Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). 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. On a tight budget at scale, Inkling is the value pick.
Hunyuan Hy4 Preview (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 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. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: both advertise 1M+ tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Hunyuan Hy4 Preview is the newer model by about 44 days (released August 28, 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.
Specifications
Spec
Hunyuan Hy4 Preview
Inkling
Provider
Tencent (China)
Thinking Machines Lab (US)
Released
August 28, 2026
July 15, 2026
Context window
1M+ tokens (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.834/$2.501 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, image, audio, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not 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 is the newer of the two.
Terminal-Bench (85.4): Hunyuan Hy4 Preview — Inkling is comparatively weak here — independent third-party benchmark verification is still limited given how recently it shipped
SWE-bench Multilingual (82.9): Hunyuan Hy4 Preview — Hunyuan Hy4 Preview lists sWE-bench Multilingual (82.9) 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 — Inkling lists the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI among its strengths; Hunyuan Hy4 Preview does not.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model: Inkling — Hunyuan Hy4 Preview is comparatively weak here — text-only, no native vision support
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 Preview does not.
Lowest cost at scale: Inkling — Its weights are open, so at volume you pay for your own hardware instead of Hunyuan Hy4 Preview's $0.834/$2.501 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 Preview, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is gpqa diamond (92.3): Hunyuan Hy4 Preview — 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 Preview — Origin (China vs US) 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.
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
This is less "which is smarter" and more "which ecosystem fits." Hunyuan Hy4 Preview (China) and Inkling (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Inkling 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.
Frequently asked questions
Is Hunyuan Hy4 Preview 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 Preview leans toward gpqa diamond (92.3) 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 Preview or Inkling?
Inkling is cheaper — $0.834/$2.501 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 1M+ tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Hunyuan Hy4 Preview and Inkling together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, 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 Preview or Inkling?
Hunyuan Hy4 Preview — released August 28, 2026, about 44 days after Inkling.
Hunyuan Hy4 Preview vs Inkling
Tencent · China | Thinking Machines Lab · US · Updated June 2026
Quick verdict
Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). 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. On a tight budget at scale, Inkling is the value pick.
Hunyuan Hy4 Preview (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 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. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: both advertise 1M+ tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Hunyuan Hy4 Preview is the newer model by about 44 days (released August 28, 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 Preview
Inkling
Provider
Tencent (China)
Thinking Machines Lab (US)
Released
August 28, 2026
July 15, 2026
Context window
1M+ tokens (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.834/$2.501 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, image, audio, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not 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 is the newer of the two.
Terminal-Bench (85.4)
Hunyuan Hy4 Preview
Inkling is comparatively weak here — independent third-party benchmark verification is still limited given how recently it shipped
SWE-bench Multilingual (82.9)
Hunyuan Hy4 Preview
Hunyuan Hy4 Preview lists sWE-bench Multilingual (82.9) 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
Inkling lists the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI among its strengths; Hunyuan Hy4 Preview does not.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Hunyuan Hy4 Preview is comparatively weak here — text-only, no native vision support
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 Preview does not.
Lowest cost at scale
Inkling
Its weights are open, so at volume you pay for your own hardware instead of Hunyuan Hy4 Preview's $0.834/$2.501 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 Preview, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is gpqa diamond (92.3)
→ Hunyuan Hy4 Preview
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 Preview
Origin (China vs US) 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.
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
This is less "which is smarter" and more "which ecosystem fits." Hunyuan Hy4 Preview (China) and Inkling (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Inkling 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 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.
Is Hunyuan Hy4 Preview 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 Preview leans toward gpqa diamond (92.3) 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 Preview or Inkling?
Inkling is cheaper — $0.834/$2.501 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 1M+ tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Hunyuan Hy4 Preview and Inkling together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, 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 Preview or Inkling?
Hunyuan Hy4 Preview — released August 28, 2026, about 44 days after Inkling.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.