Pick GPT-5.1 for adaptive reasoning that scales compute to task difficulty or general-purpose reasoning, coding and tool use. 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. Choose Qwen3.8-Flash-Next if you need self-hosting or data privacy; GPT-5.1 if you want a managed API.
GPT-5.1 (OpenAI, US) and Qwen3.8-Flash-Next (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-5.1 is openAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Qwen3.8-Flash-Next is about 7.8× cheaper on input ($0.16/$0.47 per 1M tokens vs $1.25/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: GPT-5.1 holds 1.5× more — 400K (~600 pages) vs 262K tokens natively (extensible to 1M with YaRN) (~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: Qwen3.8-Flash-Next is the newer model by about 10 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GPT-5.1
Qwen3.8-Flash-Next
Provider
OpenAI (US)
Alibaba (China)
Released
November 13, 2025
August 26, 2026
Context window
400K (~600 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
$1.25/$10 per 1M tokens
$0.16/$0.47 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text, image, video
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Adaptive reasoning that scales compute to task difficulty: GPT-5.1 — OpenAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line — and it carries the larger 400K context.
General-purpose reasoning, coding and tool use: GPT-5.1 — GPT-5.1 lists general-purpose reasoning, coding and tool use among its strengths; Qwen3.8-Flash-Next does not.
A large ~400K context for long documents and sessions: GPT-5.1 — Its 400K window holds about 1.5× more than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN) in a single prompt.
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4): Qwen3.8-Flash-Next — GPT-5.1 is comparatively weak here — no reliably published SWE-Bench Verified figure
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max: Qwen3.8-Flash-Next — At $0.16/$0.47 per 1M tokens it undercuts GPT-5.1 ($1.25/$10 per 1M tokens), and that gap compounds at volume.
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 runs cheaper at $0.16/$0.47 per 1M tokens.
Lowest cost at scale: Qwen3.8-Flash-Next — At $0.16/$0.47 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GPT-5.1 — Its 400K window is about 1.5× larger than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN), fitting roughly 600 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3.8-Flash-Next — At $0.16/$0.47 per 1M tokens it undercuts GPT-5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-5.1 — Larger 400K window fits more in one prompt.
A team with data-privacy or self-hosting needs: Qwen3.8-Flash-Next — Open weights let you run it on your own hardware; GPT-5.1 is API-only.
Anyone whose priority is adaptive reasoning that scales compute to task difficulty: GPT-5.1 — 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.
An enterprise with regional data-residency rules: GPT-5.1 or Qwen3.8-Flash-Next — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-5.1: where it fits
OpenAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. Released November 13, 2025 by OpenAI, it is built for adaptive reasoning that scales compute to task difficulty, general-purpose reasoning, coding and tool use, a large ~400K context for long documents and sessions, and text and image input.
Its trade-offs are real: superseded by GPT-5.2 through GPT-5.6 — kept for compatibility rather than being current, no reliably published SWE-Bench Verified figure, exact context window unconfirmed (400K vs 410K across trackers), and premium output price at $10/1M for a now-older model. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
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
The defining split here is open vs. closed. Qwen3.8-Flash-Next gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.1 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is GPT-5.1 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, GPT-5.1 leans toward adaptive reasoning that scales compute to task difficulty 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, GPT-5.1 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.1 is API-metered at $1.25/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-5.1 — 400K vs 262K tokens natively (extensible to 1M with YaRN), about 1.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.1 and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you GPT-5.1, Qwen3.8-Flash-Next 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, GPT-5.1 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 10 months after GPT-5.1.
GPT-5.1 vs Qwen3.8-Flash-Next
OpenAI · US | Alibaba · China · Updated June 2026
Quick verdict
Pick GPT-5.1 for adaptive reasoning that scales compute to task difficulty or general-purpose reasoning, coding and tool use. 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. Choose Qwen3.8-Flash-Next if you need self-hosting or data privacy; GPT-5.1 if you want a managed API.
GPT-5.1 (OpenAI, US) and Qwen3.8-Flash-Next (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-5.1 is openAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Qwen3.8-Flash-Next is about 7.8× cheaper on input ($0.16/$0.47 per 1M tokens vs $1.25/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: GPT-5.1 holds 1.5× more — 400K (~600 pages) vs 262K tokens natively (extensible to 1M with YaRN) (~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: Qwen3.8-Flash-Next is the newer model by about 10 months (released August 26, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GPT-5.1
Qwen3.8-Flash-Next
Provider
OpenAI (US)
Alibaba (China)
Released
November 13, 2025
August 26, 2026
Context window
400K (~600 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
$1.25/$10 per 1M tokens
$0.16/$0.47 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text, image, video
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Adaptive reasoning that scales compute to task difficulty
GPT-5.1
OpenAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line — and it carries the larger 400K context.
General-purpose reasoning, coding and tool use
GPT-5.1
GPT-5.1 lists general-purpose reasoning, coding and tool use among its strengths; Qwen3.8-Flash-Next does not.
A large ~400K context for long documents and sessions
GPT-5.1
Its 400K window holds about 1.5× more than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN) in a single prompt.
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4)
Qwen3.8-Flash-Next
GPT-5.1 is comparatively weak here — no reliably published SWE-Bench Verified figure
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max
Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens it undercuts GPT-5.1 ($1.25/$10 per 1M tokens), and that gap compounds at volume.
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 runs cheaper at $0.16/$0.47 per 1M tokens.
Lowest cost at scale
Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-5.1
Its 400K window is about 1.5× larger than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN), fitting roughly 600 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens it undercuts GPT-5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-5.1
Larger 400K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Qwen3.8-Flash-Next
Open weights let you run it on your own hardware; GPT-5.1 is API-only.
Anyone whose priority is adaptive reasoning that scales compute to task difficulty
→ GPT-5.1
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.
An enterprise with regional data-residency rules
→ GPT-5.1 or Qwen3.8-Flash-Next
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-5.1: where it fits
OpenAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. Released November 13, 2025 by OpenAI, it is built for adaptive reasoning that scales compute to task difficulty, general-purpose reasoning, coding and tool use, a large ~400K context for long documents and sessions, and text and image input.
Its trade-offs are real: superseded by GPT-5.2 through GPT-5.6 — kept for compatibility rather than being current, no reliably published SWE-Bench Verified figure, exact context window unconfirmed (400K vs 410K across trackers), and premium output price at $10/1M for a now-older model. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
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
The defining split here is open vs. closed. Qwen3.8-Flash-Next gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.1 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GPT-5.1 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 GPT-5.1 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, GPT-5.1 leans toward adaptive reasoning that scales compute to task difficulty 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, GPT-5.1 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.1 is API-metered at $1.25/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-5.1 — 400K vs 262K tokens natively (extensible to 1M with YaRN), about 1.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.1 and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you GPT-5.1, Qwen3.8-Flash-Next 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, GPT-5.1 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 10 months after GPT-5.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.