Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). Pick Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows or scmp reported it 'outshines larger rivals' from deepseek and moonshot on some benchmarks despite its smaller active size. On a tight budget at scale, Step 3.7 Flash is the value pick.
Hunyuan Hy4 Preview (Tencent) and Step 3.7 Flash (StepFun) 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. Step 3.7 Flash is stepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Step 3.7 Flash is about 4.2× cheaper on input ($0.2/$1.15 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 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
Step 3.7 Flash
Provider
Tencent (China)
StepFun (China)
Released
August 28, 2026
May 29, 2026
Context window
1M+ tokens (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.834/$2.501 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, image, 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 carries the larger 1M+ tokens context.
Terminal-Bench (85.4): Hunyuan Hy4 Preview — Step 3.7 Flash is comparatively weak here — benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep
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.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows: Step 3.7 Flash — Hunyuan Hy4 Preview is comparatively weak here — text-only, no native vision support
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size: Step 3.7 Flash — Hunyuan Hy4 Preview is comparatively weak here — sWE-Marathon score still low in absolute terms (31.9) despite a large relative jump
Open weights (Apache 2.0) at a low per-token price: Step 3.7 Flash — At $0.2/$1.15 per 1M tokens it undercuts Hunyuan Hy4 Preview ($0.834/$2.501 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale: Step 3.7 Flash — At $0.2/$1.15 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 Step 3.7 Flash's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Step 3.7 Flash — At $0.2/$1.15 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 gpqa diamond (92.3): Hunyuan Hy4 Preview — It is specifically built for that.
Anyone whose priority is a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows: Step 3.7 Flash — 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.
Step 3.7 Flash: where it fits
StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. Released May 29, 2026 by StepFun, it is built for a 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows, sCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size, open weights (Apache 2.0) at a low per-token price, and includes a 1.8B vision encoder for image understanding alongside text.
Its trade-offs: stepFun is a newer, less established lab than DeepSeek, Alibaba, or Moonshot, benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep, and smaller ecosystem and less third-party documentation than the more established Chinese labs. At $0.2 in / $1.15 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Hunyuan Hy4 Preview and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash 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), Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows. 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 Step 3.7 Flash 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 Step 3.7 Flash leans toward a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy4 Preview or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $0.834/$2.501 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 4.2× 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 Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, Step 3.7 Flash 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 Step 3.7 Flash?
Hunyuan Hy4 Preview — released August 28, 2026, about 3 months after Step 3.7 Flash.
Hunyuan Hy4 Preview vs Step 3.7 Flash
Tencent · China | StepFun · China · Updated June 2026
Quick verdict
Pick Hunyuan Hy4 Preview for gpqa diamond (92.3) or terminal-bench (85.4). Pick Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows or scmp reported it 'outshines larger rivals' from deepseek and moonshot on some benchmarks despite its smaller active size. On a tight budget at scale, Step 3.7 Flash is the value pick.
Hunyuan Hy4 Preview (Tencent) and Step 3.7 Flash (StepFun) 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. Step 3.7 Flash is stepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Step 3.7 Flash is about 4.2× cheaper on input ($0.2/$1.15 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 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
Step 3.7 Flash
Provider
Tencent (China)
StepFun (China)
Released
August 28, 2026
May 29, 2026
Context window
1M+ tokens (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.834/$2.501 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, image, 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 carries the larger 1M+ tokens context.
Terminal-Bench (85.4)
Hunyuan Hy4 Preview
Step 3.7 Flash is comparatively weak here — benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep
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.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows
Step 3.7 Flash
Hunyuan Hy4 Preview is comparatively weak here — text-only, no native vision support
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size
Step 3.7 Flash
Hunyuan Hy4 Preview is comparatively weak here — sWE-Marathon score still low in absolute terms (31.9) despite a large relative jump
Open weights (Apache 2.0) at a low per-token price
Step 3.7 Flash
At $0.2/$1.15 per 1M tokens it undercuts Hunyuan Hy4 Preview ($0.834/$2.501 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale
Step 3.7 Flash
At $0.2/$1.15 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 Step 3.7 Flash's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Step 3.7 Flash
At $0.2/$1.15 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 gpqa diamond (92.3)
→ Hunyuan Hy4 Preview
It is specifically built for that.
Anyone whose priority is a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows
→ Step 3.7 Flash
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.
Step 3.7 Flash: where it fits
StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. Released May 29, 2026 by StepFun, it is built for a 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows, sCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size, open weights (Apache 2.0) at a low per-token price, and includes a 1.8B vision encoder for image understanding alongside text.
Its trade-offs: stepFun is a newer, less established lab than DeepSeek, Alibaba, or Moonshot, benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep, and smaller ecosystem and less third-party documentation than the more established Chinese labs. At $0.2 in / $1.15 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Hunyuan Hy4 Preview and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash 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), Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Hunyuan Hy4 Preview and Step 3.7 Flash 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 Step 3.7 Flash 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 Step 3.7 Flash leans toward a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy4 Preview or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $0.834/$2.501 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 4.2× 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 Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy4 Preview, Step 3.7 Flash 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 Step 3.7 Flash?
Hunyuan Hy4 Preview — released August 28, 2026, about 3 months after Step 3.7 Flash.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.