Model profile
Alibaba

Qwen3 32B (Reasoning)

Qwen3 32B (Reasoning) is a budget-priced text-first model from Alibaba with partial runtime data, partial context coverage, and the clearest fit around agent workflows / coding.

Best for: Agent workflows / CodingN/A latencyN/A contextBudget pricing
Intelligence
11.4

Benchmark blend

Coding
15.3

Dev workflow signal

Context
N/A

N/A

Input Price
$0.16

Budget tier

Decision snapshot
30

Qwen3 32B (Reasoning) currently reads as a budget text-first option with partially published context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Coding
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

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
11.4
11

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.16
86

$0.64 output / 1M

Efficient

Editorial Profile

Qwen3 32B (Reasoning) in one narrative

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

Use-case specificCoding score 15Math score 73

The Qwen3 32B (Reasoning) AI model by Alibaba.

Identity

Alibaba text-first profile

Positioning

Agent workflows / Coding with partially published context and partially published runtime.

Cost posture

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

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

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

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context limits are only partially published, so long-session planning needs extra validation.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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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
11.4
MMLU Pro
79.8%
GPQA
66.8%
HLE
7.4%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
15.3
LiveCodeBench
0.546
SciCode
35.4%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
73.0
AIME
80.7%
AIME 2025
73.0%
Math 500
96.1%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
36.3%
TAU2
29.8%
TerminalBench Hard
3.0%
LCR
0.0%

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
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.16
Output
per 1M output tokens
$0.64
Blended
AA 3:1 mix
$0.28

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

Metadata

Raw source tables at the end

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