Skip to main content
Open SourceAlibaba

Qwen3.6 35B A3B

Qwen3.6 35B A3B is a model in Alibaba's Qwen3.6 series, using the A3B naming that indicates a mixture-of-experts design with roughly 3B active parameters per token.

License Open Source
Input from
$0.070 / 1M tokens
on Empirio Labs · 7 providers
Output from
$0.420 / 1M tokens
on Empirio Labs

API Pricing

Cheapest input on Empirio Labs · cheapest output on Empirio Labs
ProviderInput / 1MOutput / 1MCached / 1M
$0.070$0.420$0.035
$0.100$0.950-
$0.100$0.900$0.025
$0.132$0.912$0.066
$0.248$1.49-
$0.248$1.49-
$0.282$1.69-

Prices updated daily. Last check: Nov 6, 2026

Compare API pricing for every Qwen model →

Qwen3.6 35B A3B pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Performance & Benchmarks

Source: Artificial Analysis →
Output Speed
130 t/s
Latency (TTFT)
2.0s

Benchmarks measured Nov 2026. Scores are independent evaluations, not vendor-reported.

Model Details

General

Creator
Alibaba
Modalities
Text

Capabilities

Open Source
Yes

Strengths & Limitations

Strengths

  • Mixture-of-experts naming (35B total, ~3B active) implies compute per token far below that of a dense 35B model
  • Measured output throughput of roughly 143 tokens per second in Artificial Analysis benchmarking
  • Part of Alibaba's Qwen3.6 series, one of the more widely hosted open-model families across inference providers
  • Sparse activation makes it a candidate for high-request-volume workloads where per-token cost matters
  • Multiple hosting options mean pricing and latency can be compared directly in the table on this page

Limitations

  • We have not confirmed a context window length for this model in our database
  • Modality support (for example image input) is not tracked on our side and should be verified with the provider
  • No standardized quality benchmarks (reasoning, coding, math) are recorded for this model in our data
  • Measured time to first token of roughly 1,077 ms is higher than some smaller models, so it is less suited to latency-critical single-turn interactions
  • With only ~3B active parameters per token, quality on hard multi-step reasoning may trail models that activate substantially more compute

Key Features

•Mixture-of-experts architecture indicated by the A3B (≈3B active parameter) naming
•35B total parameters
•Measured throughput of approximately 143 output tokens per second (Artificial Analysis)
•Measured time to first token of approximately 1,077 ms (Artificial Analysis)
•Member of Alibaba's Qwen3.6 model series
•Available through multiple inference providers listed in the pricing table on this page

About Qwen3.6 35B A3B

Qwen3.6 35B A3B is a language model released by Alibaba as part of the Qwen3.6 series. Its name follows the convention Alibaba uses across recent Qwen generations: a total parameter count (35B) paired with an "A3B" suffix denoting the number of active parameters per token (approximately 3B). That structure indicates a mixture-of-experts architecture, where only a fraction of the network is engaged for any given token, rather than a dense model of the same nominal size. In measured serving benchmarks collected by Artificial Analysis, this model produced roughly 143 output tokens per second with a time to first token of about 1,077 ms. The high throughput figure is consistent with the sparse-activation design, since compute per token scales with active parameters rather than total parameters. The roughly one-second first-token latency is a separate axis and depends heavily on the specific provider, batching policy and prompt length, so it can differ from the figure above across the endpoints listed on this page. We do not currently track a confirmed context window, modality list or benchmark suite for Qwen3.6 35B A3B in our database, so those details should be verified against the provider you plan to use. In practice, models with this shape are typically selected when throughput per unit of compute matters more than raw parameter count — high-volume text generation, chat backends and agent loops that issue many short calls. Compare the providers in the pricing table above, since serving configuration for MoE models varies noticeably between hosts.

Common Use Cases

The 35B/A3B configuration points at workloads where throughput and cost per token dominate: bulk text generation, summarization pipelines, chat assistants handling many concurrent sessions, content classification and tagging at scale, and agent loops that make repeated short model calls. Because only about 3B parameters activate per token, per-request compute stays low relative to the model's 35B total size, which is the main reason to prefer this shape over a dense model of similar footprint. For tasks that hinge on deep multi-step reasoning or long-document analysis, verify the context window and quality benchmarks with your chosen provider first, since we do not track those figures for this model.

Frequently Asked Questions

How much does Qwen3.6 35B A3B cost to run?

Pricing varies by provider and by pricing type — per-token API billing, batch or discounted tiers, and self-hosting on rented GPUs are all priced differently. Rates also change frequently. Check the pricing table on this page for current figures across the providers we track.

What is Qwen3.6 35B A3B best used for?

It suits high-volume text workloads where cost and throughput per token matter: chat backends, summarization and extraction pipelines, classification, and agent loops with many short calls. The mixture-of-experts design keeps compute per token low relative to the 35B total parameter count.

What does the "A3B" in the name mean?

In Alibaba's Qwen naming convention, the A3B suffix denotes the approximate number of active parameters used per token — around 3B here — while the 35B refers to total parameters. That gap indicates a mixture-of-experts architecture rather than a dense model.

How fast is it?

Artificial Analysis benchmarking recorded roughly 143 output tokens per second with a time to first token of about 1,077 ms. Both figures depend on the serving provider, batching and prompt length, so actual results vary between the endpoints in the pricing table.

What context window does it support?

We do not have a confirmed context window for Qwen3.6 35B A3B in our database. Check the documentation of the specific provider you intend to use, as hosts sometimes serve the same model with different maximum context settings.