{"id":1761,"date":"2026-04-02T23:01:15","date_gmt":"2026-04-02T23:01:15","guid":{"rendered":"https:\/\/wiro.ai\/blog\/?p=1761"},"modified":"2026-03-16T06:51:42","modified_gmt":"2026-03-16T06:51:42","slug":"qwen3-5-27b-6-quick-tests-on-reasoning-parsing-and-code","status":"publish","type":"post","link":"https:\/\/wiro.ai\/blog\/qwen3-5-27b-6-quick-tests-on-reasoning-parsing-and-code\/","title":{"rendered":"Qwen3.5-27B: 6 Quick Tests on Reasoning, Parsing, and Code"},"content":{"rendered":"<h2>Qwen3.5-27B: 6 Quick Tests on Reasoning, Parsing, and Code<\/h2>\n<p>Qwen3.5-27B shows how a 27B multimodal model handles long-context reasoning and mixed tasks. This short review runs six focused tests: product copy, invoice parsing, translation, a Python utility, a JSON Schema, and a short summary task. Each test includes the exact prompt and the raw model output.<\/p>\n<h2>Model<\/h2>\n<p><a href=\"https:\/\/wiro.ai\/models\/qwen\/qwen3-5-27b\">Qwen3.5-27B on Wiro<\/a><\/p>\n<h2>Quick summary<\/h2>\n<ul>\n<li>Model type: 27B multimodal LLM with extended context.<\/li>\n<li>Strengths: instruction following, structured outputs, long-context handling.<\/li>\n<li>Observed limits: verbose &#8220;thinking&#8221; sections appear by default; prompts must force concise output for clean machine-readable results.<\/li>\n<\/ul>\n<h2>Tests and outputs<\/h2>\n<h3>Test 1 \u2014 Product copy (4 sentences)<\/h3>\n<p><strong>Prompt:<\/strong> Write a 4 sentence product description for noise cancelling headphones. Plain language. No hype. Mention battery life and comfort.<\/p>\n<pre><code>These headphones reduce outside sounds to help you focus on your audio. The padded ear cups are designed to stay comfortable during extended use. A single charge provides up to thirty hours of continuous listening time. They connect wirelessly to your devices for easy setup.<\/code><\/pre>\n<h3>Test 2 \u2014 Invoice \u2192 JSON<\/h3>\n<p><strong>Prompt:<\/strong> Convert the following messy invoice into JSON with keys invoice_id, date, vendor, total_usd, items (array of {name, qty, unit_price_usd}). Return JSON only, no extra text.<\/p>\n<p>Invoice #A-1049<br \/>\nDate: 2026\/03\/01<br \/>\nVendor: Blue Finch Tools<br \/>\n&#8211; 2x Torque wrench (1\/2 inch drive) $39.50 each<br \/>\n&#8211; 1x Safety glasses (clear) $12.00<br \/>\nTax: $7.41<br \/>\nTotal: $98.41<\/p>\n<pre><code>{\n  \"invoice_id\": \"A-1049\",\n  \"date\": \"2026\/03\/01\",\n  \"vendor\": \"Blue Finch Tools\",\n  \"total_usd\": 98.41,\n  \"items\": [\n    { \"name\": \"Torque wrench (1\/2 inch drive)\", \"qty\": 2, \"unit_price_usd\": 39.5 },\n    { \"name\": \"Safety glasses (clear)\", \"qty\": 1, \"unit_price_usd\": 12.0 }\n  ]\n}<\/code><\/pre>\n<h3>Test 3 \u2014 Turkish \u2192 English (short translation)<\/h3>\n<p><strong>Prompt:<\/strong> Translate to English. Return English only.<\/p>\n<p>Turkish: Bu modeli API ile denedim ama cevaplarda &lt;think&gt; bolumu geliyor. Kapatmanin yolu var mi?<\/p>\n<pre><code>I tried this model with the API, but the &lt;think&gt; section appears in the responses. Is there a way to disable it?<\/code><\/pre>\n<h3>Test 4 \u2014 Python utility: parse_duration<\/h3>\n<p><strong>Prompt:<\/strong> Write a Python function parse_duration(s) that accepts strings like 2h 15m, 90m, 45s, 1h and returns total seconds as an int. Handle spaces. Assume lowercase h m s only. Include 4 example calls as comments. Return code only.<\/p>\n<pre><code>import re\n\ndef parse_duration(s):\n    total_seconds = 0\n    for num_str, unit in re.findall(r\"(\\d+)([hms])\", s):\n        num = int(num_str)\n        if unit == 'h':\n            total_seconds += num * 3600\n        elif unit == 'm':\n            total_seconds += num * 60\n        elif unit == 's':\n            total_seconds += num\n    return total_seconds\n\n# Examples:\n# print(parse_duration(\"2h 15m\"))  # 8100\n# print(parse_duration(\"90m\"))     # 5400\n# print(parse_duration(\"45s\"))     # 45\n# print(parse_duration(\"1h\"))      # 3600<\/code><\/pre>\n<h3>Test 5 \u2014 JSON Schema (draft 2020-12)<\/h3>\n<p><strong>Prompt:<\/strong> Create a minimal JSON Schema (draft 2020-12) for an event object with fields: id (string), startTime (string, date-time), endTime (string, date-time), title (string), location (string, optional), attendees (array of strings, optional). Return JSON only.<\/p>\n<pre><code>{\n  \"$schema\": \"https:\/\/json-schema.org\/draft\/2020-12\/schema\",\n  \"type\": \"object\",\n  \"properties\": {\n    \"id\": { \"type\": \"string\" },\n    \"startTime\": { \"type\": \"string\", \"format\": \"date-time\" },\n    \"endTime\": { \"type\": \"string\", \"format\": \"date-time\" },\n    \"title\": { \"type\": \"string\" },\n    \"location\": { \"type\": \"string\" },\n    \"attendees\": { \"type\": \"array\", \"items\": { \"type\": \"string\" } }\n  },\n  \"required\": [\"id\", \"startTime\", \"endTime\", \"title\"]\n}<\/code><\/pre>\n<h3>Test 6 \u2014 Short summary + Best for<\/h3>\n<p><strong>Prompt:<\/strong> Summarize the text in 5 bullet points, then add a 1-line Best for: statement. Keep it short.<\/p>\n<pre><code>- Qwen3.5-27B uses a thinking stage before final answers.\n- Thinking content appears in a separate block by default.\n- Direct responses require API flags to disable thinking.\n- The model supports very long context windows (262K tokens).\n- Context can be extended further depending on serving stack.\n\nBest for: complex reasoning tasks that need very long context windows.<\/code><\/pre>\n<h2>Short assessment<\/h2>\n<p>The model follows instructions and returns structured outputs when prompts require them. The default thinking block adds verbosity; set API flags for direct answers when machine-readable output is needed. For tasks that need large context or detailed reasoning, Qwen3.5-27B performs well.<\/p>\n<h2>Try it<\/h2>\n<p>Try the model on Wiro: <a href=\"https:\/\/wiro.ai\/models\/qwen\/qwen3-5-27b\">https:\/\/wiro.ai\/models\/qwen\/qwen3-5-27b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Qwen3.5-27B: 6 Quick Tests on Reasoning, Parsing, and Code Qwen3.5-27B shows how a 27B multimodal model handles long-context reasoning and mixed tasks.&hellip;<\/p>\n","protected":false},"author":4,"featured_media":1764,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[52],"tags":[70,145,100],"class_list":["post-1761","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-model-reviews","tag-llm","tag-multimodal","tag-qwen"],"_links":{"self":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts\/1761","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/comments?post=1761"}],"version-history":[{"count":1,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts\/1761\/revisions"}],"predecessor-version":[{"id":1763,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts\/1761\/revisions\/1763"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/media\/1764"}],"wp:attachment":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/media?parent=1761"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/categories?post=1761"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/tags?post=1761"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}