Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick MiMo-V2.5 for native omnimodal — strong image and video understanding or very low cost (~half the inference of the pro tier). On a tight budget at scale, Gemma 4 is the value pick.
Gemma 4 (Google, US) and MiMo-V2.5 (Xiaomi, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. MiMo-V2.5 is xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: MiMo-V2.5 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: MiMo-V2.5 is the newer model by about 20 days (released April 22, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Gemma 4
MiMo-V2.5
Provider
Google (US)
Xiaomi (China)
Released
April 2, 2026
April 22, 2026
Context window
256K (~384 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$0.14/$0.28 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, audio, video, 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.
Native omnimodal — strong image and video understanding: MiMo-V2.5 — A core design strength of MiMo-V2.5.
Very low cost (~half the inference of the Pro tier): MiMo-V2.5 — A core design strength of MiMo-V2.5.
Agent-framework integration: MiMo-V2.5 — A core design strength of MiMo-V2.5.
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: MiMo-V2.5 — Its 1M window is about 3.9× larger, fitting roughly 1,500 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 MiMo-V2.5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MiMo-V2.5 — 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 native omnimodal — strong image and video understanding: MiMo-V2.5 — That is its strongest area.
An enterprise with regional data-residency rules: Gemma 4 or MiMo-V2.5 — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
MiMo-V2.5: where it fits
Xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost. Released April 22, 2026 by Xiaomi, it is built for native omnimodal — strong image and video understanding, very low cost (~half the inference of the Pro tier), agent-framework integration, and 1M context for full documents in one pass.
Its trade-offs: not the deepest reasoning tier (see V2.5-Pro), and limited Western tooling and support. At $0.14 in / $0.28 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Gemma 4 (US) and MiMo-V2.5 (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Gemma 4 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is Gemma 4 or MiMo-V2.5 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 MiMo-V2.5 leans toward native omnimodal — strong image and video understanding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or MiMo-V2.5?
Gemma 4 is cheaper — Open weight (self-host / free) vs $0.14/$0.28 per 1M tokens.
Which has the bigger context window?
MiMo-V2.5 — 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 MiMo-V2.5 together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, MiMo-V2.5 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 MiMo-V2.5?
MiMo-V2.5 — released April 22, 2026, about 20 days after Gemma 4.
Gemma 4 vs MiMo-V2.5
Google · US | Xiaomi · China · Updated June 2026
Quick verdict
Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick MiMo-V2.5 for native omnimodal — strong image and video understanding or very low cost (~half the inference of the pro tier). On a tight budget at scale, Gemma 4 is the value pick.
Gemma 4 (Google, US) and MiMo-V2.5 (Xiaomi, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. MiMo-V2.5 is xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: MiMo-V2.5 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: MiMo-V2.5 is the newer model by about 20 days (released April 22, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Gemma 4
MiMo-V2.5
Provider
Google (US)
Xiaomi (China)
Released
April 2, 2026
April 22, 2026
Context window
256K (~384 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$0.14/$0.28 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, audio, video, 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.
Native omnimodal — strong image and video understanding
MiMo-V2.5
A core design strength of MiMo-V2.5.
Very low cost (~half the inference of the Pro tier)
MiMo-V2.5
A core design strength of MiMo-V2.5.
Agent-framework integration
MiMo-V2.5
A core design strength of MiMo-V2.5.
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
MiMo-V2.5
Its 1M window is about 3.9× larger, fitting roughly 1,500 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 MiMo-V2.5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiMo-V2.5
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 native omnimodal — strong image and video understanding
→ MiMo-V2.5
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemma 4 or MiMo-V2.5
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
MiMo-V2.5: where it fits
Xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost. Released April 22, 2026 by Xiaomi, it is built for native omnimodal — strong image and video understanding, very low cost (~half the inference of the Pro tier), agent-framework integration, and 1M context for full documents in one pass.
Its trade-offs: not the deepest reasoning tier (see V2.5-Pro), and limited Western tooling and support. At $0.14 in / $0.28 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Gemma 4 (US) and MiMo-V2.5 (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Gemma 4 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Gemma 4 and MiMo-V2.5 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 MiMo-V2.5 leans toward native omnimodal — strong image and video understanding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or MiMo-V2.5?
Gemma 4 is cheaper — Open weight (self-host / free) vs $0.14/$0.28 per 1M tokens.
Which has the bigger context window?
MiMo-V2.5 — 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 MiMo-V2.5 together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, MiMo-V2.5 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 MiMo-V2.5?
MiMo-V2.5 — released April 22, 2026, about 20 days after Gemma 4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.