Pick MiMo-V2.5 for native omnimodal — strong image and video understanding or very low cost (~half the inference of the pro tier). Pick Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine. On a tight budget at scale, Muse Glimmer is the value pick.
MiMo-V2.5 (Xiaomi, China) and Muse Glimmer (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. MiMo-V2.5 is xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost. Muse Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: MiMo-V2.5 holds 7.6× more — 1M (~1,500 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Muse Glimmer is the newer model by about 4 months (released August 10, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
MiMo-V2.5
Muse Glimmer
Provider
Xiaomi (China)
Meta (US)
Released
April 22, 2026
August 10, 2026
Context window
1M (~1,500 pages)
128K (~197 pages)
Price (in/out)
$0.14/$0.28 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Native omnimodal — strong image and video understanding: MiMo-V2.5 — Xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost — and it carries the larger 1M context.
Very low cost (~half the inference of the Pro tier): MiMo-V2.5 — MiMo-V2.5 lists very low cost (~half the inference of the Pro tier) among its strengths; Muse Glimmer does not.
Agent-framework integration: MiMo-V2.5 — MiMo-V2.5 lists agent-framework integration among its strengths; Muse Glimmer does not.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit: Muse Glimmer — Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine: Muse Glimmer — Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; MiMo-V2.5 does not.
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery: Muse Glimmer — Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; MiMo-V2.5 does not.
Lowest cost at scale: Muse Glimmer — Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.5's $0.14/$0.28 per 1M tokens.
Largest single-prompt input: MiMo-V2.5 — Its 1M window is about 7.6× larger than Muse Glimmer's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Muse Glimmer — 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 native omnimodal — strong image and video understanding: MiMo-V2.5 — It is specifically built for that.
Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit: Muse Glimmer — That is its strongest area.
An enterprise with regional data-residency rules: Muse Glimmer or MiMo-V2.5 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Muse Glimmer: where it fits
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.
Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." MiMo-V2.5 (China) and Muse Glimmer (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Muse Glimmer 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 MiMo-V2.5 or Muse Glimmer 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, MiMo-V2.5 leans toward native omnimodal — strong image and video understanding while Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MiMo-V2.5 or Muse Glimmer?
Muse Glimmer is cheaper — $0.14/$0.28 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
MiMo-V2.5 — 1M vs 128K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiMo-V2.5 and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you MiMo-V2.5, Muse Glimmer 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, MiMo-V2.5 or Muse Glimmer?
Muse Glimmer — released August 10, 2026, about 4 months after MiMo-V2.5.
MiMo-V2.5 vs Muse Glimmer
Xiaomi · China | Meta · US · Updated June 2026
Quick verdict
Pick MiMo-V2.5 for native omnimodal — strong image and video understanding or very low cost (~half the inference of the pro tier). Pick Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine. On a tight budget at scale, Muse Glimmer is the value pick.
MiMo-V2.5 (Xiaomi, China) and Muse Glimmer (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. MiMo-V2.5 is xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost. Muse Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. 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 7.6× more — 1M (~1,500 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Muse Glimmer is the newer model by about 4 months (released August 10, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
MiMo-V2.5
Muse Glimmer
Provider
Xiaomi (China)
Meta (US)
Released
April 22, 2026
August 10, 2026
Context window
1M (~1,500 pages)
128K (~197 pages)
Price (in/out)
$0.14/$0.28 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Native omnimodal — strong image and video understanding
MiMo-V2.5
Xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost — and it carries the larger 1M context.
Very low cost (~half the inference of the Pro tier)
MiMo-V2.5
MiMo-V2.5 lists very low cost (~half the inference of the Pro tier) among its strengths; Muse Glimmer does not.
Agent-framework integration
MiMo-V2.5
MiMo-V2.5 lists agent-framework integration among its strengths; Muse Glimmer does not.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit
Muse Glimmer
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine
Muse Glimmer
Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; MiMo-V2.5 does not.
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery
Muse Glimmer
Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; MiMo-V2.5 does not.
Lowest cost at scale
Muse Glimmer
Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.5's $0.14/$0.28 per 1M tokens.
Largest single-prompt input
MiMo-V2.5
Its 1M window is about 7.6× larger than Muse Glimmer's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Muse Glimmer
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 native omnimodal — strong image and video understanding
→ MiMo-V2.5
It is specifically built for that.
Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit
→ Muse Glimmer
That is its strongest area.
An enterprise with regional data-residency rules
→ Muse Glimmer or MiMo-V2.5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Muse Glimmer: where it fits
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.
Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." MiMo-V2.5 (China) and Muse Glimmer (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Muse Glimmer 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 MiMo-V2.5 and Muse Glimmer 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, MiMo-V2.5 leans toward native omnimodal — strong image and video understanding while Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MiMo-V2.5 or Muse Glimmer?
Muse Glimmer is cheaper — $0.14/$0.28 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
MiMo-V2.5 — 1M vs 128K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiMo-V2.5 and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you MiMo-V2.5, Muse Glimmer 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, MiMo-V2.5 or Muse Glimmer?
Muse Glimmer — released August 10, 2026, about 4 months after MiMo-V2.5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.