Qwen 3.8 Flash is Alibaba's cheapest model at $0.16/1M input tokens — 94% less than the flagship Qwen 3.7 Max. It is also the best capability-per-dollar pick in the lineup.
The safest alibaba model worth using default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Qwen 3.8 Flash is the strongest answer here for alibaba model worth using — pick it when quality of output matters more than the $0.16/1M/1M input you pay for it.
Grok 4.5 is the cheaper way in for alibaba model worth using, at $2.00/1M/1M input against Qwen 3.8 Flash's $0.16/1M/1M.
Qwen 3.8 Max is the fastest of these for alibaba model worth using — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Qwen 3.8 Flash is the lowest-cost Alibaba model: $0.16/1M input, $0.47/1M output.
Qwen 3.8 Flash is the best capability-per-dollar pick (budget score 93/100).
Qwen 3.7 Max costs 16x more on input — reserve it for work where quality is the bottleneck.
Choose Qwen 3.8 Flash for high-volume, low-stakes tasks like classification, extraction, and drafts.
Choose Qwen 3.8 Flash as the everyday default if you want one budget model.
Route only the hardest tasks to Qwen 3.7 Max — a two-tier setup usually cuts spend 60–80%.
Switch the scoring lens to see whether the alibaba model worth using answer changes when cost, speed, or long-document depth leads the decision.
Alibaba / Balanced / Aug 6, 2026
Best Chinese flagship — beats GPT-5.6 Sol on coding, #2 globally for vision.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need independently verified benchmarks or Western data residency.
Where the alibaba model worth using recommendation shifts once you weigh price or latency differently.
SWE-bench Pro 62.5 at sixteen cents per million input.
Best Chinese flagship — beats GPT-5.6 Sol on coding, #2 globally for vision.
Agent-first Qwen flagship, superseded by Qwen 3.8 Max.
List prices and published scores — the numbers this page's pick 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 |
| Qwen 3.8 MaxAlibaba | $2.00/1M | $6.00/1M | $32 | 1M tokens | Balanced | 93 | 85 | 88 |
| Qwen 3.7 MaxAlibaba | $2.50/1M | $7.50/1M | $40 | 1M tokens | Balanced | 89 | 83 | 89 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for alibaba model worth using, what it is genuinely good at, and where we would steer you away from it.
Our pick for alibaba model worth using. It scores 84/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
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.
Full pricing, benchmark table and release notes on the Qwen 3.8 Flash page.
The fastest model in this shortlist for alibaba model worth using. Pick it when turnaround is what your readers or users notice.
Alibaba's largest model ever — a 2.4-trillion-parameter MoE (95B active) multimodal flagship that beat GPT-5.6 Sol on SWE-bench Pro and ranks #2 globally for vision.
You need independently verified benchmarks or Western data residency.
The strongest Chinese multimodal flagship and a legitimate SWE-bench Pro upset over GPT-5.6 Sol. If vision matters, only Fable 5-class models beat it — at 3–8x the price. Wait for independent evals before betting production on the self-reported numbers.
Full pricing, benchmark table and release notes on the Qwen 3.8 Max page.
Also worth a look for alibaba model worth using, at 89/100 on the coding axis.
Agent-first Qwen flagship, superseded by Qwen 3.8 Max. Full Qwen 3.7 Max review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Qwen 3.8 Flash at $0.16/1M input and $0.47/1M output tokens. SWE-bench Pro 62.5 at sixteen cents per million input.
Qwen 3.8 Flash is the best capability-per-dollar pick in Alibaba's lineup (budget score 93/100). It handles cheap high-throughput coding and reasoning well — step up to Qwen 3.7 Max only where quality visibly falls short.
Qwen 3.8 Flash costs $0.16/1M input vs $2.5/1M for Qwen 3.7 Max — a 94% saving on input tokens.
Qwen 3.8 Max — 1M tokens at $2/1M input. Context is where budget models are least compromised: you usually lose reasoning depth before you lose window size, so a cheap model is often a perfectly good choice for summarising or extracting from long documents.
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.
On a moderate workload of 10M input and 2M output tokens, Qwen 3.8 Flash runs about $2.54 against $40.00 for Qwen 3.7 Max — a difference of $37.46 a month at the same volume. Output tokens dominate the bill on both, so the length of the responses you generate matters far more than the length of your prompts.
Mixing is almost always cheaper for the same quality. Route high-volume, low-stakes work — classification, extraction, first drafts, routine agent steps — to Qwen 3.8 Flash, and reserve Qwen 3.7 Max for the calls where a wrong answer costs real time. Teams that split this way typically cut spend substantially without a quality drop anyone notices, because most tokens in a real workload are not hard problems.