Both are Google models. Gemma 4 is the newer, generally stronger default; reach for Gemini 3.1 Pro when a specific cost or latency profile matters more than the latest capabilities.
Gemini 3.1 Pro and Gemma 4 are both Google models, so the real question is not which lab to trust but which tier fits your workload and budget. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Cost model: Gemma 4 ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Gemini 3.1 Pro holds 7.8× more — 2M (~3,000 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 42 days (released April 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 3.1 Pro
Gemma 4
Provider
Google (US)
Google (US)
Released
February 19, 2026
April 2, 2026
Context window
2M (~3,000 pages)
256K (~384 pages)
Price (in/out)
$2/$12 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Long video and document analysis: Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
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.
Lowest cost at scale: Gemma 4 — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Gemini 3.1 Pro — Its 2M window is about 7.8× larger, fitting roughly 3,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Gemma 4 — At Open weight (self-host / free) it undercuts Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 3.1 Pro — Larger 2M window fits more in one prompt.
A team with data-privacy or self-hosting needs: Gemma 4 — Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is largest mainstream production context (2m): Gemini 3.1 Pro — It is specifically built for that.
Anyone whose priority is self-hosted, data-private deployment: Gemma 4 — That is its strongest area.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs are real: long-context recall drops sharply past 256K, and premium price per token. At $2 in / $12 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
Because Gemini 3.1 Pro and Gemma 4 come from the same lab (Google), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Gemma 4 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Gemma 4 and drop down only with a concrete reason.
Frequently asked questions
Is Gemini 3.1 Pro or Gemma 4 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, Gemini 3.1 Pro leans toward largest mainstream production context (2m) while Gemma 4 leans toward self-hosted, data-private deployment, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or Gemma 4?
Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. 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?
Gemini 3.1 Pro — 2M vs 256K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Gemini 3.1 Pro to Gemma 4?
Since both are Google models, the newer one (Gemma 4) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Gemini 3.1 Pro or Gemma 4?
Gemma 4 — released April 2, 2026, about 42 days after Gemini 3.1 Pro.
Gemini 3.1 Pro vs Gemma 4
Google · US | Google · US · Updated June 2026
Quick verdict
Both are Google models. Gemma 4 is the newer, generally stronger default; reach for Gemini 3.1 Pro when a specific cost or latency profile matters more than the latest capabilities.
Gemini 3.1 Pro and Gemma 4 are both Google models, so the real question is not which lab to trust but which tier fits your workload and budget. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Cost model: Gemma 4 ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Gemini 3.1 Pro holds 7.8× more — 2M (~3,000 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 42 days (released April 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 3.1 Pro
Gemma 4
Provider
Google (US)
Google (US)
Released
February 19, 2026
April 2, 2026
Context window
2M (~3,000 pages)
256K (~384 pages)
Price (in/out)
$2/$12 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Long video and document analysis
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
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.
Lowest cost at scale
Gemma 4
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Gemini 3.1 Pro
Its 2M window is about 7.8× larger, fitting roughly 3,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Gemma 4
At Open weight (self-host / free) it undercuts Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 3.1 Pro
Larger 2M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Gemma 4
Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is largest mainstream production context (2m)
→ Gemini 3.1 Pro
It is specifically built for that.
Anyone whose priority is self-hosted, data-private deployment
→ Gemma 4
That is its strongest area.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs are real: long-context recall drops sharply past 256K, and premium price per token. At $2 in / $12 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
Because Gemini 3.1 Pro and Gemma 4 come from the same lab (Google), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Gemma 4 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Gemma 4 and drop down only with a concrete reason.
Want both Gemini 3.1 Pro and Gemma 4 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, Gemini 3.1 Pro leans toward largest mainstream production context (2m) while Gemma 4 leans toward self-hosted, data-private deployment, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or Gemma 4?
Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. 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?
Gemini 3.1 Pro — 2M vs 256K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Gemini 3.1 Pro to Gemma 4?
Since both are Google models, the newer one (Gemma 4) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Gemini 3.1 Pro or Gemma 4?
Gemma 4 — released April 2, 2026, about 42 days after Gemini 3.1 Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.