Qwen 3.7 Max is Alibaba's best model for research — it scores 89/100 vs 88/100 for Qwen 3.8 Max, at $2.5/1M input tokens. Across all providers, GPT-6 Astra still leads research at 100/100 — worth considering if you're not committed to Alibaba.
The safest alibaba model for research default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Qwen 3.7 Max is the strongest answer here for alibaba model for research — pick it when quality of output matters more than the $2.50/1M/1M input you pay for it.
Qwen 3.8 Flash handles the same job for about 94% less per token. Start here and only move up if the output is not good enough.
Qwen 3.8 Max carries 1M tokens of context, so it is the pick for alibaba model for research when whole documents, transcripts, or repositories go in at once.
Qwen 3.7 Max leads Alibaba's lineup for research at 89/100 ($2.5/1M input, 1M context).
Qwen 3.8 Flash is the value pick at $0.16/1M input with a research score of 78/100.
GPT-6 Astra (OpenAI) is the overall research leader at 100/100 if provider choice is open.
Choose Qwen 3.7 Max when research quality is the priority and you're staying on Alibaba.
Choose Qwen 3.8 Flash when token volume matters more than peak quality.
Teams open to other providers should also evaluate GPT-6 Astra before committing.
Switch the scoring lens to see whether the alibaba model for research 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 for research recommendation shifts once you weigh price or latency differently.
Agent-first Qwen flagship, superseded by Qwen 3.8 Max.
Best Chinese flagship — beats GPT-5.6 Sol on coding, #2 globally for vision.
SWE-bench Pro 62.5 at sixteen cents per million input.
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 3.7 MaxAlibaba | $2.50/1M | $7.50/1M | $40 | 1M tokens | Balanced | 89 | 83 | 89 |
| Qwen 3.8 MaxAlibaba | $2.00/1M | $6.00/1M | $32 | 1M tokens | Balanced | 93 | 85 | 88 |
| Qwen 3.8 FlashAlibaba | $0.16/1M | $0.47/1M | $2.54 | 991k tokens | Very fast | 84 | 76 | 78 |
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 for research, what it is genuinely good at, and where we would steer you away from it.
The default answer for alibaba model for research — 54/100 on the budget axis, and the model we would start with unless the price below rules it out.
Alibaba's first closed-weight flagship — an agent-first model with native extended thinking, built to run autonomously for up to ~35 hours firing thousands of tool calls.
Starting fresh — Qwen 3.8 Max is stronger, cheaper, and multimodal.
A capable agent-first flagship that was superseded within three months by Qwen 3.8 Max at a lower price. Choose it only if its specific extended-thinking behavior fits your agent stack; otherwise 3.8 Max is the better Qwen.
Full pricing, benchmark table and release notes on the Qwen 3.7 Max page.
The long-document choice for alibaba model for research — the largest context window in this shortlist, so whole files go in at once.
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.
Where most budgets should land for alibaba model for research — about 94% less per token than Qwen 3.7 Max, and still 93/100 on the budget axis.
SWE-bench Pro 62.5 at sixteen cents per million input. Full Qwen 3.8 Flash review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Qwen 3.7 Max — it scores 89/100 on research in this directory, ahead of Qwen 3.8 Max at 88/100. Agent-first Qwen flagship, superseded by Qwen 3.8 Max.
Not overall. GPT-6 Astra (OpenAI) leads the directory for research at 100/100 vs Qwen 3.7 Max's 89/100. Qwen 3.7 Max is the best pick if you're staying within Alibaba's ecosystem.
Qwen 3.8 Flash at $0.16/1M input tokens (research score: 78/100). Use it for volume work and reserve Qwen 3.7 Max for the tasks where quality matters most.
$2.5/1M input tokens and $7.5/1M output tokens via the API. Context window: 1M tokens. On a moderate month — 10M input and 2M output tokens — that works out to about $40.00, against $2.54 for Qwen 3.8 Flash.
Behind the mid-2026 Western frontier on agentic coding, and now superseded by Qwen 3.8 Max — which is also cheaper. API-only with no open weights — a break from Qwen tradition that removes the self-hosting advantage. Concretely, avoid it if starting fresh — Qwen 3.8 Max is stronger, cheaper, and multimodal. If none of that is negotiable, GPT-6 Astra (OpenAI) is the cross-provider leader at 100/100.
marathon agent sessions — designed for ~35-hour autonomous runs with thousands of tool calls, graduate-level science reasoning (92.4 GPQA Diamond, near the top of the field), and long agent loops made cheap by the 90% cached-input discount ($0.25/1M cached). Its 1M-token context window is the practical limit on how much you can hand it in one go.
Qwen 3.7 Max scores 89/100 on research against 78/100 for Qwen 3.8 Flash, at 16x the input price. That premium is worth it on work where a wrong answer costs real time or money, and hard to justify on high-volume, low-stakes calls. Most teams run both and route by task rather than picking one.