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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 modelQwen 3.8 27B KimikoQwenRemove
ProviderQwenQwen
Discovery tierEfficientOpen weights
AddedSep 17, 2025Oct 5, 2026
Limits
Context window128K262.14KBest
Max output65.54KBest32.77K
Average output—210 tokens
Pricing, USD per 1M tokens
Input$0.30$0.10Best
Output$1.50$0.60Best
Cache read—$0.05
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index9.6—
Math Index29.0—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3 Coder Flash
  • Qwen 3.8 27B Kimiko

Intelligence Index

  • Qwen3 Coder Flash9.6
  • Qwen 3.8 27B Kimiko—

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

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Qwen3 Coder Flash: 29.0
    • Qwen 3.8 27B Kimiko: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 Coder Flash: 32.7%
    • Qwen 3.8 27B Kimiko: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 Coder Flash: 3.8%
    • Qwen 3.8 27B Kimiko: —

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 Coder Flash: 51.6%
    • Qwen 3.8 27B Kimiko: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3 Coder Flash: 70.6%
    • Qwen 3.8 27B Kimiko: —

  • IFBench

    Following precise output instructions

    • Qwen3 Coder Flash: 32.7%
    • Qwen 3.8 27B Kimiko: —

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3 Coder Flash: 40.3%
    • Qwen 3.8 27B Kimiko: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3 Coder Flash: 29.0%
    • Qwen 3.8 27B Kimiko: —

  • AIME

    Competition mathematics

    • Qwen3 Coder Flash: 29.7%
    • Qwen 3.8 27B Kimiko: —

  • MATH-500

    Competition math problems

    • Qwen3 Coder Flash: 89.3%
    • Qwen 3.8 27B Kimiko: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3 Coder Flash: 15.2%
    • Qwen 3.8 27B Kimiko: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3 Coder Flash: 34.5%
    • Qwen 3.8 27B Kimiko: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 Coder Flash: 32.7%
    • Qwen 3.8 27B Kimiko: —

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

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