Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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. On a tight budget at scale, Qwen3.8-Flash-Next is the value pick.
GLM 5 (Z.ai) and Qwen3.8-Flash-Next (Alibaba) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Qwen3.8-Flash-Next is about 6.3× cheaper on input ($0.16/$0.47 per 1M tokens vs $1/$3.2 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Qwen3.8-Flash-Next holds 1.3× more — 262K tokens natively (extensible to 1M with YaRN) (~393 pages) vs 200K (~300 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 7 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5
Qwen3.8-Flash-Next
Provider
Z.ai (China)
Alibaba (China)
Released
February 12, 2026
August 26, 2026
Context window
200K (~300 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.16/$0.47 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video
SWE-Bench Verified
77.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows: GLM 5 — GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; Qwen3.8-Flash-Next does not.
Complex systems design and backend reasoning: GLM 5 — GLM 5 lists complex systems design and backend reasoning among its strengths; Qwen3.8-Flash-Next does not.
Iterative self-correction on autonomous tasks: GLM 5 — GLM 5 lists iterative self-correction on autonomous tasks 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 — 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.
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 GLM 5 ($1/$3.2 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 carries the larger 262K tokens natively (extensible to 1M with YaRN) context.
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: Qwen3.8-Flash-Next — Its 262K tokens natively (extensible to 1M with YaRN) window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 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 GLM 5, 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 agentic planning and long-horizon coding workflows: GLM 5 — 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.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget 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
GLM 5 and Qwen3.8-Flash-Next overlap enough that the right pick depends on your specific job. Qwen3.8-Flash-Next costs less per token; Qwen3.8-Flash-Next holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GLM 5 or Qwen3.8-Flash-Next better for coding?
Public SWE-Bench figures are not available for Qwen3.8-Flash-Next, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows 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, GLM 5 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is cheaper — $1/$3.2 per 1M tokens vs $0.16/$0.47 per 1M tokens, roughly 6.3× apart on input.
Which has the bigger context window?
Qwen3.8-Flash-Next — 262K tokens natively (extensible to 1M with YaRN) vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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, GLM 5 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 7 months after GLM 5.
GLM 5 vs Qwen3.8-Flash-Next
Z.ai · China | Alibaba · China · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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. On a tight budget at scale, Qwen3.8-Flash-Next is the value pick.
GLM 5 (Z.ai) and Qwen3.8-Flash-Next (Alibaba) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Qwen3.8-Flash-Next is about 6.3× cheaper on input ($0.16/$0.47 per 1M tokens vs $1/$3.2 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Qwen3.8-Flash-Next holds 1.3× more — 262K tokens natively (extensible to 1M with YaRN) (~393 pages) vs 200K (~300 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 7 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5
Qwen3.8-Flash-Next
Provider
Z.ai (China)
Alibaba (China)
Released
February 12, 2026
August 26, 2026
Context window
200K (~300 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.16/$0.47 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video
SWE-Bench Verified
77.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; Qwen3.8-Flash-Next does not.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; Qwen3.8-Flash-Next does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks 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
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.
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 GLM 5 ($1/$3.2 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 carries the larger 262K tokens natively (extensible to 1M with YaRN) context.
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
Qwen3.8-Flash-Next
Its 262K tokens natively (extensible to 1M with YaRN) window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 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 GLM 5, 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 agentic planning and long-horizon coding workflows
→ GLM 5
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.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget 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
GLM 5 and Qwen3.8-Flash-Next overlap enough that the right pick depends on your specific job. Qwen3.8-Flash-Next costs less per token; Qwen3.8-Flash-Next holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5 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.
Public SWE-Bench figures are not available for Qwen3.8-Flash-Next, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows 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, GLM 5 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is cheaper — $1/$3.2 per 1M tokens vs $0.16/$0.47 per 1M tokens, roughly 6.3× apart on input.
Which has the bigger context window?
Qwen3.8-Flash-Next — 262K tokens natively (extensible to 1M with YaRN) vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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, GLM 5 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 7 months after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.