Both are Alibaba models. Qwen3.8-Flash-Next is the newer, generally stronger default; reach for Qwen3 235B A22B (2507) when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Qwen3 235B A22B (2507) and Qwen3.8-Flash-Next are both Alibaba models, so the real question is not which lab to trust but which tier fits your workload and budget. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Qwen3.8-Flash-Next is alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Qwen3.8-Flash-Next is the newer model by about 13 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Qwen3 235B A22B (2507)
Qwen3.8-Flash-Next
Provider
Alibaba (China)
Alibaba (China)
Released
July 21, 2025
August 26, 2026
Context window
256K (~393 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
Open weight (self-host / free)
$0.16/$0.47 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B (2507) — Qwen3.8-Flash-Next is comparatively weak here — lower OSWorld 2.0 binary success rate (19.4%)
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B (2507) — Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Qwen3.8-Flash-Next does not.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B (2507) — Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Qwen3.8-Flash-Next does not.
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4): Qwen3.8-Flash-Next — Qwen3 235B A22B (2507) is comparatively weak here — coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max: Qwen3.8-Flash-Next — Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max — and it is the newer of the two.
Vision-based agentic tasks (AndroidWorld: 84.5): Qwen3.8-Flash-Next — Qwen3 235B A22B (2507) is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Lowest cost at scale: Qwen3 235B A22B (2507) — Its weights are open, so at volume you pay for your own hardware instead of Qwen3.8-Flash-Next's $0.16/$0.47 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3 235B A22B (2507) — At Open weight (self-host / free) it undercuts Qwen3.8-Flash-Next, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B (2507) — It is specifically built for that.
Anyone whose priority is swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4): Qwen3.8-Flash-Next — That is its strongest area.
Qwen3 235B A22B (2507): where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs are real: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3.8-Flash-Next: where it fits
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. Released August 26, 2026 by Alibaba, it is built for sWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4), cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max, vision-based agentic tasks (AndroidWorld: 84.5), and previews Qwen4's hybrid gated-DeltaNet plus sparse-attention architecture.
Its trade-offs: trails Claude Opus 4.6 Max on Humanity's Last Exam (35.9 vs 40.0), lower OSWorld 2.0 binary success rate (19.4%), and an open-weight architecture preview rather than Alibaba's polished flagship product. At $0.16 in / $0.47 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because Qwen3 235B A22B (2507) and Qwen3.8-Flash-Next come from the same lab (Alibaba), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Qwen3.8-Flash-Next is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Qwen3.8-Flash-Next and drop down only with a concrete reason.
Frequently asked questions
Is Qwen3 235B A22B (2507) or Qwen3.8-Flash-Next 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, Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) while Qwen3.8-Flash-Next leans toward swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Qwen3 235B A22B (2507) or Qwen3.8-Flash-Next?
Qwen3 235B A22B (2507) is cheaper — Open weight (self-host / free) vs $0.16/$0.47 per 1M tokens.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Qwen3 235B A22B (2507) to Qwen3.8-Flash-Next?
Since both are Alibaba models, the newer one (Qwen3.8-Flash-Next) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Qwen3 235B A22B (2507) or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 13 months after Qwen3 235B A22B (2507).
Qwen3 235B A22B (2507) vs Qwen3.8-Flash-Next
Alibaba · China | Alibaba · China · Updated June 2026
Quick verdict
Both are Alibaba models. Qwen3.8-Flash-Next is the newer, generally stronger default; reach for Qwen3 235B A22B (2507) when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Qwen3 235B A22B (2507) and Qwen3.8-Flash-Next are both Alibaba models, so the real question is not which lab to trust but which tier fits your workload and budget. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Qwen3.8-Flash-Next is alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Qwen3.8-Flash-Next is the newer model by about 13 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Qwen3 235B A22B (2507)
Qwen3.8-Flash-Next
Provider
Alibaba (China)
Alibaba (China)
Released
July 21, 2025
August 26, 2026
Context window
256K (~393 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
Open weight (self-host / free)
$0.16/$0.47 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B (2507)
Qwen3.8-Flash-Next is comparatively weak here — lower OSWorld 2.0 binary success rate (19.4%)
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Qwen3.8-Flash-Next does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Qwen3.8-Flash-Next does not.
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4)
Qwen3.8-Flash-Next
Qwen3 235B A22B (2507) is comparatively weak here — coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max
Qwen3.8-Flash-Next
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max — and it is the newer of the two.
Vision-based agentic tasks (AndroidWorld: 84.5)
Qwen3.8-Flash-Next
Qwen3 235B A22B (2507) is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Lowest cost at scale
Qwen3 235B A22B (2507)
Its weights are open, so at volume you pay for your own hardware instead of Qwen3.8-Flash-Next's $0.16/$0.47 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3 235B A22B (2507)
At Open weight (self-host / free) it undercuts Qwen3.8-Flash-Next, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B (2507)
It is specifically built for that.
Anyone whose priority is swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4)
→ Qwen3.8-Flash-Next
That is its strongest area.
Qwen3 235B A22B (2507): where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs are real: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3.8-Flash-Next: where it fits
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. Released August 26, 2026 by Alibaba, it is built for sWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4), cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max, vision-based agentic tasks (AndroidWorld: 84.5), and previews Qwen4's hybrid gated-DeltaNet plus sparse-attention architecture.
Its trade-offs: trails Claude Opus 4.6 Max on Humanity's Last Exam (35.9 vs 40.0), lower OSWorld 2.0 binary success rate (19.4%), and an open-weight architecture preview rather than Alibaba's polished flagship product. At $0.16 in / $0.47 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because Qwen3 235B A22B (2507) and Qwen3.8-Flash-Next come from the same lab (Alibaba), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Qwen3.8-Flash-Next is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Qwen3.8-Flash-Next and drop down only with a concrete reason.
Want both Qwen3 235B A22B (2507) and Qwen3.8-Flash-Next 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 Qwen3 235B A22B (2507) or Qwen3.8-Flash-Next 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, Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) while Qwen3.8-Flash-Next leans toward swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Qwen3 235B A22B (2507) or Qwen3.8-Flash-Next?
Qwen3 235B A22B (2507) is cheaper — Open weight (self-host / free) vs $0.16/$0.47 per 1M tokens.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Qwen3 235B A22B (2507) to Qwen3.8-Flash-Next?
Since both are Alibaba models, the newer one (Qwen3.8-Flash-Next) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Qwen3 235B A22B (2507) or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 13 months after Qwen3 235B A22B (2507).
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.