Model profile
Alibaba
New in 2026

Qwen: Qwen3.6 35B A3B

Qwen: Qwen3.6 35B A3B is a budget-priced multimodal generalist from Alibaba with fast runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyLarge contextBudget pricing
Intelligence
32.1

Benchmark blend

Coding
41.9

Dev workflow signal

Context
262K Tokens

Large

Input Price
$0.38

Budget tier

Decision snapshot
61

Qwen: Qwen3.6 35B A3B currently reads as a budget multimodal option with large context and a fast runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
32.1
32

General reasoning and benchmark headroom.

Limited
Speed
140 tok/s
89

TTFT 1.17s

Above average
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.38
86

$2.25 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3.6 35B A3B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 42Math score N/AVision enabled

Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated...

Identity

Alibaba multimodal profile

Positioning

Long-context research / Multimodal with large context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
32.1
GPQA
84.1%
HLE
22.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
41.9
SciCode
35.8%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
64.4%
TAU2
95.3%
TerminalBench Hard
34.8%
LCR
66.7%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
262K Tokens
Vision
Enabled
Modalities
image, text, video
Tokenizer
Qwen
Max Completion
262144
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
imagetextvideo
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.38
Output
per 1M output tokens
$2.25
Blended
AA 3:1 mix
$0.84

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.