Hunyuan Hy4 vs Qwen 3.8-Max

Tencent · China  |  Alibaba · China · Updated June 2026

Quick verdict

Pick Hunyuan Hy4 for first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview or tencent-reported gains over the preview of 19.1% on gamedev and 9.8% on gamelogic. Pick Qwen 3.8-Max for near-frontier quality at value pricing — artificial analysis intelligence index 58 or large 1m-token context with multimodal input (text, image, video). On a tight budget at scale, Hunyuan Hy4 is the value pick.

Hunyuan Hy4 (Tencent) and Qwen 3.8-Max (Alibaba) are two of the models people most often weigh against each other in 2026. Hunyuan Hy4 is tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public. Qwen 3.8-Max is alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. 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 Hy4Qwen 3.8-Max
ProviderTencent (China) Alibaba (China)
ReleasedNovember 4, 2026 August 3, 2026
Context window1M tokens (~1,573 pages) 1M (~1,573 pages)
Price (in/out)$0.9/$2.7 per 1M tokens $2/$6 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image, code text, image, video, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

First Hunyuan model with vision and computer use, adding image input to the text-only Hy4 Preview

Hunyuan Hy4

Qwen 3.8-Max is comparatively weak here — trails the very top models (Opus 5, Fable 5, GPT-5.6 Sol) on independent tests

Tencent-reported gains over the preview of 19.1% on GameDev and 9.8% on GameLogic

Hunyuan Hy4

Tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public — and it runs cheaper at $0.9/$2.7 per 1M tokens.

Tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97

Hunyuan Hy4

Qwen 3.8-Max is comparatively weak here — flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced

Near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58

Qwen 3.8-Max

Qwen 3.8-Max lists near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58 among its strengths; Hunyuan Hy4 does not.

Large 1M-token context with multimodal input (text, image, video)

Qwen 3.8-Max

Qwen 3.8-Max lists large 1M-token context with multimodal input (text, image, video) among its strengths; Hunyuan Hy4 does not.

Mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token

Qwen 3.8-Max

Qwen 3.8-Max lists mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token among its strengths; Hunyuan Hy4 does not.

Lowest cost at scale

Hunyuan Hy4

At $0.9/$2.7 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

At $0.9/$2.7 per 1M tokens it undercuts Qwen 3.8-Max, and on millions of tokens that margin decides the monthly bill.

Anyone whose priority is first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview

→ Hunyuan Hy4

It is specifically built for that.

Anyone whose priority is near-frontier quality at value pricing — artificial analysis intelligence index 58

→ Qwen 3.8-Max

That is its strongest area.

Hunyuan Hy4: where it fits

Tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public. Released November 4, 2026 by Tencent, it is built for first Hunyuan model with vision and computer use, adding image input to the text-only Hy4 Preview, tencent-reported gains over the preview of 19.1% on GameDev and 9.8% on GameLogic, tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97, and about $0.90/$2.70 per million tokens, with 50% off on Tencent Cloud TokenHub through December 31, 2026 (OpenRouter lists $0.417/$1.25 with that discount).

Its trade-offs are real: weights not public as of November 5, 2026 - Tencent says weights and system cards will come in the coming days, and no license is announced yet, only the earlier Hy4 Preview is downloadable today (Apache 2.0), published improvements are Tencent's own and mostly relative to the preview, with no SWE-bench Verified figure, and sources differ on list price: Tencent says about $0.90/$2.70, while OpenRouter and Vals show $0.83/$2.50. At $0.9 in / $2.7 out per million tokens, it sits in the budget price band.

Qwen 3.8-Max: where it fits

Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. Released August 3, 2026 by Alibaba, it is built for near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58, large 1M-token context with multimodal input (text, image, video), mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token, and $2/$6 per million tokens — far below US flagships like Claude Opus 5 and GPT-5.6 Sol.

Its trade-offs: open weights were announced for release but had not shipped as of mid-August 2026 — a closed API for now, active-parameter count is not officially disclosed by Alibaba, flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced, and trails the very top models (Opus 5, Fable 5, GPT-5.6 Sol) on independent tests. At $2 in / $6 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

Hunyuan Hy4 and Qwen 3.8-Max overlap enough that the right pick depends on your specific job. Hunyuan Hy4 costs less per token; and each leads in its own area — Hunyuan Hy4 for first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview, Qwen 3.8-Max for near-frontier quality at value pricing — artificial analysis intelligence index 58. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Hunyuan Hy4 and Qwen 3.8-Max 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 or Qwen 3.8-Max 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 leans toward first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview while Qwen 3.8-Max leans toward near-frontier quality at value pricing — artificial analysis intelligence index 58, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Hunyuan Hy4 or Qwen 3.8-Max?

Hunyuan Hy4 is cheaper — $0.9/$2.7 per 1M tokens vs $2/$6 per 1M tokens, roughly 2.2× apart on input.

Which has the bigger context window?

Both advertise 1M tokens (~1,573 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Hunyuan Hy4 and Qwen 3.8-Max together?

Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4, Qwen 3.8-Max 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 or Qwen 3.8-Max?

Hunyuan Hy4 — released November 4, 2026, about 3 months after Qwen 3.8-Max.

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.