Qwen 3.8 Flash wins on coding (84 vs 82). For most workflows, Qwen 3.8 Flash is the stronger default — swe-bench pro 62.5 at sixteen cents per million input.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Qwen 3.8 Flash is the safest overall answer here when you want the strongest default instead of the lowest list price.
Mistral: Mistral Nemo is the lower-cost option to start with when you still need useful output at scale.
GLM-5.3 Flash is the better pick when response speed matters more than maximum reasoning depth.
Qwen 3.8 Flash leads on coding with a score of 84 vs 82 for GLM-5.3 Flash.
GLM-5.3 Flash has the larger context window: 1M vs 991K for Qwen 3.8 Flash.
GLM-5.3 Flash is cheaper at $0.15/1M input tokens vs $0.16/1M for Qwen 3.8 Flash.
Qwen 3.8 Flash is the safer default: it is built for cheap high-throughput coding and reasoning, which covers most of what people bring to this comparison.
Choose GLM-5.3 Flash when your work is mostly cheap multimodal work at scale on MIT-licensed weights — that is the workload it was tuned for.
GLM-5.3 Flash is the more cost-efficient option at $0.15/1M input — Qwen 3.8 Flash costs 1x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
Z.ai / Budget / Aug 27, 2026
Native vision and video, MIT weights, fifteen cents per million.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need top-tier reasoning or a published SWE-bench Verified figure — this is a volume model, not a ceiling model.
The fastest way to see where the recommendation shifts when your priority changes.
SWE-bench Pro 62.5 at sixteen cents per million input.
Native vision and video, MIT weights, fifteen cents per million.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Qwen 3.8 FlashAlibaba | $0.16/1M | $0.47/1M | $2.54 | 991k tokens | Very fast | 84 | 76 | 78 |
| GLM-5.3 FlashZ.ai | $0.15/1M | $0.50/1M | $2.50 | 1M tokens | Fast | 82 | 76 | 78 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
Alibaba's preview of the Qwen4 architecture — 125B parameters with only 6B active per token, at sixteen cents per million input.
You need Alibaba's maximum capability — that is Qwen 3.8 Max — or a SWE-bench Verified number.
One of the best coding-score-per-dollar picks in the catalog. Route volume work here and reserve Qwen 3.8 Max or a frontier model for the hard cases.
Released August 26, 2026. The open-weight release is Qwen3.8-Flash-Next, a preview of the Qwen4 architecture: 125B mixture-of-experts with 6B active per token, a 51B n-gram embedding table and a 4B multi-token prediction layer. Qwen 3.8 Flash is the production API version on Qwen Cloud at $0.16/$0.47.
A 320B-A18B mixture-of-experts model with native vision and video, MIT-licensed weights and a 1M context — at fifteen cents per million input tokens.
You need top-tier reasoning or a published SWE-bench Verified figure — this is a volume model, not a ceiling model.
The cheapest genuinely multimodal 1M-context model worth using in August 2026. If you are running vision or video at volume and can host weights, nothing at this price is close.
Released August 26, 2026. 320B total parameters, 18B active (320B-A18B MoE). Z.ai lists $0.15/1M input, $0.03/1M cached input and $0.50/1M output, with a launch promotion halving those rates through September 9, 2026. Self-reported against GLM-5.2: DeepSWE 63.4 vs 46.2, AutomationBench 48.8 vs 26.2.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Newsletter
Useful if you care about ranking shifts, pricing changes, or a better recommendation appearing in this decision path.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
Qwen 3.8 Flash wins on more of the categories we score — budget, coding, reasoning — so it is the better default of the two. GLM-5.3 Flash is the better pick when your work is mostly cheap multimodal work at scale on MIT-licensed weights. Neither is universally "better": Qwen 3.8 Flash is aimed at cheap high-throughput coding and reasoning, GLM-5.3 Flash at cheap multimodal work at scale on MIT-licensed weights.
GLM-5.3 Flash is cheaper at $0.15/1M input and $0.5/1M output. Qwen 3.8 Flash costs $0.16/1M input and $0.47/1M output.
GLM-5.3 Flash has the larger context window at 1M tokens vs Qwen 3.8 Flash's 991K. For large document analysis, GLM-5.3 Flash is the stronger pick.
Qwen 3.8 Flash is better for coding with a score of 84 vs GLM-5.3 Flash's 82 (out of 100). Claude Fable 5 is the overall coding leader in this directory at 100/100.
Qwen 3.8 Flash is faster with a very fast speed rating (score: 5) vs GLM-5.3 Flash's fast rating (score: 4). Speed matters most for interactive and high-throughput work; for batch jobs the GLM-5.3 Flash latency penalty is usually invisible.
No published SWE-bench Verified score, only SWE-bench Pro. An architecture preview rather than a settled flagship — Qwen 3.8 Max remains Alibaba's top-end model. Avoid it if you need Alibaba's maximum capability — that is Qwen 3.8 Max — or a SWE-bench Verified number. That is the main case for looking at GLM-5.3 Flash instead.
No published SWE-bench Verified score. The launch promotion halves these rates only through September 9, 2026 — the $0.15/$0.50 list price applies after that. Avoid it if you need top-tier reasoning or a published SWE-bench Verified figure — this is a volume model, not a ceiling model. Against Qwen 3.8 Flash specifically, the gap shows up most on coding (84 vs 82).
Take a moderate workload of 10M input and 2M output tokens a month. Qwen 3.8 Flash runs $2.54 (at $0.16/1M in and $0.47/1M out); GLM-5.3 Flash runs $2.50 (at $0.15/1M in and $0.5/1M out). The gap is small enough that price should not decide this one. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is Qwen 3.8 Flash for cheap high-throughput coding and reasoning, with GLM-5.3 Flash handling cheap multimodal work at scale on MIT-licensed weights. Routing high-volume, low-stakes calls to GLM-5.3 Flash at $0.15/1M and reserving Qwen 3.8 Flash for the hard cases is usually the cheapest arrangement that does not cost you quality.