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Qwen · Model guide

Qwen3.5 Flash

qwen/qwen3.5-flash-02-23
Modalities
TextImageVideoText
Context
1M
Max output
66K
Qwen3.5 Flash

A stable and mature Qwen3.5 multimodal execution tier

Context window
1M
tokens
Max output
65.53K
tokens
Thinking budget
80K
tokens
Existing workloads
A stable baseline for production workflows already using it
Multimodal batches
Images, video, OCR, and structured extraction
Migration baseline
A comparison point before moving to Qwen3.6 Flash

CrossModel keeps the short name qwen3.5-flash-02-23; the upstream snapshot is qwen3.5-flash-2026-02-23.

Overview

Qwen3.5 Flash is the mature Flash-tier model in the Qwen3.5 family. In CrossModel, qwen/qwen3.5-flash-02-23 maps to Qwen Cloud's qwen3.5-flash-2026-02-23 snapshot while keeping the shorter public ID requested for the catalog.

Its best role has changed now that Qwen3.6 Flash is available. Qwen3.5 Flash is still useful for existing production workflows that value stability, long context, multimodal input, function calling, built-in tools, and structured output. For new projects, treat it as a baseline to compare against Qwen3.6 Flash rather than the default ceiling.

Key capabilities

DimensionDetail
Context window1M tokens
Max output65.53K tokens
Thinking budget80K tokens
Input modalitiesText, image, video
Output modalitiesText
Toolsfunction calling, built-in tools, structured output, explicit cache, session cache

Qwen3.5 Flash supports session cache and Qwen's cache product rules. Current pricing is available in the model catalog.

Multimodal baseline

Vision & Video

Input limits in the same class as the Qwen3.6 line

Image limit
16M
pixels
Image batch
256 / 250
URL / Base64
Video batch
64
2 hours / 2GB per video

For new projects that need stronger spatial intelligence or agentic coding, evaluate Qwen3.6 Flash first.

The visual understanding docs list qwen3.5-flash-2026-02-23 with 1M context, 16M pixels per image, 256 URL images, 250 Base64 images, 64 videos, and single videos up to 2 hours / 2GB. It remains a capable worker for document OCR, image extraction, video summaries, and mixed media normalization.

The reason to keep it is operational confidence. If a workflow is already tuned around its outputs, prompts, validators, and fallbacks, Qwen3.5 Flash can stay as the stable path while a Qwen3.6 Flash migration runs in parallel.

Migration path

Upgrade Path

Use it as the stable baseline, not the ceiling for new work

Qwen3.5 Flash
Stable
existing batch work and low-risk compatibility
Qwen3.6 Flash
Upgrade
stronger agentic coding and spatial intelligence
Qwen3.6 Plus
Review
multimodal lead and higher-quality outputs
01
Keep baseline
Record current quality, latency, failure rate, and cost
02
Dual-run samples
Compare Qwen3.6 Flash on critical task sets
03
Check deltas
Watch code, math, localization, and detection tasks
04
Move gradually
Migrate low-risk queues first while keeping rollback

Qwen changelog states that Qwen3.6 Flash improves overall capability, agentic programming, and spatial intelligence over Qwen3.5 Flash.

The Qwen changelog states that Qwen3.6 Flash improves over Qwen3.5 Flash in overall capability, agent programming, math and code reasoning, and spatial intelligence, especially object localization and detection. Those are the first areas to test when planning an upgrade.

A safe migration keeps Qwen3.5 Flash as the control group: log current quality, latency, validation failures, and reviewer overrides; dual-run representative samples through Qwen3.6 Flash; then move the low-risk queues first. Keep rollback until the hardest prompt classes are covered.

When to use it

  • Existing stable production: workflows already tuned on Qwen3.5 Flash outputs and validation behavior.
  • Low-risk multimodal batches: OCR, extraction, summaries, and normalization where schema checks catch failures.
  • Migration baselines: compare Qwen3.6 Flash quality, latency, and cost against a known reference.
  • Fallback routing: keep a stable legacy path while newer Qwen models roll out gradually.

CrossModel exposes Qwen3.5 Flash through an OpenAI-compatible API. Current pricing is available in the model catalog.