Kimi K3 vs MiMo-V2.5
Moonshot AI · China | Xiaomi · China · Updated June 2026
Quick verdict
Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). 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.
Kimi K3 (Moonshot AI) and MiMo-V2.5 (Xiaomi) are two of the models people most often weigh against each other in 2026. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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 21× cheaper on input ($0.14/$0.28 per 1M tokens vs $3/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Kimi K3 is the newer model by about 3 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Kimi K3 | MiMo-V2.5 |
|---|---|---|
| Provider | Moonshot AI (China) | Xiaomi (China) |
| Released | July 27, 2026 | April 22, 2026 |
| Context window | 1M (~1,573 pages) | 1M (~1,500 pages) |
| Price (in/out) | $3/$15 per 1M tokens | $0.14/$0.28 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, video, code | text, image, audio, video, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it is the newer of the two.
1M-token context with native vision (text, image and video)
Kimi K3
Kimi K3 lists 1M-token context with native vision (text, image and video) among its strengths; MiMo-V2.5 does not.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
Kimi K3 lists vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness among its strengths; MiMo-V2.5 does not.
Native omnimodal — strong image and video understanding
MiMo-V2.5
Kimi K3 is comparatively weak here — image input but no audio or video
Very low cost (~half the inference of the Pro tier)
MiMo-V2.5
At $0.14/$0.28 per 1M tokens it undercuts Kimi K3 ($3/$15 per 1M tokens), and that gap compounds at volume.
Agent-framework integration
MiMo-V2.5
Xiaomi's cheap omnimodal model — Pro-level agentic perception across image and video at a fraction of the cost — and it runs cheaper at $0.14/$0.28 per 1M tokens.
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.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MiMo-V2.5
At $0.14/$0.28 per 1M tokens it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K3
Larger 1M window fits more in one prompt.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable
→ Kimi K3
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.
Kimi K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid 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
Kimi K3 and MiMo-V2.5 overlap enough that the right pick depends on your specific job. MiMo-V2.5 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable, 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 Kimi K3 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.
See pricingFrequently asked questions
Is Kimi K3 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, Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable 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, Kimi K3 or MiMo-V2.5?
MiMo-V2.5 is cheaper — $3/$15 per 1M tokens vs $0.14/$0.28 per 1M tokens, roughly 21× apart on input.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Kimi K3 and MiMo-V2.5 together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, 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, Kimi K3 or MiMo-V2.5?
Kimi K3 — released July 27, 2026, about 3 months after MiMo-V2.5.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.