Kimi K3 vs MiMo-V2.6-Pro
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-V2.6-Pro for natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters or 1.02 trillion total parameters, 42b active per token (sparse moe), mit-licensed and self-hostable. On a tight budget at scale, MiMo-V2.6-Pro is the value pick.
Kimi K3 (Moonshot AI) and MiMo-V2.6-Pro (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-V2.6-Pro is xiaomi's flagship omnimodal model — 1.02T parameters, native text/image/video/audio, MIT-licensed, released September 22, 2026. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Price: MiMo-V2.6-Pro is about 6.9× cheaper on input ($0.435/$0.87 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: both advertise 1M (~1,573 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: MiMo-V2.6-Pro is the newer model by about 57 days (released September 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Kimi K3 | MiMo-V2.6-Pro |
|---|---|---|
| Provider | Moonshot AI (China) | Xiaomi (China) |
| Released | July 27, 2026 | September 22, 2026 |
| Context window | 1M (~1,573 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $3/$15 per 1M tokens | $0.435/$0.87 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, video, code | text, image, video, audio |
| 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
MiMo-V2.6-Pro is comparatively weak here — first-generation omnimodal release from Xiaomi's MiMo line — benchmark claims are largely Xiaomi's own reporting, not yet widely independently verified
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-V2.6-Pro 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-V2.6-Pro does not.
Natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters
MiMo-V2.6-Pro
Kimi K3 is comparatively weak here — image input but no audio or video
1.02 trillion total parameters, 42B active per token (sparse MoE), MIT-licensed and self-hostable
MiMo-V2.6-Pro
Xiaomi's flagship omnimodal model — 1.02T parameters, native text/image/video/audio, MIT-licensed, released September 22, 2026 — and it runs cheaper at $0.435/$0.87 per 1M tokens.
Reported Artificial Analysis Intelligence Index score of 46
MiMo-V2.6-Pro
Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
Lowest cost at scale
MiMo-V2.6-Pro
At $0.435/$0.87 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-V2.6-Pro
At $0.435/$0.87 per 1M tokens it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
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 natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters
→ MiMo-V2.6-Pro
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-V2.6-Pro: where it fits
Xiaomi's flagship omnimodal model — 1.02T parameters, native text/image/video/audio, MIT-licensed, released September 22, 2026. Released September 22, 2026 by Xiaomi, it is built for natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters, 1.02 trillion total parameters, 42B active per token (sparse MoE), MIT-licensed and self-hostable, reported Artificial Analysis Intelligence Index score of 46, and a separate 'UltraSpeed' variant reportedly delivers up to 20x faster output than Pro at similar quality for high-throughput use cases.
Its trade-offs: first-generation omnimodal release from Xiaomi's MiMo line — benchmark claims are largely Xiaomi's own reporting, not yet widely independently verified, an Artificial Analysis Index score of 46 trails several established frontier models, and no official API pricing from Xiaomi directly — the listed price reflects third-party inference providers (e.g. OpenRouter), which can change independently of Xiaomi's own terms. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Kimi K3 and MiMo-V2.6-Pro overlap enough that the right pick depends on your specific job. MiMo-V2.6-Pro costs less per token; and each leads in its own area — Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable, MiMo-V2.6-Pro for natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Kimi K3 and MiMo-V2.6-Pro 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-V2.6-Pro 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-V2.6-Pro leans toward natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K3 or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is cheaper — $3/$15 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 6.9× apart on input.
Which has the bigger context window?
Both advertise 1M (~1,573 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Kimi K3 and MiMo-V2.6-Pro together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, MiMo-V2.6-Pro 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-V2.6-Pro?
MiMo-V2.6-Pro — released September 22, 2026, about 57 days after Kimi K3.
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.