Hunyuan Hy4 vs Inkling
Tencent · China | Thinking Machines Lab · 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 Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. Choose Inkling if you need self-hosting or data privacy; Hunyuan Hy4 if you want a managed API.
Hunyuan Hy4 (Tencent, China) and Inkling (Thinking Machines Lab, 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. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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: Inkling 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: 1M tokens vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Hunyuan Hy4 is the newer model by about 4 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 | Inkling |
|---|---|---|
| Provider | Tencent (China) | Thinking Machines Lab (US) |
| Released | November 4, 2026 | July 15, 2026 |
| Context window | 1M tokens (~1,573 pages) | 1M (~1,500 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, image, audio, 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
Inkling is comparatively weak here — a brand-new lab's first release - no multi-generation track record yet
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 is the newer of the two.
Tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97
Hunyuan Hy4
Hunyuan Hy4 lists tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97 among its strengths; Inkling does not.
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI
Inkling
Open weights make this possible at all — Hunyuan Hy4 is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and its weights are open while Hunyuan Hy4 is API-only.
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Inkling lists a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request among its strengths; Hunyuan Hy4 does not.
Lowest cost at scale
Inkling
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.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
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
→ Inkling
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 first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai
→ Inkling
That is its strongest area.
An enterprise with regional data-residency rules
→ Inkling 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.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. 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. Inkling 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 Inkling 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 Inkling 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 Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy4 or Inkling?
Inkling 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?
Effectively neither — 1M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Hunyuan Hy4 and Inkling together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4, Inkling 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 Inkling?
Hunyuan Hy4 — released November 4, 2026, about 4 months after Inkling.
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.