Qwen 3.8 Max is Alibaba's best model for coding — it scores 93/100 vs 89/100 for Qwen 3.7 Max, at $2/1M input tokens. Across all providers, Claude Fable 5 still leads coding at 100/100 — worth considering if you're not committed to Alibaba.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Qwen 3.8 Max 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.
Qwen 3.8 Flash is the better pick when response speed matters more than maximum reasoning depth.
Qwen 3.8 Max leads Alibaba's lineup for coding at 93/100 ($2/1M input, 1M context).
Qwen 3.8 Flash is the value pick at $0.16/1M input with a coding score of 84/100.
Claude Fable 5 (Anthropic) is the overall coding leader at 100/100 if provider choice is open.
Choose Qwen 3.8 Max when coding 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 Claude Fable 5 before committing.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
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.
The fastest way to see where the recommendation shifts when your priority changes.
Best Chinese flagship — beats GPT-5.6 Sol on coding, #2 globally for vision.
Agent-first Qwen flagship, superseded by Qwen 3.8 Max.
SWE-bench Pro 62.5 at sixteen cents per million input.
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 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 |
| Qwen 3.8 FlashAlibaba | $0.16/1M | $0.47/1M | $2.54 | 991k tokens | Very fast | 84 | 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 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.
Announced August 3, 2026 on Alibaba Cloud Model Studio; open weights promised a week after launch. $2/$6 is first-party Model Studio pricing; cache reads from $0.17/1M. Announcement moved Alibaba stock +7% in Hong Kong.
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.
Announced at Alibaba Cloud Summit May 20, 2026. API-only on DashScope/Model Studio. Cached input $0.25/1M.
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.
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Qwen 3.8 Max — it scores 93/100 on coding in this directory, ahead of Qwen 3.7 Max at 89/100. Best Chinese flagship — beats GPT-5.6 Sol on coding, #2 globally for vision.
Not overall. Claude Fable 5 (Anthropic) leads the directory for coding at 100/100 vs Qwen 3.8 Max's 93/100. Qwen 3.8 Max is the best pick if you're staying within Alibaba's ecosystem.
Qwen 3.8 Flash at $0.16/1M input tokens (coding score: 84/100). Use it for volume work and reserve Qwen 3.8 Max for the tasks where quality matters most.
$2/1M input tokens and $6/1M output tokens via the API. Context window: 1M tokens. On a moderate month — 10M input and 2M output tokens — that works out to about $32.00, against $2.54 for Qwen 3.8 Flash.
Well behind Claude Fable 5 on SWE-bench Pro (67.7 vs 80.0) and behind several Anthropic models on text rankings. No independent third-party benchmarks at GA — early claims are largely Alibaba-reported. Concretely, avoid it if you need independently verified benchmarks or Western data residency. If none of that is negotiable, Claude Fable 5 (Anthropic) is the cross-provider leader at 100/100.
agentic coding — 67.7 SWE-bench Pro, ahead of GPT-5.6 Sol (64.6) and near Claude Opus 4.8 (69.2), vision-heavy pipelines: image and video understanding ranked #2 globally on Arena.AI, and large-scale deployments where 95B active params keep inference cost moderate. Its 1M-token context window is the practical limit on how much you can hand it in one go.
Qwen 3.8 Max scores 93/100 on coding against 84/100 for Qwen 3.8 Flash, at 13x 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.