GPT-6 Astra vs Hunyuan Hy4 Preview

OpenAI · US  |  Tencent · China · Updated June 2026

Quick verdict

Pick GPT-6 Astra for computer & browser use (screenspot-pro 92.7%) or cybersecurity exploit development (exploitbench 100%). 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; GPT-6 Astra if you want a managed API.

GPT-6 Astra (OpenAI, 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. GPT-6 Astra is openAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. 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, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGPT-6 AstraHunyuan Hy4 Preview
ProviderOpenAI (US) Tencent (China)
ReleasedSeptember 3, 2026 August 28, 2026
Context window1.05M tokens (~1,575 pages) 1M+ tokens (~1,500 pages)
Price (in/out)$10/$50 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 @ 1M96.3% Not published

Who wins what

Computer & browser use (ScreenSpot-Pro 92.7%)

GPT-6 Astra

OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks — and it is the newer of the two.

Cybersecurity exploit development (ExploitBench 100%)

GPT-6 Astra

GPT-6 Astra lists cybersecurity exploit development (ExploitBench 100%) among its strengths; Hunyuan Hy4 Preview does not.

Frontier math reasoning (FrontierMath Tier 4: 97.6%)

GPT-6 Astra

GPT-6 Astra lists frontier math reasoning (FrontierMath Tier 4: 97.6%) 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 GPT-6 Astra is API-only.

SWE-bench Multilingual (82.9)

Hunyuan Hy4 Preview

Hunyuan Hy4 Preview lists sWE-bench Multilingual (82.9) among its strengths; GPT-6 Astra does not.

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 GPT-6 Astra, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

GPT-6 Astra

Larger 1.05M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

Hunyuan Hy4 Preview

Open weights let you run it on your own hardware; GPT-6 Astra is API-only.

Anyone whose priority is computer & browser use (screenspot-pro 92.7%)

GPT-6 Astra

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

GPT-6 Astra 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.

GPT-6 Astra: where it fits

OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. Released September 3, 2026 by OpenAI, it is built for computer & browser use (ScreenSpot-Pro 92.7%), cybersecurity exploit development (ExploitBench 100%), frontier math reasoning (FrontierMath Tier 4: 97.6%), and long-context recall (MRCR v2 512K-1M: 96.3%).

Its trade-offs are real: no native audio or video input, pricing doubles for prompts over 272K tokens (input/cache 2x, output 1.5x), trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4), and a separate opt-in "Daybreak" program gives vetted cybersecurity defenders a less-restricted version for legitimate vulnerability research; the public version already refuses ~91.5% of offensive cyber jailbreak attempts by default. At $10 in / $50 out per million tokens, it sits in the premium 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. GPT-6 Astra 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 GPT-6 Astra 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 GPT-6 Astra 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, GPT-6 Astra leans toward computer & browser use (screenspot-pro 92.7%) while Hunyuan Hy4 Preview leans toward gpqa diamond (92.3), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GPT-6 Astra or Hunyuan Hy4 Preview?

Hunyuan Hy4 Preview is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Astra is API-metered at $10/$50 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?

Effectively neither — 1.05M tokens vs 1M+ tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both GPT-6 Astra and Hunyuan Hy4 Preview together?

Yes — a multi-model platform like LumiChats gives you GPT-6 Astra, 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, GPT-6 Astra or Hunyuan Hy4 Preview?

GPT-6 Astra — released September 3, 2026, about 6 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.