Amazon Nova Premier vs Hunyuan Hy4 Preview

Amazon · US  |  Tencent · China · Updated June 2026

Quick verdict

Pick Amazon Nova Premier for 1m-token context with deep aws bedrock integration or amazon's most capable nova model, positioned as a 'teacher' for distilling smaller models. Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). Choose Hunyuan Hy4 Preview if you need self-hosting or data privacy; Amazon Nova Premier if you want a managed API.

Amazon Nova Premier (Amazon, US) and Hunyuan Hy4 Preview (Tencent, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Amazon Nova Premier is amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence. Hunyuan Hy4 Preview is tencent's open-sourced (Apache 2.0) next-generation Hunyuan flagship preview, released August 28, 2026 as a 770B-parameter (49B active) MoE model that helped optimize its own training pipeline. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecAmazon Nova PremierHunyuan Hy4 Preview
ProviderAmazon (US) Tencent (China)
ReleasedApril 30, 2025 August 28, 2026
Context window1M (~1,500 pages) 1M+ tokens (~1,500 pages)
Price (in/out)$2.5/$12.5 per 1M tokens $0.834/$2.501 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

1M-token context with deep AWS Bedrock integration

Amazon Nova Premier

Amazon Nova Premier lists 1M-token context with deep AWS Bedrock integration among its strengths; Hunyuan Hy4 Preview does not.

Amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models

Amazon Nova Premier

Amazon Nova Premier lists amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models among its strengths; Hunyuan Hy4 Preview does not.

A natural fit for teams already building on AWS

Amazon Nova Premier

Amazon Nova Premier lists a natural fit for teams already building on AWS among its strengths; Hunyuan Hy4 Preview does not.

GPQA Diamond (92.3)

Hunyuan Hy4 Preview

Tencent's open-sourced (Apache 2.0) next-generation Hunyuan flagship preview, released August 28, 2026 as a 770B-parameter (49B active) MoE model that helped optimize its own training pipeline — and it runs cheaper at $0.834/$2.501 per 1M tokens.

Terminal-Bench (85.4)

Hunyuan Hy4 Preview

Tencent's open-sourced (Apache 2.0) next-generation Hunyuan flagship preview, released August 28, 2026 as a 770B-parameter (49B active) MoE model that helped optimize its own training pipeline — and its weights are open while Amazon Nova Premier is API-only.

SWE-bench Multilingual (82.9)

Hunyuan Hy4 Preview

Tencent's open-sourced (Apache 2.0) next-generation Hunyuan flagship preview, released August 28, 2026 as a 770B-parameter (49B active) MoE model that helped optimize its own training pipeline — and it is the newer of the two.

Lowest cost at scale

Hunyuan Hy4 Preview

At $0.834/$2.501 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

Hunyuan Hy4 Preview

At $0.834/$2.501 per 1M tokens it undercuts Amazon Nova Premier, and on millions of tokens that margin decides the monthly bill.

A team with data-privacy or self-hosting needs

Hunyuan Hy4 Preview

Open weights let you run it on your own hardware; Amazon Nova Premier is API-only.

Anyone whose priority is 1m-token context with deep aws bedrock integration

Amazon Nova Premier

It is specifically built for that.

Anyone whose priority is gpqa diamond (92.3)

Hunyuan Hy4 Preview

That is its strongest area.

An enterprise with regional data-residency rules

Amazon Nova Premier or Hunyuan Hy4 Preview

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

Amazon Nova Premier: where it fits

Amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence. Released April 30, 2025 by Amazon, it is built for 1M-token context with deep AWS Bedrock integration, amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models, a natural fit for teams already building on AWS, and multimodal input for complex reasoning across text and images.

Its trade-offs are real: weak on independent intelligence - Artificial Analysis Intelligence Index of 13, below average for its tier, expensive for its score at $2.50/$12.50 per million tokens, a 2025 model - older than the 2026 frontier it competes against, and sources disagree on modalities (Amazon cites image input; some evaluations list text-only). At $2.5 in / $12.5 out per million tokens, it sits in the mid price band.

Hunyuan Hy4 Preview: where it fits

Tencent's open-sourced (Apache 2.0) next-generation Hunyuan flagship preview, released August 28, 2026 as a 770B-parameter (49B active) MoE model that helped optimize its own training pipeline. Released August 28, 2026 by Tencent, it is built for gPQA Diamond (92.3), terminal-Bench (85.4), sWE-bench Multilingual (82.9), and large jump in long-horizon coding vs prior Hunyuan model (DeepSWE 28.0 to 64.3).

Its trade-offs: text-only, no native vision support, labeled a 'preview', not yet Tencent's finalized GA flagship, and sWE-Marathon score still low in absolute terms (31.9) despite a large relative jump. At $0.834 in / $2.501 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. Hunyuan Hy4 Preview gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Amazon Nova Premier 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 Amazon Nova Premier and Hunyuan Hy4 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.

See pricing

Frequently asked questions

Is Amazon Nova Premier or Hunyuan Hy4 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, Amazon Nova Premier leans toward 1m-token context with deep aws bedrock integration while Hunyuan Hy4 Preview leans toward gpqa diamond (92.3), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Amazon Nova Premier or Hunyuan Hy4 Preview?

Hunyuan Hy4 Preview is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Amazon Nova Premier is API-metered at $2.5/$12.5 per 1M tokens. 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?

Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Amazon Nova Premier and Hunyuan Hy4 Preview together?

Yes — a multi-model platform like LumiChats gives you Amazon Nova Premier, Hunyuan Hy4 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, Amazon Nova Premier or Hunyuan Hy4 Preview?

Hunyuan Hy4 Preview — released August 28, 2026, about 16 months after Amazon Nova Premier.

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.