans toward self-hosted, data-private deployment while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or Llama 4 Maverick?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Llama 4 Maverick — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemma 4 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, Llama 4 Maverick 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, Gemma 4 or Llama 4 Maverick?
Gemma 4 — released April 2, 2026, about 12 months after Llama 4 Maverick.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.