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Qwen · Efficient

Qwen3 Coder Flash

Added Sep 17, 2025

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Use in the app

A speed‑optimized and budget‑friendly sibling to Coder Plus. Excellent at code generation and agentic workflows (tool use, environment interaction) with solid general‑purpose ability.

Input
Text
Output
Text
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.30
Output
$1.50

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
65.5K

Side by side

Qwen3 Coder Flash compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Qwen3 Coder FlashQwenThis modelLlama 4 MaverickMetaRemove
ProviderQwenMeta
Discovery tierEfficientOpen weights
AddedSep 17, 2025Sep 5, 2025
Limits
Context window128K1.05MBest
Max output65.54K65.54K
Average output—114 tokens
Pricing, USD per 1M tokens
Input$0.30$0.19Best
Output$1.50$0.65Best
Cache read—$0.05
Performance
Throughput—15.4 tok/s
Time to first token—1.0 s
Request success (24h)—99.6%
Benchmark indexes
Intelligence Index9.6Best9.3
Coding Index—16.3
Math Index29.0Best19.3
Capabilities
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Qwen3 Coder Flash
  • Llama 4 Maverick

Intelligence Index

  • Qwen3 Coder Flash9.6
  • Llama 4 Maverick9.3

Qwen3 Coder Flash: Better than 19% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3 Coder Flash: —
    • Llama 4 Maverick: 16.3

  • Math Index

    Composite of the math evaluations

    • Qwen3 Coder Flash: 29.0Top
    • Llama 4 Maverick: 19.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen3 Coder Flash: —
    • Llama 4 Maverick: 0.2%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen3 Coder Flash: —
    • Llama 4 Maverick: 0.0%

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen3 Coder Flash: —
    • Llama 4 Maverick: 0.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 Coder Flash: 32.7%
    • Llama 4 Maverick: 50.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 Coder Flash: 3.8%
    • Llama 4 Maverick: 4.9%Top

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 Coder Flash: 51.6%
    • Llama 4 Maverick: 67.1%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3 Coder Flash: 70.6%
    • Llama 4 Maverick: 80.9%Top

  • IFBench

    Following precise output instructions

    • Qwen3 Coder Flash: 32.7%
    • Llama 4 Maverick: 43.0%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen3 Coder Flash: —
    • Llama 4 Maverick: 0.0%

  • SciCode

    Scientific computing in Python

    • Qwen3 Coder Flash: —
    • Llama 4 Maverick: 31.7%

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3 Coder Flash: 40.3%Top
    • Llama 4 Maverick: 39.7%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3 Coder Flash: 29.0%Top
    • Llama 4 Maverick: 19.3%

  • AIME

    Competition mathematics

    • Qwen3 Coder Flash: 29.7%
    • Llama 4 Maverick: 39.0%Top

  • MATH-500

    Competition math problems

    • Qwen3 Coder Flash: 89.3%Top
    • Llama 4 Maverick: 88.9%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3 Coder Flash: 15.2%Top
    • Llama 4 Maverick: 6.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3 Coder Flash: 34.5%Top
    • Llama 4 Maverick: 17.8%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 Coder Flash: 32.7%
    • Llama 4 Maverick: 50.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-coder-flash",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="qwen/qwen3-coder-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.