Hunyuan Hy4 Preview vs Mistral Medium 3.5

Tencent · China  |  Mistral AI · France · Updated June 2026

Quick verdict

Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). Pick Mistral Medium 3.5 for strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier or 128b dense open-weight model — self-hostable. On a tight budget at scale, Hunyuan Hy4 Preview is the value pick.

Hunyuan Hy4 Preview (Tencent, China) and Mistral Medium 3.5 (Mistral AI, France) 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. Mistral Medium 3.5 is mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecHunyuan Hy4 PreviewMistral Medium 3.5
ProviderTencent (China) Mistral AI (France)
ReleasedAugust 28, 2026 April 28, 2026
Context window1M+ tokens (~1,500 pages) 256K (~384 pages)
Price (in/out)$0.834/$2.501 per 1M tokens $1.5/$7.5 per 1M tokens
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

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 it carries the larger 1M+ tokens context.

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.

Strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier

Mistral Medium 3.5

Mistral Medium 3.5 lists strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier among its strengths; Hunyuan Hy4 Preview does not.

128B dense open-weight model — self-hostable

Mistral Medium 3.5

Mistral Medium 3.5 lists 128B dense open-weight model — self-hostable among its strengths; Hunyuan Hy4 Preview does not.

Unifies reasoning and coding into one model with an adjustable reasoning effort

Mistral Medium 3.5

Mistral Medium 3.5 lists unifies reasoning and coding into one model with an adjustable reasoning effort among its strengths; Hunyuan Hy4 Preview 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.

Largest single-prompt input

Hunyuan Hy4 Preview

Its 1M+ tokens window is about 3.9× larger than Mistral Medium 3.5's 256K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Hunyuan Hy4 Preview

At $0.834/$2.501 per 1M tokens it undercuts Mistral Medium 3.5, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Hunyuan Hy4 Preview

Larger 1M+ tokens window fits more in one prompt.

Anyone whose priority is gpqa diamond (92.3)

Hunyuan Hy4 Preview

It is specifically built for that.

Anyone whose priority is strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier

Mistral Medium 3.5

That is its strongest area.

An enterprise with regional data-residency rules

Mistral Medium 3.5 or Hunyuan Hy4 Preview

Origin (China vs France) 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.

Mistral Medium 3.5: where it fits

Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). Released April 28, 2026 by Mistral AI, it is built for strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier, 128B dense open-weight model — self-hostable, unifies reasoning and coding into one model with an adjustable reasoning effort, and 256K context with text and image input.

Its trade-offs: below the absolute frontier — a value/efficiency pick, not a flagship-beater, output pricing ($7.50/M) is higher than the cheapest Chinese rivals, license framing ('open' vs enterprise) varies across sources — confirm terms for commercial use, and no native video or audio. At $1.5 in / $7.5 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Hunyuan Hy4 Preview (China) and Mistral Medium 3.5 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Hunyuan Hy4 Preview 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 Mistral Medium 3.5 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 Mistral Medium 3.5 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 Mistral Medium 3.5 leans toward strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Hunyuan Hy4 Preview or Mistral Medium 3.5?

Hunyuan Hy4 Preview is cheaper — $0.834/$2.501 per 1M tokens vs $1.5/$7.5 per 1M tokens, roughly 1.8× apart on input.

Which has the bigger context window?

Hunyuan Hy4 Preview — 1M+ tokens vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Hunyuan Hy4 Preview and Mistral Medium 3.5 together?

Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, Mistral Medium 3.5 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 Mistral Medium 3.5?

Hunyuan Hy4 Preview — released August 28, 2026, about 4 months after Mistral Medium 3.5.

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.