Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Choose Hunyuan Hy3 if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
Hunyuan Hy3 (Tencent, China) and MAI-1-preview (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Hunyuan Hy3 ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-1-preview is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Hunyuan Hy3 holds 2× more — 256K (~384 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: Hunyuan Hy3 is the newer model by about 10 months (released July 6, 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
Hunyuan Hy3
MAI-1-preview
Provider
Tencent (China)
Microsoft (US)
Released
July 6, 2026
August 28, 2025
Context window
256K (~384 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — MAI-1-preview is comparatively weak here — distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry
Runs a 295B model at the cost of a 21B — only 21B parameters active per token: Hunyuan Hy3 — Its 256K window holds about 2× more than MAI-1-preview's 128K in a single prompt.
Clean, unrestricted Apache-2.0 license with no geographic carve-out: Hunyuan Hy3 — A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it carries the larger 256K context.
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI: MAI-1-preview — MAI-1-preview lists microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI among its strengths; Hunyuan Hy3 does not.
Ranked in the top 15 on LM Arena at launch: MAI-1-preview — MAI-1-preview lists ranked in the top 15 on LM Arena at launch among its strengths; Hunyuan Hy3 does not.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment: MAI-1-preview — MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; Hunyuan Hy3 does not.
Largest single-prompt input: Hunyuan Hy3 — Its 256K window is about 2× larger than MAI-1-preview's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: Hunyuan Hy3 — Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs: Hunyuan Hy3 — Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — It is specifically built for that.
Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai: MAI-1-preview — That is its strongest area.
An enterprise with regional data-residency rules: MAI-1-preview or Hunyuan Hy3 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs are real: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MAI-1-preview: where it fits
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.
Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.
The bottom line for this matchup
The defining split here is open vs. closed. Hunyuan Hy3 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-1-preview gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Hunyuan Hy3 or MAI-1-preview 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, Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy3 or MAI-1-preview?
Hunyuan Hy3 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-1-preview is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Hunyuan Hy3 — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Hunyuan Hy3 and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy3, MAI-1-preview 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, Hunyuan Hy3 or MAI-1-preview?
Hunyuan Hy3 — released July 6, 2026, about 10 months after MAI-1-preview.
Hunyuan Hy3 vs MAI-1-preview
Tencent · China | Microsoft · US · Updated June 2026
Quick verdict
Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Choose Hunyuan Hy3 if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
Hunyuan Hy3 (Tencent, China) and MAI-1-preview (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Hunyuan Hy3 ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-1-preview is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Hunyuan Hy3 holds 2× more — 256K (~384 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: Hunyuan Hy3 is the newer model by about 10 months (released July 6, 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
Hunyuan Hy3
MAI-1-preview
Provider
Tencent (China)
Microsoft (US)
Released
July 6, 2026
August 28, 2025
Context window
256K (~384 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost
Hunyuan Hy3
MAI-1-preview is comparatively weak here — distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry
Runs a 295B model at the cost of a 21B — only 21B parameters active per token
Hunyuan Hy3
Its 256K window holds about 2× more than MAI-1-preview's 128K in a single prompt.
Clean, unrestricted Apache-2.0 license with no geographic carve-out
Hunyuan Hy3
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it carries the larger 256K context.
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI
MAI-1-preview
MAI-1-preview lists microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI among its strengths; Hunyuan Hy3 does not.
Ranked in the top 15 on LM Arena at launch
MAI-1-preview
MAI-1-preview lists ranked in the top 15 on LM Arena at launch among its strengths; Hunyuan Hy3 does not.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment
MAI-1-preview
MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; Hunyuan Hy3 does not.
Largest single-prompt input
Hunyuan Hy3
Its 256K window is about 2× larger than MAI-1-preview's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Hunyuan Hy3
Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Hunyuan Hy3
Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost
→ Hunyuan Hy3
It is specifically built for that.
Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai
→ MAI-1-preview
That is its strongest area.
An enterprise with regional data-residency rules
→ MAI-1-preview or Hunyuan Hy3
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs are real: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MAI-1-preview: where it fits
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.
Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.
The bottom line for this matchup
The defining split here is open vs. closed. Hunyuan Hy3 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-1-preview gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Hunyuan Hy3 and MAI-1-preview 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.
Is Hunyuan Hy3 or MAI-1-preview 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, Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy3 or MAI-1-preview?
Hunyuan Hy3 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-1-preview is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Hunyuan Hy3 — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Hunyuan Hy3 and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy3, MAI-1-preview 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, Hunyuan Hy3 or MAI-1-preview?
Hunyuan Hy3 — released July 6, 2026, about 10 months after MAI-1-preview.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.