Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, Hunyuan Hy4 Preview is the value pick.
Hunyuan Hy4 Preview (Tencent) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. 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. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: nearly identical — $0.834/$2.501 per 1M tokens vs $0.95/$4 per 1M tokens. Cost will not be the deciding factor here.
Context window: Hunyuan Hy4 Preview holds 3.8× more — 1M+ tokens (~1,500 pages) vs 256K (~393 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 Preview is the newer model by about 3 months (released August 28, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Hunyuan Hy4 Preview
Kimi K2.7 Code
Provider
Tencent (China)
Moonshot AI (China)
Released
August 28, 2026
June 12, 2026
Context window
1M+ tokens (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.834/$2.501 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, image, video, 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 runs cheaper at $0.834/$2.501 per 1M tokens.
Terminal-Bench (85.4): Hunyuan Hy4 Preview — Kimi K2.7 Code is comparatively weak here — only self-reported benchmarks; no SWE-Bench Verified
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 carries the larger 1M+ tokens context.
Long-horizon agentic software engineering: Kimi K2.7 Code — Kimi K2.7 Code lists long-horizon agentic software engineering among its strengths; Hunyuan Hy4 Preview does not.
Token-efficient reasoning (~30% fewer than K2.6): Kimi K2.7 Code — Kimi K2.7 Code lists token-efficient reasoning (~30% fewer than K2.6) among its strengths; Hunyuan Hy4 Preview does not.
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — Kimi K2.7 Code lists open-weight 1T MoE, self-hostable 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 3.8× larger than Kimi K2.7 Code'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 Kimi K2.7 Code, 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 long-horizon agentic software engineering: Kimi K2.7 Code — That is its strongest area.
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.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Hunyuan Hy4 Preview and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. Hunyuan Hy4 Preview costs less per token; Hunyuan Hy4 Preview holds the larger context; and each leads in its own area — Hunyuan Hy4 Preview for gpqa diamond (92.3), Kimi K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Hunyuan Hy4 Preview or Kimi K2.7 Code 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 Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy4 Preview or Kimi K2.7 Code?
Hunyuan Hy4 Preview is cheaper — $0.834/$2.501 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
Hunyuan Hy4 Preview — 1M+ tokens vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Hunyuan Hy4 Preview and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, Kimi K2.7 Code 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 Kimi K2.7 Code?
Hunyuan Hy4 Preview — released August 28, 2026, about 3 months after Kimi K2.7 Code.
Hunyuan Hy4 Preview vs Kimi K2.7 Code
Tencent · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, Hunyuan Hy4 Preview is the value pick.
Hunyuan Hy4 Preview (Tencent) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. 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. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: nearly identical — $0.834/$2.501 per 1M tokens vs $0.95/$4 per 1M tokens. Cost will not be the deciding factor here.
▸Context window: Hunyuan Hy4 Preview holds 3.8× more — 1M+ tokens (~1,500 pages) vs 256K (~393 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 Preview is the newer model by about 3 months (released August 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Hunyuan Hy4 Preview
Kimi K2.7 Code
Provider
Tencent (China)
Moonshot AI (China)
Released
August 28, 2026
June 12, 2026
Context window
1M+ tokens (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.834/$2.501 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, image, video, 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 runs cheaper at $0.834/$2.501 per 1M tokens.
Terminal-Bench (85.4)
Hunyuan Hy4 Preview
Kimi K2.7 Code is comparatively weak here — only self-reported benchmarks; no SWE-Bench Verified
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 carries the larger 1M+ tokens context.
Long-horizon agentic software engineering
Kimi K2.7 Code
Kimi K2.7 Code lists long-horizon agentic software engineering among its strengths; Hunyuan Hy4 Preview does not.
Token-efficient reasoning (~30% fewer than K2.6)
Kimi K2.7 Code
Kimi K2.7 Code lists token-efficient reasoning (~30% fewer than K2.6) among its strengths; Hunyuan Hy4 Preview does not.
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
Kimi K2.7 Code lists open-weight 1T MoE, self-hostable 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 3.8× larger than Kimi K2.7 Code'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 Kimi K2.7 Code, 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 long-horizon agentic software engineering
→ Kimi K2.7 Code
That is its strongest area.
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.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Hunyuan Hy4 Preview and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. Hunyuan Hy4 Preview costs less per token; Hunyuan Hy4 Preview holds the larger context; and each leads in its own area — Hunyuan Hy4 Preview for gpqa diamond (92.3), Kimi K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Hunyuan Hy4 Preview and Kimi K2.7 Code 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 Kimi K2.7 Code 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 Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy4 Preview or Kimi K2.7 Code?
Hunyuan Hy4 Preview is cheaper — $0.834/$2.501 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
Hunyuan Hy4 Preview — 1M+ tokens vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Hunyuan Hy4 Preview and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, Kimi K2.7 Code 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 Kimi K2.7 Code?
Hunyuan Hy4 Preview — released August 28, 2026, about 3 months after Kimi K2.7 Code.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.