Hunyuan Hy4 vs OLMo 3 32B Think
Tencent · China | Allen Institute for AI · US · 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 OLMo 3 32B Think for the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights or a genuine 32b reasoning ('think') model that generates explicit chain-of-thought. Choose OLMo 3 32B Think if you need self-hosting or data privacy; Hunyuan Hy4 if you want a managed API.
Hunyuan Hy4 (Tencent, China) and OLMo 3 32B Think (Allen Institute for 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 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. OLMo 3 32B Think is allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. 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
- ▸Cost model: OLMo 3 32B Think ships open weights you can self-host (hardware cost only, no per-token fee), while Hunyuan Hy4 is API-metered at $0.9/$2.7 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Hunyuan Hy4 holds 16× more — 1M tokens (~1,573 pages) vs 65K (~98 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Hunyuan Hy4 is the newer model by about 12 months (released November 4, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Hunyuan Hy4 | OLMo 3 32B Think |
|---|---|---|
| Provider | Tencent (China) | Allen Institute for AI (US) |
| Released | November 4, 2026 | November 20, 2025 |
| Context window | 1M tokens (~1,573 pages) | 65K (~98 pages) |
| Price (in/out) | $0.9/$2.7 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not 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
OLMo 3 32B Think is comparatively weak here — a 65K context window is modest next to million-token frontier models
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 carries the larger 1M tokens context.
Tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97
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 is the newer of the two.
The most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights
OLMo 3 32B Think
Open weights make this possible at all — Hunyuan Hy4 is API-only, so it cannot leave the vendor's servers.
A genuine 32B reasoning ('Think') model that generates explicit chain-of-thought
OLMo 3 32B Think
Allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights — and its weights are open while Hunyuan Hy4 is API-only.
Fully open under Apache 2.0 - free to self-host
OLMo 3 32B Think
Hunyuan Hy4 is comparatively weak here — only the earlier Hy4 Preview is downloadable today (Apache 2.0)
Lowest cost at scale
OLMo 3 32B Think
Its weights are open, so at volume you pay for your own hardware instead of Hunyuan Hy4's $0.9/$2.7 per 1M tokens.
Largest single-prompt input
Hunyuan Hy4
Its 1M tokens window is about 16× larger than OLMo 3 32B Think's 65K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ OLMo 3 32B Think
At Open weight (self-host / free) it undercuts Hunyuan Hy4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Hunyuan Hy4
Larger 1M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ OLMo 3 32B Think
Open weights let you run it on your own hardware; Hunyuan Hy4 is API-only.
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 the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights
→ OLMo 3 32B Think
That is its strongest area.
An enterprise with regional data-residency rules
→ OLMo 3 32B Think or Hunyuan Hy4
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
OLMo 3 32B Think: where it fits
Allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. Released November 20, 2025 by Allen Institute for AI, it is built for the most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights, a genuine 32B reasoning ('Think') model that generates explicit chain-of-thought, fully open under Apache 2.0 - free to self-host, and built specifically to let researchers reproduce and audit exactly how the model was trained.
Its trade-offs: a 65K context window is modest next to million-token frontier models, prioritizes full openness and reproducibility over topping raw capability leaderboards, and 32B scale trails much larger frontier and open MoE models on general benchmarks. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. OLMo 3 32B Think gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Hunyuan Hy4 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 Hunyuan Hy4 and OLMo 3 32B Think 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 pricingFrequently asked questions
Is Hunyuan Hy4 or OLMo 3 32B Think 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 OLMo 3 32B Think leans toward the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy4 or OLMo 3 32B Think?
OLMo 3 32B Think is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Hunyuan Hy4 is API-metered at $0.9/$2.7 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?
Hunyuan Hy4 — 1M tokens vs 65K, about 16× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Hunyuan Hy4 and OLMo 3 32B Think together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4, OLMo 3 32B Think 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 OLMo 3 32B Think?
Hunyuan Hy4 — released November 4, 2026, about 12 months after OLMo 3 32B Think.
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.