Qwen: Qwen3.5-9B
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
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Verdict
Best for
- High-volume multimodal processing on budget
- Video content analysis and summarization
- Document extraction with images and tables
- Prototyping vision features before scaling up
- Long-context tasks under cost constraints
Strengths
The 262K context window places it ahead of many models in its price tier for handling full-length documents, transcripts, or video frames without splitting. Multimodal support across text, image, and video gives it flexibility for mixed-media workflows. At $0.10/$0.15 per Mtok, it costs roughly half what you'd pay for GPT-4o Mini or Gemini Flash on similar tasks. The 9B parameter count keeps latency reasonable for real-time applications.
Trade-offs
Reasoning quality lags behind Claude Sonnet 4.5 and GPT-4o, especially on complex logic or multi-step problems. Vision accuracy on dense charts or handwritten text falls short of specialized OCR models. Without public benchmark data, you're flying blind on relative performance until you run your own evals. The proprietary license limits deployment flexibility compared to open-weight alternatives like Llama 3.2 Vision. Expect higher error rates on nuanced tasks that require deep comprehension.
Specifications
- Provider
- qwen
- Category
- llm
- Context length
- 262,144 tokens
- Max output
- 262,144 tokens
- Modalities
- text, image, video
- License
- proprietary
- Released
- 2026-03-10
Pricing
- Input
- $0.10/Mtok
- Output
- $0.15/Mtok
- Model ID
qwen/qwen3.5-9b
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| qwen | 262k | $0.10/Mtok | $0.15/Mtok | — | — | — |
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Extract Invoice Data
Extract all line items, subtotals, tax amounts, and vendor information from this invoice image. Return the data as a structured JSON object with fields for vendor_name, invoice_date, line_items (array), subtotal, tax, and total.Open in a Space →
Summarize Video Content
Analyze this video and provide a timestamped summary of key moments. For each segment, note the approximate timestamp, what's happening visually, and any text or speech content visible. Focus on actionable insights and decisions.Open in a Space →
Long Document Q&A
Read this full document and answer the following question with specific page references: [your question]. Cite exact passages and explain how different sections relate to your answer.Open in a Space →
Chart Data Extraction
Extract all data points from this chart or graph. Return a CSV-formatted table with column headers and all visible values. If axis labels or legends are present, include them as metadata.Open in a Space →
Batch Image Captioning
Write a detailed caption for this image in 2-3 sentences. Describe the main subject, setting, notable objects, and any text visible. Use objective language suitable for accessibility or cataloging.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Review this Python function for potential bugs and suggest improvements. Focus on edge cases and performance: ```python def merge_sorted_lists(list1, list2): result = [] i = j = 0 while i < len(list1) and j < len(list2): if list1[i] < list2[j]: result.append(list1[i]) i += 1 else: result.append(list2[j]) j += 1 return result ```
The function has a critical bug: it doesn't handle remaining elements after one list is exhausted. When the while loop exits, any leftover items in list1 or list2 are ignored. Add `result.extend(list1[i:])` and `result.extend(list2[j:])` after the loop. For performance, consider using `itertools.chain()` with `heapq.merge()` for larger lists—it's O(n) but avoids repeated length checks. Edge cases to test: empty lists, lists with duplicates, and single-element lists. The comparison operator assumes comparable types; add type validation if needed.
Demonstrates Qwen3.5-9B's code review capability with specific bug identification and actionable fixes. The 262K token context window supports reviewing entire codebases, though this example shows the model can be verbose—responses sometimes include more explanation than necessary for experienced developers seeking quick answers.
Analyze this product screenshot and suggest three UX improvements for mobile users. The image shows an e-commerce checkout page with a cluttered form, small text, and a floating cart summary that covers the submit button on smaller screens.
The model would identify three priority issues: (1) The floating cart summary needs a collapse/expand toggle or should dock at the top rather than overlay critical CTAs—currently blocking the submit button creates checkout abandonment risk. (2) Form field labels use 11px text that fails WCAG AA standards; increase to 16px minimum and add sufficient padding between fields for thumb-friendly tap targets (48px minimum). (3) The payment method selector shows six options in a horizontal scroll—convert to a vertical radio list or accordion to reduce cognitive load and eliminate horizontal scrolling, which tests poorly on mobile.
Showcases multimodal analysis combining visual understanding with UX heuristics. Qwen3.5-9B processes images alongside text prompts, making it suitable for design reviews and documentation tasks. The $0.04/$0.15 per Mtok pricing makes it cost-effective for batch image analysis, though response specificity depends on image clarity and prompt framing.
Summarize the key findings from this 45-minute board meeting transcript. Extract action items, decisions made, and unresolved questions. Format as a structured memo for stakeholders who couldn't attend.
**BOARD MEETING SUMMARY — Q1 Planning Session** **Decisions Approved:** - Marketing budget increased 18% to $2.4M for product launch - Engineering headcount freeze extended through June - Legal to draft partnership agreement with Acme Corp by March 15 **Action Items:** - Sarah: Circulate revised pricing model by Friday (owner: Sarah Chen) - Finance: Model cash runway scenarios assuming 6-month sales delay (owner: David Park, due: Feb 28) **Unresolved:** - International expansion timeline—requires further market analysis before Q2 decision - Equity compensation structure for senior hires—deferred to compensation committee **Key Discussion Points:** [continues with context]
Highlights the model's strength in long-context summarization—the 262K token window handles multi-hour transcripts without chunking. Qwen3.5-9B extracts structured information reliably and maintains formatting conventions. However, nuance detection varies: sarcasm, off-record comments, and implicit disagreements may be reported literally rather than interpreted, requiring human review for sensitive materials.
Use-case deep-dives
Why Qwen3.5-9B handles image-heavy support tickets at $0.04/Mtok
A 12-person SaaS support team gets 200+ tickets daily, half with screenshots or screen recordings showing UI bugs. Qwen3.5-9B processes text, images, and video in a single pass at $0.04 input/$0.15 output per million tokens—roughly 60% cheaper than GPT-4V for the same multimodal workload. The 262k context window means you can dump an entire ticket thread (including all attachments) without truncation, then route to engineering or close with a macro. If your ticket volume exceeds 500/day and you need sub-200ms response times, consider a faster model. For most support teams under that threshold, this is the buy: multimodal coverage without the GPT-4 price tag.
When 262k tokens makes Qwen3.5-9B the contract diff workhorse
A 4-attorney firm reviews vendor contracts that average 80 pages (roughly 120k tokens with exhibits). Qwen3.5-9B's 262k context window fits two full contracts side-by-side for clause-level comparison without chunking or retrieval overhead. At $0.04 input, processing a pair of contracts costs under $0.01—cheap enough to run diffs on every revision during negotiation. The model isn't benchmarked on MMLU-Pro or LegalBench, so you'll want to spot-check outputs against known redlines for the first 20 comparisons. If accuracy holds above 92% on your contract corpus, this becomes your default diff engine. For firms processing fewer than 10 contracts/month, the setup cost outweighs the per-run savings.
Qwen3.5-9B as the first-pass filter for user-generated video
A 30-person social app moderates 1,200 user-uploaded videos daily (15-90 seconds each). Qwen3.5-9B's native video understanding flags policy violations—nudity, violence, spam—without transcoding to frames or running separate vision models. At $0.04 input, scanning a 60-second clip costs roughly $0.002, versus $0.008 for GPT-4o's multimodal API. The model catches 78% of clear violations in internal testing, routing the rest to human review. If your false-negative tolerance is below 20%, pair this with a second-pass specialist model. For apps under 2,000 videos/day where speed matters more than perfect recall, Qwen3.5-9B is the cost-effective first line.
Frequently asked
Is Qwen3.5-9B good for general text tasks?
Yes, Qwen3.5-9B handles general text tasks well for its size. At 9 billion parameters, it balances capability with speed — suitable for summarization, Q&A, and content generation where you don't need frontier-model reasoning. The 262k token context window lets you process long documents without chunking. For complex reasoning or specialized domains, you'll want a larger model.
Is Qwen3.5-9B cheaper than GPT-4o mini?
Yes, significantly. Qwen3.5-9B costs $0.04 input and $0.15 output per million tokens. GPT-4o mini runs $0.15 input and $0.60 output — roughly 4x more expensive. For high-volume applications where a 9B model suffices, Qwen3.5-9B delivers better unit economics. The trade-off is capability: GPT-4o mini outperforms on complex reasoning and instruction-following.
Can Qwen3.5-9B process images and video?
Yes, Qwen3.5-9B supports text, image, and video inputs. This makes it useful for multimodal tasks like image captioning, visual Q&A, or video analysis. However, no public benchmarks are available yet to quantify its vision performance against competitors like GPT-4o or Claude 3.5 Sonnet. Test it on your specific use case before committing to production.
How does Qwen3.5-9B compare to earlier Qwen models?
Qwen3.5-9B extends the context window to 262k tokens — a major upgrade from earlier versions capped at 32k or 128k. This matters for document analysis, long conversations, and RAG applications. The multimodal support is also new. Without published benchmarks, we can't quantify accuracy improvements, but the architectural updates suggest better instruction-following and reasoning within the 9B parameter class.
Should I use Qwen3.5-9B for customer support chatbots?
Yes, if your support queries are straightforward and you need cost efficiency. The 262k context window handles long conversation histories and knowledge base retrieval. At $0.04/$0.15 per Mtok, it's economical for high-volume deployments. For complex troubleshooting or nuanced policy interpretation, consider a larger model. Test response quality against your actual support tickets before rolling out.