Model profile
Alibaba

Qwen: Qwen3 Coder 30B A3B Instruct

Qwen: Qwen3 Coder 30B A3B Instruct is a budget-priced text-first model from Alibaba with heavy runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsHeavy latencyExtended contextBudget pricing
Intelligence
20.0

Benchmark blend

Coding
19.4

Dev workflow signal

Context
160K Tokens

Extended

Input Price
$0.45

Budget tier

Decision snapshot
44

Qwen: Qwen3 Coder 30B A3B Instruct currently reads as a budget text-first option with extended context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Heavy
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
20.0
20

General reasoning and benchmark headroom.

Limited
Speed
28 tok/s
44

TTFT 1.49s

Situational
Context
160K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.45
86

$2.25 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 Coder 30B A3B Instruct 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 19Math score 29

Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...

Identity

Alibaba text-first profile

Positioning

Long-context research / Agent workflows with extended context and heavy 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.

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

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

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

Best fit
  • 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
20.0
MMLU Pro
70.6%
GPQA
51.6%
HLE
4.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
19.4
LiveCodeBench
0.403
SciCode
27.8%
Math

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

Math Index
29.0
AIME
29.7%
AIME 2025
29.0%
Math 500
89.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
32.7%
TAU2
34.5%
TerminalBench Hard
15.2%
LCR
29.0%

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
160K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Qwen3
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.45
Output
per 1M output tokens
$2.25
Blended
AA 3:1 mix
$0.90

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.