Model profile
Alibaba

Qwen3 8B (Reasoning)

Qwen3 8B (Reasoning) is a budget-priced text-first model from Alibaba with fast runtime profile, partial context coverage, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningFast latencyN/A contextBudget pricing
Intelligence
13.2

Benchmark blend

Coding
9.0

Dev workflow signal

Context
N/A

N/A

Input Price
$0.18

Budget tier

Decision snapshot
39

Qwen3 8B (Reasoning) currently reads as a budget text-first option with partially published context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
Fast
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
13.2
13

General reasoning and benchmark headroom.

Limited
Speed
89 tok/s
74

TTFT 0.96s

Competitive
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.18
86

$2.10 output / 1M

Efficient

Editorial Profile

Qwen3 8B (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 9Math score 19

The Qwen3 8B (Reasoning) AI model by Alibaba.

Identity

Alibaba text-first profile

Positioning

Agent workflows / Reasoning with partially published context and fast runtime.

Cost posture

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

Strengths
  • Latency and throughput look responsive enough for interactive loops.

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

  • 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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Context
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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
13.2
MMLU Pro
74.3%
GPQA
58.9%
HLE
4.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
9.0
LiveCodeBench
0.406
SciCode
22.6%
Math

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

Math Index
19.0
AIME
74.7%
AIME 2025
19.0%
Math 500
90.4%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
33.5%
TAU2
27.8%
TerminalBench Hard
2.3%
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
$0.18
Output
per 1M output tokens
$2.10
Blended
AA 3:1 mix
$0.66

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.