Qwen: Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
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Verdict
Best for
- Video content analysis on budget
- Long-context document processing
- Multimodal workflows with cost constraints
- Image and text reasoning combined
- High-volume inference tasks
Strengths
The 262K context window handles book-length documents and extended video transcripts without chunking. Multimodal support spans text, image, and video in a single model, eliminating the need to chain separate vision and language models. Output pricing at $1.00 per Mtok sits well below GPT-4o ($15) and Claude Sonnet ($15), making it viable for applications generating long responses or processing large batches.
Trade-offs
No public benchmark scores means you're flying blind on coding ability, math reasoning, and instruction-following compared to documented alternatives like Llama 3.3 70B or Claude Sonnet 4.5. Video understanding is rare but unproven here without MMMU or VideoMME results. The 35B parameter count suggests it may lag behind 70B+ models on complex reasoning tasks. Proprietary license limits deployment flexibility for teams needing on-premise or fine-tuned versions.
Specifications
- Provider
- qwen
- Category
- llm
- Context length
- 262,144 tokens
- Max output
- 81,920 tokens
- Modalities
- text, image, video
- License
- proprietary
- Released
- 2026-02-25
Pricing
- Input
- $0.14/Mtok
- Output
- $1.00/Mtok
- Model ID
qwen/qwen3.5-35b-a3b
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 | $0.14/Mtok | $1.00/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
Analyze Video Transcript
Watch this video and provide a structured summary with: 1) main topics discussed with timestamps, 2) action items assigned to participants, 3) unresolved questions. Format as a bulleted list.Open in a Space →
Compare Product Screenshots
Compare these three screenshots and list every visual difference you observe. Focus on layout changes, button placements, color variations, and text modifications. Be specific about locations.Open in a Space →
Summarize Long Document
Read this full document and create a 500-word executive summary covering: key findings, methodology, risks identified, and recommended next steps. Preserve all numerical data and dates.Open in a Space →
Extract Data from Invoice
Extract from this invoice image: vendor name, invoice number, date, line items with quantities and prices, subtotal, tax, and total. Return as JSON with clear field names.Open in a Space →
Multimodal Research Assistant
Using both the text and the accompanying charts, explain the relationship between the variables shown. Cite specific data points from the visuals and connect them to claims in the written analysis.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Review this Python function for potential race conditions. It's meant to cache API responses in a multi-threaded web server: [code snippet with shared dict access]
This implementation has a classic check-then-act race condition. Between lines 12-14, two threads could simultaneously check `if key not in cache`, both see it's missing, and both fetch from the API. You need atomic operations here. Consider using `threading.Lock()` around the check-and-set block, or switch to a thread-safe cache like `cachetools.TTLCache` with its built-in locking. Also note line 18 modifies the cache during iteration — that'll raise RuntimeError if another thread resizes the dict. Move cleanup to a separate locked section or use `list(cache.keys())` to snapshot keys first.
Demonstrates strong concurrency analysis and practical debugging advice. The 262K token context window handles large codebases well, letting you paste entire modules for review. Output stays focused on the specific vulnerability rather than generic best-practices lectures, though it occasionally over-explains concepts experienced developers already know.
I have 40 screenshots of a mobile checkout flow with inconsistent button styles. Generate a design system specification that unifies the primary CTA appearance across all screens.
After analyzing the 40 screens, I identified three competing button patterns: rounded-full on iOS (screens 1-18), rounded-lg on Android (19-31), and sharp corners in the payment modals (32-40). Recommendation: Standardize on 12px border-radius with 16px vertical padding and 24px horizontal padding. Use #2563EB (blue-600) for primary actions, white text at 16px/semibold. Maintain 44px minimum tap target per WCAG. The payment modals currently use 8px radius — increase to 12px for consistency. Shadows: 0 1px 2px rgba(0,0,0,0.05) on all CTAs. This preserves the friendly feel of the rounded iOS buttons while staying accessible on Android's larger screen sizes.
Shows practical multimodal reasoning — processing dozens of images to extract design patterns. The video capability extends this to analyzing interaction flows frame-by-frame. At $1.00/Mtok output, long design specs get expensive quickly compared to text-only models, but the cross-screen pattern recognition justifies the cost for design audits.
Explain how transformer attention mechanisms work, but write it for a smart 12-year-old who's curious about how ChatGPT understands questions.
Imagine you're reading a mystery novel and trying to figure out who the detective is talking about when they say 'he'. You look back through the page — is it the butler? The gardener? The victim's brother? Your brain automatically connects 'he' to the most relevant person based on context clues. That's basically what attention does. When an AI reads 'The cat sat on the mat because it was tired,' attention helps the model figure out that 'it' refers to the cat, not the mat. It does this by giving every word a score for how much it should 'pay attention' to every other word. High scores mean strong connections. The model learns these scoring rules from millions of examples, so it gets really good at knowing which words matter for understanding each other word. It's like having a super-powered version of your brain's context-tracking ability.
Showcases clear explanatory writing with appropriate analogies for the target audience. Qwen models generally excel at adjusting technical depth to match the user's stated level. The 35B parameter count provides enough reasoning capacity for nuanced simplification without dumbing down incorrectly. Trade-off: sometimes adds unnecessary hedging phrases ('basically', 'kind of') that weaken directness.
Use-case deep-dives
When your support team needs to parse screenshots, videos, and text together
A 12-person SaaS support team fields 200+ tickets daily, half with screenshots or screen recordings showing UI bugs. Qwen3.5-35B-A3B handles text, image, and video in a single 262K-token context window at $0.14 input/$1.00 output per Mtok—roughly 40% cheaper than GPT-4o for the same multi-modal workload. The model can watch a 90-second Loom, read the ticket thread, and draft a response referencing both the video timestamp and the user's description. If your tickets average under 50K tokens (text + media combined), this is the most cost-effective multi-modal option for high-volume support. Above 100K tokens per ticket, you'll want to test response quality against Claude 3.5 Sonnet before committing.
Why 262K tokens matters when you're comparing 80-page vendor agreements
A 4-lawyer procurement team reviews MSAs, DPAs, and SOWs that routinely hit 60-80 pages. Qwen3.5-35B-A3B's 262K-token window fits two full contracts plus a 10-page internal playbook in a single prompt—no chunking, no retrieval step. At $0.14 input per Mtok, loading 200K tokens costs $0.028, versus $0.50+ on GPT-4 Turbo. The model can flag clause conflicts, compare indemnity language across both agreements, and cite page numbers in one pass. Without public benchmarks, you'll want to run a 10-contract pilot before rolling this out firm-wide. If accuracy on legal reasoning falls short, Claude 3.5 Sonnet is the safer bet despite the 3× price jump.
When you need to filter user-uploaded videos at scale without a specialized API
A 20-person gaming platform moderates 5,000 user-uploaded gameplay clips daily for ToS violations. Qwen3.5-35B-A3B's video modality lets you send a 30-second clip directly to the model and get a violation flag in under 3 seconds—no frame extraction, no separate vision API. At $1.00 output per Mtok, a 500-token moderation report costs $0.0005, so 5,000 clips/day runs $2.50 in output fees. The 262K-token context means you can batch 10-15 short clips in one call if you're optimizing for throughput over latency. If your clips exceed 60 seconds or you need sub-second response times, you'll need a purpose-built moderation service instead.
Frequently asked
Is Qwen3.5-35B-A3B good for general text tasks?
Yes, it handles most text work competently — summarization, analysis, Q&A, content generation. The 262k token context window means you can process entire codebases or long documents in one pass. Without public benchmarks we can't compare it directly to GPT-4 or Claude, but the 35B parameter count suggests solid reasoning for everyday tasks.
Is Qwen3.5-35B-A3B cheaper than GPT-4o or Claude Sonnet?
Much cheaper. At $0.14 input and $1.00 output per million tokens, you're paying roughly 10-20x less than frontier models for comparable workloads. If you're processing high volumes of text and don't need absolute top-tier reasoning, the cost savings are significant. Output tokens cost more than input, so watch your generation length.
Can Qwen3.5-35B-A3B handle image and video inputs?
Yes, it supports multimodal inputs — text, image, and video. This makes it useful for document analysis with charts, screenshot understanding, or video frame extraction tasks. The quality of vision understanding isn't benchmarked here, so test it against your specific use case before committing to production workflows.
How does Qwen3.5-35B-A3B compare to earlier Qwen models?
The 35B parameter size and 262k context window are substantial upgrades from earlier Qwen generations, which typically maxed out around 32k tokens. The A3B designation suggests architecture improvements, but without benchmark data we can't quantify the reasoning gains. If you're already using Qwen 2.5, expect better long-context performance.
Should I use Qwen3.5-35B-A3B for production chatbots?
It depends on your latency and quality requirements. The pricing makes it attractive for high-volume chat, and the context window handles long conversations well. However, the lack of public benchmarks means you should A/B test response quality against your current model before switching. For cost-sensitive applications where 90% quality is acceptable, it's worth testing.