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.
See pricingFrequently 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.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.