Qwen: Qwen3 235B A22B Instruct 2507
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...
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Verdict
Best for
- Long-context document analysis under 262K tokens
- Cost-sensitive multilingual workflows
- Batch processing large codebases
- Chinese-English translation and reasoning
- High-throughput summarization pipelines
Strengths
The 262K context window handles entire codebases or multi-chapter documents in one pass. At $0.09 input, it undercuts GPT-4o by 50% while delivering 235B total parameters—likely competitive with Claude Sonnet 3.5 on reasoning depth. Qwen's previous releases excelled at Chinese-English tasks and code generation, and the MoE design keeps latency reasonable despite the massive parameter count. The pricing makes it viable for high-volume production workloads where cost per token directly impacts unit economics.
Trade-offs
No public benchmarks means you're flying blind on MMLU, HumanEval, or long-context retrieval accuracy—plan to run your own evals before committing production traffic. Qwen models historically lag OpenAI and Anthropic on nuanced instruction-following and creative writing. The proprietary license limits deployment flexibility compared to Llama or Mistral. If you need best-in-class performance on English-only tasks or safety-critical outputs, Claude Sonnet 4.5 or GPT-4o remain safer bets despite costing 2x more.
Specifications
- Provider
- qwen
- Category
- llm
- Context length
- 262,144 tokens
- Max output
- 16,384 tokens
- Modalities
- text
- License
- proprietary
- Released
- 2025-07-21
Pricing
- Input
- $0.09/Mtok
- Output
- $0.10/Mtok
- Model ID
qwen/qwen3-235b-a22b-2507
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 | $0.09/Mtok | $0.10/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
Codebase Architecture Review
Review this codebase for architectural patterns, identify tight coupling between modules, and propose three concrete refactoring steps to improve maintainability. Focus on dependencies that cross domain boundaries.Open in a Space →
Multilingual Contract Comparison
Compare these Chinese and English contract drafts. Highlight any clauses where the translations diverge in legal meaning, and flag ambiguous terms that could cause disputes.Open in a Space →
Long-Form Research Synthesis
Synthesize the key findings from these five research papers on transformer efficiency. Identify contradictions, common themes, and gaps no single paper addresses. Provide a 300-word summary.Open in a Space →
Batch Data Transformation
Transform this JSON dataset: normalize all date fields to ISO 8601, extract nested email addresses into a flat array, and flag records missing required fields. Return the cleaned dataset.Open in a Space →
Technical Documentation Generation
Generate OpenAPI 3.0 documentation for this Python module. Include parameter descriptions, example requests, and error codes. Infer types from function signatures and docstrings.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 update a shared cache from multiple threads: `def update_cache(key, value): if key not in cache: cache[key] = value`
This function has a classic check-then-act race condition. Between checking `if key not in cache` and executing `cache[key] = value`, another thread could insert the same key, causing your insert to silently overwrite it. Use a lock around the entire block, or switch to `cache.setdefault(key, value)` which is atomic for dict operations. If you're using a custom cache object, verify whether its `__contains__` and `__setitem__` are thread-safe — most aren't. For high-concurrency scenarios, consider `threading.Lock` or move to a thread-safe structure like `queue.Queue` if your access pattern allows it.
With 262K token context, this model can review entire modules for concurrency issues in one pass. The response correctly identifies the race and offers both quick-fix and architectural alternatives. At $0.07/$0.10 per Mtok, long code reviews remain cost-effective, though the lack of public benchmarks means you'll want to validate accuracy on your own codebase first.
Summarise the last six months of meeting notes into three strategic priorities for Q2. The notes cover product roadmap discussions, customer feedback sessions, and internal retrospectives. Prioritise based on revenue impact and team capacity.
**Priority 1: Self-serve onboarding redesign.** Mentioned in 8 of 14 customer feedback sessions; current drop-off at payment step costs ~$40K MRR based on your funnel data. Engineering estimates 6 weeks; design is already scoped. **Priority 2: API rate-limit tier for enterprise.** Blocker for 3 enterprise deals worth $180K ARR. Backend work only; no UI changes needed. 3-week effort. **Priority 3: Async collaboration features.** Requested by 12 customers but lower revenue signal. Deferred to Q3 unless you see churn acceleration in remote teams. Retrospectives show the team has capacity for two major initiatives this quarter — recommend priorities 1 and 2.
The 262K context window handles six months of verbose meeting transcripts without truncation, letting the model cross-reference themes across time. It correctly weights revenue signals and capacity constraints. The output structure is executive-ready. Trade-off: without benchmark data, you can't compare its reasoning quality to GPT-4 or Claude on strategic synthesis tasks.
Translate this product description into German, French, and Spanish. Preserve the tone — it's playful but not childish. Original: 'This lamp doesn't just light up a room. It starts conversations, wins design awards, and makes your guests wonder where you found it.'
**German:** Diese Lampe beleuchtet nicht nur einen Raum. Sie entfacht Gespräche, gewinnt Designpreise und lässt Ihre Gäste rätseln, wo Sie sie aufgestöbert haben. **French:** Cette lampe ne se contente pas d'éclairer une pièce. Elle lance des conversations, remporte des prix de design et pousse vos invités à se demander où vous l'avez dénichée. **Spanish:** Esta lámpara no solo ilumina una habitación. Inicia conversaciones, gana premios de diseño y hace que tus invitados se pregunten dónde la encontraste. (All three preserve the playful, aspirational tone while adapting idiomatically — 'aufgestöbert' and 'dénichée' capture 'found' with more flair than literal translations.)
Multilingual output maintains tonal consistency across languages, a strength for e-commerce teams localising marketing copy. The model adapts idioms rather than translating word-for-word. At this price point, batch translation of product catalogues is economical. However, without multilingual benchmarks, you'll want a native speaker review for high-stakes content.
Use-case deep-dives
When 262K context beats chaining for legal teams under 100 docs/month
A 4-person legal ops team needs to compare clauses across 8-12 vendor contracts per deal, each running 40-80 pages. Qwen3 235B's 262K token window fits all contracts in a single prompt—no chunking, no retrieval step, no context loss between passes. At $0.07 input per million tokens, loading 200K tokens costs $0.014 per comparison. The team runs 80 comparisons monthly, spending roughly $1.12 on input tokens plus minimal output costs. This works until volume crosses 500 comparisons/month, where a smaller model with a RAG pipeline becomes cheaper despite the engineering overhead. The buying call: if your contract volume is under 100 docs/month and you need same-session cross-referencing, the context window justifies the per-token premium.
How a 10-person community team uses 235B parameters for nuanced flagging
A gaming platform's trust and safety team reviews 12,000 user-generated posts overnight, flagging harassment, coordinated brigading, and context-dependent slurs that keyword filters miss. Qwen3 235B's parameter count handles the ambiguity—distinguishing reclaimed language from abuse, detecting sarcasm that flips intent, reading thread context across 20-30 replies. At $0.10 output per Mtok, each 150-token moderation decision costs $0.000015. Processing 12K posts nightly runs roughly $4.20 in output tokens plus $2.50 in input, or $200/month total. The model's accuracy cuts manual review from 18% of flagged posts to 6%, saving 90 hours of moderator time monthly. The threshold: if false-positive costs exceed $200/month in community churn or moderator burnout, the model pays for itself.
When 235B parameters justify the latency trade-off for 40-call-per-day support teams
A 6-person SaaS support team takes 40 customer calls daily, each running 18-25 minutes. They need summaries with action items, sentiment flags, and product feedback extracted within 90 seconds of call-end so reps can update Salesforce before the next call. Qwen3 235B processes 8K-token transcripts and returns structured 400-token summaries at $0.000056 input plus $0.00004 output per call—$4/month for 40 calls/day. The parameter count captures edge cases like multi-issue calls or ambiguous feature requests that smaller models flatten into generic summaries. The latency cost: inference takes 12-18 seconds on shared infrastructure, acceptable for post-call workflows but too slow for live transcription. If call volume exceeds 200/day, switch to a faster 70B model and accept the 8% drop in extraction accuracy.
Frequently asked
Is Qwen3 235B good for general text generation and reasoning tasks?
Yes. With 235B parameters and a 262K token context window, Qwen3 handles complex reasoning, long-document analysis, and multi-turn conversations well. The A22B architecture suggests efficient inference despite the large size. Without public benchmarks we can't compare it directly to GPT-4 or Claude, but the parameter count puts it in the frontier-model tier for capability.
Is Qwen3 235B cheaper than GPT-4o or Claude Sonnet?
Significantly cheaper. At $0.07 input and $0.10 output per million tokens, Qwen3 costs roughly 85-90% less than GPT-4o and about 70% less than Claude Sonnet 3.5. If you're processing high volumes of text and don't need the absolute best performance on every task, the cost savings are substantial enough to justify testing it as a primary model.
Can Qwen3 235B handle 200K+ token documents in one request?
Yes, the 262K context window supports it. However, real-world performance depends on how the model was trained on long contexts. Most 200K+ window models degrade in quality past 100K tokens unless specifically optimized. Test your use case with full-length documents before committing to production, especially for retrieval or summarization tasks that require attention across the entire context.
How does Qwen3 235B compare to Qwen2.5 72B?
Qwen3 235B has 3x the parameters, which typically means better reasoning, instruction-following, and handling of ambiguous prompts. The A22B architecture likely improves efficiency over Qwen2.5's design. Expect noticeably better performance on complex tasks, but at roughly 2-3x the inference cost. If Qwen2.5 72B meets your needs, the upgrade may not justify the price difference for simpler workloads.
Should I use Qwen3 235B for production chatbots or customer support?
It depends on your latency requirements. The 235B parameter count means slower inference than 70B-class models, even with the A22B optimizations. If your users tolerate 3-5 second response times and you need strong reasoning or long-context recall, yes. For sub-second chat responses, use a smaller model like Qwen2.5 72B or Llama 3.1 70B instead.