Pick Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) or 744b-parameter moe built on a glm-5.2 base, released under a fully open mit license. Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. On a tight budget at scale, Atria Dawn Preview is the value pick.
Atria Dawn Preview (Shanghai AI Laboratory) and DeepSeek V4 (DeepSeek) are two of the models people most often weigh against each other in 2026. Atria Dawn Preview is a free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: DeepSeek V4 holds 3.8× more — 1M (~1,500 pages) vs 256K tokens (~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: Atria Dawn Preview is the newer model by about 5 months (released September 11, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Atria Dawn Preview
DeepSeek V4
Provider
Shanghai AI Laboratory (China)
DeepSeek (China)
Released
September 11, 2026
April 24, 2026
Context window
256K tokens (~393 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$0.66/$1.98 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, code
SWE-Bench Verified
Not published
80.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0): Atria Dawn Preview — A free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper — and it is the newer of the two.
744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license: Atria Dawn Preview — Atria Dawn Preview lists 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license among its strengths; DeepSeek V4 does not.
Free to self-host — no API pricing, run entirely on your own hardware: Atria Dawn Preview — Atria Dawn Preview lists free to self-host — no API pricing, run entirely on your own hardware among its strengths; DeepSeek V4 does not.
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it carries the larger 1M context.
Open MIT-licensed weights you can self-host: DeepSeek V4 — Atria Dawn Preview is comparatively weak here — released quietly, with weights and an API posted to GitHub before Shanghai AI Lab's own technical report went up on arXiv days later
No long-context surcharge: DeepSeek V4 — Its 1M window holds about 3.8× more than Atria Dawn Preview's 256K tokens in a single prompt.
Lowest cost at scale: Atria Dawn Preview — Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4's $0.66/$1.98 per 1M tokens.
Largest single-prompt input: DeepSeek V4 — Its 1M window is about 3.8× larger than Atria Dawn Preview's 256K tokens, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Atria Dawn Preview — At Open weight (self-host / free) it undercuts DeepSeek V4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4 — Larger 1M window fits more in one prompt.
Anyone whose priority is best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0): Atria Dawn Preview — It is specifically built for that.
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — That is its strongest area.
Atria Dawn Preview: where it fits
A free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. Released September 11, 2026 by Shanghai AI Laboratory, it is built for best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0), 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license, and free to self-host — no API pricing, run entirely on your own hardware.
Its trade-offs are real: context window is reported inconsistently across trackers — most list 256K tokens, at least one lists 1M; unconfirmed which is accurate, released quietly, with weights and an API posted to GitHub before Shanghai AI Lab's own technical report went up on arXiv days later, and a lab research preview rather than a commercial product — support and update cadence are unclear. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs: this generic 'V4' entry mirrors DeepSeek V4-Pro's specs - see DeepSeek V4-Pro and DeepSeek V4-Flash for DeepSeek's actual named product tiers, trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.66 in / $1.98 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Atria Dawn Preview and DeepSeek V4 overlap enough that the right pick depends on your specific job. Atria Dawn Preview costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0), DeepSeek V4 for near-frontier coding at ~1/12 the cost. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Atria Dawn Preview or DeepSeek V4 better for coding?
Public SWE-Bench figures are not available for Atria Dawn Preview, so the honest test is your own repository — run an identical real bug through both. By design, Atria Dawn Preview leans toward best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) while DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Atria Dawn Preview or DeepSeek V4?
Atria Dawn Preview is cheaper — Open weight (self-host / free) vs $0.66/$1.98 per 1M tokens.
Which has the bigger context window?
DeepSeek V4 — 1M vs 256K tokens, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Atria Dawn Preview and DeepSeek V4 together?
Yes — a multi-model platform like LumiChats gives you Atria Dawn Preview, DeepSeek V4 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, Atria Dawn Preview or DeepSeek V4?
Atria Dawn Preview — released September 11, 2026, about 5 months after DeepSeek V4.
Atria Dawn Preview vs DeepSeek V4
Shanghai AI Laboratory · China | DeepSeek · China · Updated June 2026
Quick verdict
Pick Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) or 744b-parameter moe built on a glm-5.2 base, released under a fully open mit license. Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. On a tight budget at scale, Atria Dawn Preview is the value pick.
Atria Dawn Preview (Shanghai AI Laboratory) and DeepSeek V4 (DeepSeek) are two of the models people most often weigh against each other in 2026. Atria Dawn Preview is a free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: DeepSeek V4 holds 3.8× more — 1M (~1,500 pages) vs 256K tokens (~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: Atria Dawn Preview is the newer model by about 5 months (released September 11, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Atria Dawn Preview
DeepSeek V4
Provider
Shanghai AI Laboratory (China)
DeepSeek (China)
Released
September 11, 2026
April 24, 2026
Context window
256K tokens (~393 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$0.66/$1.98 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text
text, code
SWE-Bench Verified
Not published
80.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0)
Atria Dawn Preview
A free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper — and it is the newer of the two.
744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license
Atria Dawn Preview
Atria Dawn Preview lists 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license among its strengths; DeepSeek V4 does not.
Free to self-host — no API pricing, run entirely on your own hardware
Atria Dawn Preview
Atria Dawn Preview lists free to self-host — no API pricing, run entirely on your own hardware among its strengths; DeepSeek V4 does not.
Near-frontier coding at ~1/12 the cost
DeepSeek V4
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it carries the larger 1M context.
Open MIT-licensed weights you can self-host
DeepSeek V4
Atria Dawn Preview is comparatively weak here — released quietly, with weights and an API posted to GitHub before Shanghai AI Lab's own technical report went up on arXiv days later
No long-context surcharge
DeepSeek V4
Its 1M window holds about 3.8× more than Atria Dawn Preview's 256K tokens in a single prompt.
Lowest cost at scale
Atria Dawn Preview
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4's $0.66/$1.98 per 1M tokens.
Largest single-prompt input
DeepSeek V4
Its 1M window is about 3.8× larger than Atria Dawn Preview's 256K tokens, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Atria Dawn Preview
At Open weight (self-host / free) it undercuts DeepSeek V4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4
Larger 1M window fits more in one prompt.
Anyone whose priority is best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0)
→ Atria Dawn Preview
It is specifically built for that.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
That is its strongest area.
Atria Dawn Preview: where it fits
A free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. Released September 11, 2026 by Shanghai AI Laboratory, it is built for best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0), 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license, and free to self-host — no API pricing, run entirely on your own hardware.
Its trade-offs are real: context window is reported inconsistently across trackers — most list 256K tokens, at least one lists 1M; unconfirmed which is accurate, released quietly, with weights and an API posted to GitHub before Shanghai AI Lab's own technical report went up on arXiv days later, and a lab research preview rather than a commercial product — support and update cadence are unclear. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs: this generic 'V4' entry mirrors DeepSeek V4-Pro's specs - see DeepSeek V4-Pro and DeepSeek V4-Flash for DeepSeek's actual named product tiers, trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.66 in / $1.98 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Atria Dawn Preview and DeepSeek V4 overlap enough that the right pick depends on your specific job. Atria Dawn Preview costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0), DeepSeek V4 for near-frontier coding at ~1/12 the cost. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Atria Dawn Preview and DeepSeek V4 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 Atria Dawn Preview or DeepSeek V4 better for coding?
Public SWE-Bench figures are not available for Atria Dawn Preview, so the honest test is your own repository — run an identical real bug through both. By design, Atria Dawn Preview leans toward best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) while DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Atria Dawn Preview or DeepSeek V4?
Atria Dawn Preview is cheaper — Open weight (self-host / free) vs $0.66/$1.98 per 1M tokens.
Which has the bigger context window?
DeepSeek V4 — 1M vs 256K tokens, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Atria Dawn Preview and DeepSeek V4 together?
Yes — a multi-model platform like LumiChats gives you Atria Dawn Preview, DeepSeek V4 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, Atria Dawn Preview or DeepSeek V4?
Atria Dawn Preview — released September 11, 2026, about 5 months after DeepSeek V4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.