MiMo-V2.6-Flash vs Reflection Beam 1.0
Xiaomi · China | Reflection AI · US · Updated June 2026
Quick verdict
Pick MiMo-V2.6-Flash for same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price or 309b total parameters, 15b active per token (sparse moe) — a hybrid attention mechanism for efficiency. Pick Reflection Beam 1.0 for 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0 or reflection claims 3-4x less inference compute than rival models (vendor claim). On a tight budget at scale, Reflection Beam 1.0 is the value pick.
MiMo-V2.6-Flash (Xiaomi, China) and Reflection Beam 1.0 (Reflection AI, 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-V2.6-Flash is xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens. Reflection Beam 1.0 is reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: 1M tokens vs 1M tokens — 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: Reflection Beam 1.0 is the newer model by about 25 days (released October 16, 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-V2.6-Flash | Reflection Beam 1.0 |
|---|---|---|
| Provider | Xiaomi (China) | Reflection AI (US) |
| Released | September 21, 2026 | October 16, 2026 |
| Context window | 1M tokens (~1,573 pages) | 1M tokens (~1,500 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, video, audio | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price
MiMo-V2.6-Flash
MiMo-V2.6-Flash lists same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price among its strengths; Reflection Beam 1.0 does not.
309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency
MiMo-V2.6-Flash
MiMo-V2.6-Flash lists 309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency among its strengths; Reflection Beam 1.0 does not.
MIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens
MiMo-V2.6-Flash
MiMo-V2.6-Flash lists mIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens among its strengths; Reflection Beam 1.0 does not.
501B-parameter sparse MoE (about 23B active per token) with weights on Hugging Face under Apache 2.0
Reflection Beam 1.0
Reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models — and it is the newer of the two.
Reflection claims 3-4x less inference compute than rival models (vendor claim)
Reflection Beam 1.0
MiMo-V2.6-Flash is comparatively weak here — same caveat as Pro: benchmark claims are largely self-reported by Xiaomi at launch, not yet independently verified at scale
Fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task
Reflection Beam 1.0
Reflection Beam 1.0 lists fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task among its strengths; MiMo-V2.6-Flash does not.
Lowest cost at scale
Reflection Beam 1.0
Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.6-Flash's $0.14/$0.28 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Reflection Beam 1.0
At Open weight (self-host / free) it undercuts MiMo-V2.6-Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiMo-V2.6-Flash
Larger 1M tokens window fits more in one prompt.
Anyone whose priority is same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price
→ MiMo-V2.6-Flash
It is specifically built for that.
Anyone whose priority is 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0
→ Reflection Beam 1.0
That is its strongest area.
An enterprise with regional data-residency rules
→ Reflection Beam 1.0 or MiMo-V2.6-Flash
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
MiMo-V2.6-Flash: where it fits
Xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens. Released September 21, 2026 by Xiaomi, it is built for same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price, 309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency, and mIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens.
Its trade-offs are real: lower capacity than Pro — expect a real quality gap on the hardest reasoning and generation tasks, and same caveat as Pro: benchmark claims are largely self-reported by Xiaomi at launch, not yet independently verified at scale. At $0.14 in / $0.28 out per million tokens, it sits in the budget price band.
Reflection Beam 1.0: where it fits
Reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models. Released October 16, 2026 by Reflection AI, it is built for 501B-parameter sparse MoE (about 23B active per token) with weights on Hugging Face under Apache 2.0, reflection claims 3-4x less inference compute than rival models (vendor claim), fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task, and third-party hosts list it from $0.25/$1.00 per million tokens (Nebius, as of Oct 27); Reflection's own API is in free beta.
Its trade-offs: artificial Analysis Intelligence Index of 30, well below DeepSeek V4.1 Flash and GLM-5.3 (45), text-only input; some users report reasoning loops on long agent runs, self-hosting needs about 353 GB of GPU memory even at NVFP4, and kernels are CUDA-only for now, and headline benchmarks come from Reflection's own model card; independent evaluation was limited at launch. 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-V2.6-Flash (China) and Reflection Beam 1.0 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Reflection Beam 1.0 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-V2.6-Flash and Reflection Beam 1.0 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 MiMo-V2.6-Flash or Reflection Beam 1.0 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-V2.6-Flash leans toward same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price while Reflection Beam 1.0 leans toward 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MiMo-V2.6-Flash or Reflection Beam 1.0?
Reflection Beam 1.0 is cheaper — $0.14/$0.28 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Effectively neither — 1M tokens vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both MiMo-V2.6-Flash and Reflection Beam 1.0 together?
Yes — a multi-model platform like LumiChats gives you MiMo-V2.6-Flash, Reflection Beam 1.0 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-V2.6-Flash or Reflection Beam 1.0?
Reflection Beam 1.0 — released October 16, 2026, about 25 days after MiMo-V2.6-Flash.
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.