Hunyuan Hy4 Preview vs Reflection Beam 1.0

Tencent · China  |  Reflection AI · US · Updated June 2026

Quick verdict

Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). 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.

Hunyuan Hy4 Preview (Tencent, 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. 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. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.

Key differences at a glance

Side-by-side specs

SpecHunyuan Hy4 PreviewReflection Beam 1.0
ProviderTencent (China) Reflection AI (US)
ReleasedAugust 28, 2026 October 16, 2026
Context window1M+ tokens (~1,500 pages) 1M tokens (~1,500 pages)
Price (in/out)$0.834/$2.501 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

GPQA Diamond (92.3)

Hunyuan Hy4 Preview

Hunyuan Hy4 Preview lists gPQA Diamond (92.3) among its strengths; Reflection Beam 1.0 does not.

Terminal-Bench (85.4)

Hunyuan Hy4 Preview

Reflection Beam 1.0 is comparatively weak here — headline benchmarks come from Reflection's own model card; independent evaluation was limited at launch

SWE-bench Multilingual (82.9)

Hunyuan Hy4 Preview

Hunyuan Hy4 Preview lists sWE-bench Multilingual (82.9) 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

Reflection Beam 1.0 lists reflection claims 3-4x less inference compute than rival models (vendor claim) among its strengths; Hunyuan Hy4 Preview does not.

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; Hunyuan Hy4 Preview 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 Hunyuan Hy4 Preview's $0.834/$2.501 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 Hunyuan Hy4 Preview, and on millions of tokens that margin decides the monthly bill.

Anyone whose priority is gpqa diamond (92.3)

→ Hunyuan Hy4 Preview

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 Hunyuan Hy4 Preview

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

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 are real: 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.

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." Hunyuan Hy4 Preview (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 Hunyuan Hy4 Preview 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 pricing

Frequently asked questions

Is Hunyuan Hy4 Preview 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, Hunyuan Hy4 Preview leans toward gpqa diamond (92.3) 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, Hunyuan Hy4 Preview or Reflection Beam 1.0?

Reflection Beam 1.0 is cheaper — $0.834/$2.501 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

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

Can I use both Hunyuan Hy4 Preview and Reflection Beam 1.0 together?

Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, 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, Hunyuan Hy4 Preview or Reflection Beam 1.0?

Reflection Beam 1.0 — released October 16, 2026, about 49 days after Hunyuan Hy4 Preview.

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.