Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). On a tight budget at scale, DeepSeek V3.2 is the value pick.
DeepSeek V3.2 (DeepSeek) and Hunyuan Hy4 Preview (Tencent) are two of the models people most often weigh against each other in 2026. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: DeepSeek V3.2 is about 3× cheaper on input ($0.28/$0.42 per 1M tokens vs $0.834/$2.501 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Hunyuan Hy4 Preview holds 7.6× more — 1M+ tokens (~1,500 pages) vs 131K (~197 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 9 months (released August 28, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V3.2
Hunyuan Hy4 Preview
Provider
DeepSeek (China)
Tencent (China)
Released
December 1, 2025
August 28, 2026
Context window
131K (~197 pages)
1M+ tokens (~1,500 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.834/$2.501 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it runs cheaper at $0.28/$0.42 per 1M tokens.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — DeepSeek V3.2 lists agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes) among its strengths; Hunyuan Hy4 Preview does not.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; Hunyuan Hy4 Preview does not.
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 carries the larger 1M+ tokens context.
Terminal-Bench (85.4): Hunyuan Hy4 Preview — DeepSeek V3.2 is comparatively weak here — sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2)
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.
Lowest cost at scale: DeepSeek V3.2 — At $0.28/$0.42 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 7.6× larger than DeepSeek V3.2's 131K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V3.2 — At $0.28/$0.42 per 1M tokens it undercuts Hunyuan Hy4 Preview, 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 long-context efficiency via deepseek sparse attention (dsa): DeepSeek V3.2 — It is specifically built for that.
Anyone whose priority is gpqa diamond (92.3): Hunyuan Hy4 Preview — That is its strongest area.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
DeepSeek V3.2 and Hunyuan Hy4 Preview overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; Hunyuan Hy4 Preview holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), Hunyuan Hy4 Preview for gpqa diamond (92.3). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek V3.2 or Hunyuan Hy4 Preview better for coding?
Public SWE-Bench figures are not available for Hunyuan Hy4 Preview, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while Hunyuan Hy4 Preview leans toward gpqa diamond (92.3), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or Hunyuan Hy4 Preview?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $0.834/$2.501 per 1M tokens, roughly 3× apart on input.
Which has the bigger context window?
Hunyuan Hy4 Preview — 1M+ tokens vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Hunyuan Hy4 Preview together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, Hunyuan Hy4 Preview 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, DeepSeek V3.2 or Hunyuan Hy4 Preview?
Hunyuan Hy4 Preview — released August 28, 2026, about 9 months after DeepSeek V3.2.
DeepSeek V3.2 vs Hunyuan Hy4 Preview
DeepSeek · China | Tencent · China · Updated June 2026
Quick verdict
Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). On a tight budget at scale, DeepSeek V3.2 is the value pick.
DeepSeek V3.2 (DeepSeek) and Hunyuan Hy4 Preview (Tencent) are two of the models people most often weigh against each other in 2026. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: DeepSeek V3.2 is about 3× cheaper on input ($0.28/$0.42 per 1M tokens vs $0.834/$2.501 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Hunyuan Hy4 Preview holds 7.6× more — 1M+ tokens (~1,500 pages) vs 131K (~197 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 9 months (released August 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V3.2
Hunyuan Hy4 Preview
Provider
DeepSeek (China)
Tencent (China)
Released
December 1, 2025
August 28, 2026
Context window
131K (~197 pages)
1M+ tokens (~1,500 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.834/$2.501 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it runs cheaper at $0.28/$0.42 per 1M tokens.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
DeepSeek V3.2 lists agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes) among its strengths; Hunyuan Hy4 Preview does not.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; Hunyuan Hy4 Preview does not.
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 carries the larger 1M+ tokens context.
Terminal-Bench (85.4)
Hunyuan Hy4 Preview
DeepSeek V3.2 is comparatively weak here — sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2)
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.
Lowest cost at scale
DeepSeek V3.2
At $0.28/$0.42 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 7.6× larger than DeepSeek V3.2's 131K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V3.2
At $0.28/$0.42 per 1M tokens it undercuts Hunyuan Hy4 Preview, 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 long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
It is specifically built for that.
Anyone whose priority is gpqa diamond (92.3)
→ Hunyuan Hy4 Preview
That is its strongest area.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
DeepSeek V3.2 and Hunyuan Hy4 Preview overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; Hunyuan Hy4 Preview holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), Hunyuan Hy4 Preview for gpqa diamond (92.3). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek V3.2 and Hunyuan Hy4 Preview 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 DeepSeek V3.2 or Hunyuan Hy4 Preview better for coding?
Public SWE-Bench figures are not available for Hunyuan Hy4 Preview, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while Hunyuan Hy4 Preview leans toward gpqa diamond (92.3), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or Hunyuan Hy4 Preview?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $0.834/$2.501 per 1M tokens, roughly 3× apart on input.
Which has the bigger context window?
Hunyuan Hy4 Preview — 1M+ tokens vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Hunyuan Hy4 Preview together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, Hunyuan Hy4 Preview 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, DeepSeek V3.2 or Hunyuan Hy4 Preview?
Hunyuan Hy4 Preview — released August 28, 2026, about 9 months after DeepSeek V3.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.