Pick Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4) or cost efficiency: ~1/9th the training cost of qwen3.7-plus, ~12x cheaper api than flagship qwen3.8-max. Pick Reka Flash 3.1 for a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) or strong base for fine-tuning agentic tasks - notably improved coding since the original flash 3. On a tight budget at scale, Reka Flash 3.1 is the value pick.
Qwen3.8-Flash-Next (Alibaba, China) and Reka Flash 3.1 (Reka AI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Reka Flash 3.1 is reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Qwen3.8-Flash-Next holds 8× more — 262K tokens natively (extensible to 1M with YaRN) (~393 pages) vs 32K (~49 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Qwen3.8-Flash-Next is the newer model by about 14 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Qwen3.8-Flash-Next
Reka Flash 3.1
Provider
Alibaba (China)
Reka AI (US)
Released
August 26, 2026
July 2025
Context window
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
32K (~49 pages)
Price (in/out)
$0.16/$0.47 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4): 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 carries the larger 262K tokens natively (extensible to 1M with YaRN) context.
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max: Qwen3.8-Flash-Next — Reka Flash 3.1 is comparatively weak here — smaller and less capable overall than flagship frontier models from major labs
Vision-based agentic tasks (AndroidWorld: 84.5): 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.
A 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists): Reka Flash 3.1 — Reka Flash 3.1 lists a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) among its strengths; Qwen3.8-Flash-Next does not.
Strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3: Reka Flash 3.1 — Reka Flash 3.1 lists strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3 among its strengths; Qwen3.8-Flash-Next does not.
Fully open weights (Apache 2.0) from a frontier-caliber research team: Reka Flash 3.1 — Reka Flash 3.1 lists fully open weights (Apache 2.0) from a frontier-caliber research team among its strengths; Qwen3.8-Flash-Next does not.
Lowest cost at scale: Reka Flash 3.1 — 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.
Largest single-prompt input: Qwen3.8-Flash-Next — Its 262K tokens natively (extensible to 1M with YaRN) window is about 8× larger than Reka Flash 3.1's 32K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Reka Flash 3.1 — At Open weight (self-host / free) it undercuts Qwen3.8-Flash-Next, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen3.8-Flash-Next — Larger 262K tokens natively (extensible to 1M with YaRN) window fits more in one prompt.
Anyone whose priority is swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4): Qwen3.8-Flash-Next — It is specifically built for that.
Anyone whose priority is a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists): Reka Flash 3.1 — That is its strongest area.
An enterprise with regional data-residency rules: Reka Flash 3.1 or Qwen3.8-Flash-Next — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Reka Flash 3.1: where it fits
Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. Released July 2025 by Reka AI, it is built for a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3, fully open weights (Apache 2.0) from a frontier-caliber research team, and built by alumni from Google DeepMind and Meta's AI labs.
Its trade-offs: a relatively small 32K context window next to million-token frontier models, smaller and less capable overall than flagship frontier models from major labs, and reka's broader roadmap has shifted toward robotics/world models after merging with Moonvalley in June 2026, raising questions about ongoing LLM investment. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Qwen3.8-Flash-Next (China) and Reka Flash 3.1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Reka Flash 3.1 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is Qwen3.8-Flash-Next or Reka Flash 3.1 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, Qwen3.8-Flash-Next leans toward swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4) while Reka Flash 3.1 leans toward a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Qwen3.8-Flash-Next or Reka Flash 3.1?
Reka Flash 3.1 is cheaper — $0.16/$0.47 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3.8-Flash-Next — 262K tokens natively (extensible to 1M with YaRN) vs 32K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Qwen3.8-Flash-Next and Reka Flash 3.1 together?
Yes — a multi-model platform like LumiChats gives you Qwen3.8-Flash-Next, Reka Flash 3.1 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, Qwen3.8-Flash-Next or Reka Flash 3.1?
Qwen3.8-Flash-Next — released August 26, 2026, about 14 months after Reka Flash 3.1.
Qwen3.8-Flash-Next vs Reka Flash 3.1
Alibaba · China | Reka AI · US · Updated June 2026
Quick verdict
Pick Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4) or cost efficiency: ~1/9th the training cost of qwen3.7-plus, ~12x cheaper api than flagship qwen3.8-max. Pick Reka Flash 3.1 for a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) or strong base for fine-tuning agentic tasks - notably improved coding since the original flash 3. On a tight budget at scale, Reka Flash 3.1 is the value pick.
Qwen3.8-Flash-Next (Alibaba, China) and Reka Flash 3.1 (Reka AI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Reka Flash 3.1 is reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Qwen3.8-Flash-Next holds 8× more — 262K tokens natively (extensible to 1M with YaRN) (~393 pages) vs 32K (~49 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Qwen3.8-Flash-Next is the newer model by about 14 months (released August 26, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Qwen3.8-Flash-Next
Reka Flash 3.1
Provider
Alibaba (China)
Reka AI (US)
Released
August 26, 2026
July 2025
Context window
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
32K (~49 pages)
Price (in/out)
$0.16/$0.47 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4)
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 carries the larger 262K tokens natively (extensible to 1M with YaRN) context.
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max
Qwen3.8-Flash-Next
Reka Flash 3.1 is comparatively weak here — smaller and less capable overall than flagship frontier models from major labs
Vision-based agentic tasks (AndroidWorld: 84.5)
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.
A 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists)
Reka Flash 3.1
Reka Flash 3.1 lists a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) among its strengths; Qwen3.8-Flash-Next does not.
Strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3
Reka Flash 3.1
Reka Flash 3.1 lists strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3 among its strengths; Qwen3.8-Flash-Next does not.
Fully open weights (Apache 2.0) from a frontier-caliber research team
Reka Flash 3.1
Reka Flash 3.1 lists fully open weights (Apache 2.0) from a frontier-caliber research team among its strengths; Qwen3.8-Flash-Next does not.
Lowest cost at scale
Reka Flash 3.1
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.
Largest single-prompt input
Qwen3.8-Flash-Next
Its 262K tokens natively (extensible to 1M with YaRN) window is about 8× larger than Reka Flash 3.1's 32K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Reka Flash 3.1
At Open weight (self-host / free) it undercuts Qwen3.8-Flash-Next, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen3.8-Flash-Next
Larger 262K tokens natively (extensible to 1M with YaRN) window fits more in one prompt.
Anyone whose priority is swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4)
→ Qwen3.8-Flash-Next
It is specifically built for that.
Anyone whose priority is a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists)
→ Reka Flash 3.1
That is its strongest area.
An enterprise with regional data-residency rules
→ Reka Flash 3.1 or Qwen3.8-Flash-Next
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Reka Flash 3.1: where it fits
Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. Released July 2025 by Reka AI, it is built for a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3, fully open weights (Apache 2.0) from a frontier-caliber research team, and built by alumni from Google DeepMind and Meta's AI labs.
Its trade-offs: a relatively small 32K context window next to million-token frontier models, smaller and less capable overall than flagship frontier models from major labs, and reka's broader roadmap has shifted toward robotics/world models after merging with Moonvalley in June 2026, raising questions about ongoing LLM investment. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Qwen3.8-Flash-Next (China) and Reka Flash 3.1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Reka Flash 3.1 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Qwen3.8-Flash-Next and Reka Flash 3.1 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 Qwen3.8-Flash-Next or Reka Flash 3.1 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, Qwen3.8-Flash-Next leans toward swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4) while Reka Flash 3.1 leans toward a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Qwen3.8-Flash-Next or Reka Flash 3.1?
Reka Flash 3.1 is cheaper — $0.16/$0.47 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3.8-Flash-Next — 262K tokens natively (extensible to 1M with YaRN) vs 32K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Qwen3.8-Flash-Next and Reka Flash 3.1 together?
Yes — a multi-model platform like LumiChats gives you Qwen3.8-Flash-Next, Reka Flash 3.1 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, Qwen3.8-Flash-Next or Reka Flash 3.1?
Qwen3.8-Flash-Next — released August 26, 2026, about 14 months after Reka Flash 3.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.