Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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, MiMo-V2.5 is the value pick.
DeepSeek R1 (DeepSeek) and MiMo-V2.5 (Xiaomi) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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
Price: MiMo-V2.5 is about 3.9× cheaper on input ($0.14/$0.28 per 1M tokens vs $0.55/$2.19 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: MiMo-V2.5 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: MiMo-V2.5 is the newer model by about 15 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
MiMo-V2.5
Provider
DeepSeek (China)
Xiaomi (China)
Released
January 2025
April 22, 2026
Context window
128K (~192 pages)
1M (~1,500 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.14/$0.28 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, audio, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model: DeepSeek R1 — A core design strength of DeepSeek R1.
Transparent chain-of-thought: DeepSeek R1 — A core design strength of DeepSeek R1.
Low cost: DeepSeek R1 — A core design strength of DeepSeek R1.
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: MiMo-V2.5 — At $0.14/$0.28 per 1M tokens, 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 7.8× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MiMo-V2.5 — At $0.14/$0.28 per 1M tokens it undercuts DeepSeek R1, 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 open-weight reasoning model: DeepSeek R1 — 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.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
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
DeepSeek R1 and MiMo-V2.5 overlap enough that the right pick depends on your specific job. MiMo-V2.5 costs less per token; MiMo-V2.5 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, MiMo-V2.5 for native omnimodal — strong image and video understanding. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek R1 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, DeepSeek R1 leans toward open-weight reasoning model 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, DeepSeek R1 or MiMo-V2.5?
MiMo-V2.5 is cheaper — $0.55/$2.19 per 1M tokens vs $0.14/$0.28 per 1M tokens, roughly 3.9× apart on input.
Which has the bigger context window?
MiMo-V2.5 — 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 DeepSeek R1 and MiMo-V2.5 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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, DeepSeek R1 or MiMo-V2.5?
MiMo-V2.5 — released April 22, 2026, about 15 months after DeepSeek R1.
DeepSeek R1 vs MiMo-V2.5
DeepSeek · China | Xiaomi · China · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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, MiMo-V2.5 is the value pick.
DeepSeek R1 (DeepSeek) and MiMo-V2.5 (Xiaomi) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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
▸Price: MiMo-V2.5 is about 3.9× cheaper on input ($0.14/$0.28 per 1M tokens vs $0.55/$2.19 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: MiMo-V2.5 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: MiMo-V2.5 is the newer model by about 15 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
MiMo-V2.5
Provider
DeepSeek (China)
Xiaomi (China)
Released
January 2025
April 22, 2026
Context window
128K (~192 pages)
1M (~1,500 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.14/$0.28 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, audio, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model
DeepSeek R1
A core design strength of DeepSeek R1.
Transparent chain-of-thought
DeepSeek R1
A core design strength of DeepSeek R1.
Low cost
DeepSeek R1
A core design strength of DeepSeek R1.
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
MiMo-V2.5
At $0.14/$0.28 per 1M tokens, 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 7.8× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MiMo-V2.5
At $0.14/$0.28 per 1M tokens it undercuts DeepSeek R1, 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 open-weight reasoning model
→ DeepSeek R1
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.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
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
DeepSeek R1 and MiMo-V2.5 overlap enough that the right pick depends on your specific job. MiMo-V2.5 costs less per token; MiMo-V2.5 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, MiMo-V2.5 for native omnimodal — strong image and video understanding. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek R1 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, DeepSeek R1 leans toward open-weight reasoning model 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, DeepSeek R1 or MiMo-V2.5?
MiMo-V2.5 is cheaper — $0.55/$2.19 per 1M tokens vs $0.14/$0.28 per 1M tokens, roughly 3.9× apart on input.
Which has the bigger context window?
MiMo-V2.5 — 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 DeepSeek R1 and MiMo-V2.5 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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, DeepSeek R1 or MiMo-V2.5?
MiMo-V2.5 — released April 22, 2026, about 15 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.