- Text Generation
Trinity Large Thinking is Arcee AI’s open-weight, 398–400B-parameter sparse Mixture-of-Experts model focused on advanced reasoning and long-horizon agentic tasks. It is notable for activating only about…
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Qwen3.5-35B-A3B
- Text Generation
Qwen3.5-35B-A3B is a 35B-parameter Mixture-of-Experts vision-language model from Qwen with a 262K-token context window, optimized for high-throughput inference and long-context reasoning.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B is a 35B-parameter hybrid Mixture-of-Experts vision-language model from Qwen, offering a 262K-token native context window for efficient text and multimodal generation. It is mainly used for complex assistant-style chat, long-document understanding, and multi-step reasoning workflows, including tool-using and agentic applications. It is also used for high-volume or always-on workloads where its sparse MoE design reduces active parameters to around 3B per token, improving throughput and cost efficiency. The model is part of the Qwen3.5 series of open Qwen models, which span multiple sizes and architectures and serve as successors to earlier Qwen 2.x and 3.x generations.
Model capabilities
5 Core Capabilities
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Conversational Reasoning
Handles multi-turn dialogue, follows instructions, and maintains context to provide coherent, relevant answers across diverse topics.
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Code Assistance
Understands and generates code snippets, explains programming concepts, and helps debug logic errors in multiple programming languages.
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Multilingual Translation
Translates between major languages, preserving meaning and tone while handling informal expressions and technical terminology.
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Image Interpretation
Interprets images to identify objects, scenes, relationships, and basic text content, supporting visual question answering tasks.
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Document OCR
Extracts machine-readable text from images or scanned documents, enabling downstream search, analysis, and structured processing.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Financial Document Analysis
- Legal Case Research
- Regulatory Case Monitoring
- E-commerce Product Assistance
- Code Generation and Review
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Qwen3.5-35B-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.20 | $0.40 | 128K |
| Qwen | Asia Pacific | ~220ms | ~40 tps | 99.9% | ~$0.40 | ~$0.80 | ~64K |
| Alibaba Cloud | Asia Pacific | ~260ms | ~30 tps | 99.9% | ~$0.45 | ~$0.90 | ~64K |
| Together AI | US East | ~180ms | ~50 tps | 99.9% | ~$0.30 | ~$0.60 | ~32K |
| Fireworks AI | US West | ~170ms | ~55 tps | 99.9% | ~$0.28 | ~$0.56 | ~32K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5-35B-A3B | GPT-4.1-mini | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~280ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.30 | $0.15 | $3.00 |
| Output Price ($/1M) | $0.60 | $0.60 | $15.00 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 40 tps | 50 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 12.4B
- Prompt tokens processed (last 30 days)
- 9.4B
- Completion tokens generated (last 30 days)
- 7.6M
- API requests served (last 30 days)
- 99.8%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers based on latency, quality, and constraints—without changing your integration or redeploying.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend by automatically selecting cheaper equivalents, downshifting models for non-critical paths, and enforcing budget guardrails at the endpoint or project level.
Max performance, min cost -
Resilient Fallback Flows
Design multi-step failover chains that transparently retry on alternate models or regions, so outages and rate limits don’t take your product offline.
Built-in high availability -
Deep LLM Observability
Inspect every call with traces, logs, and metrics across providers—latency, token usage, errors, and outcomes—so you can debug, tune, and ship safely.
See every token -
Task-Level Abstractions
Describe what you need—chat, extraction, ranking, tools—and let LLM.API handle prompts, model quirks, and formatting so you ship features, not glue code.
Code to tasks, not models -
High-Throughput Batch APIs
Run large jobs across models and providers with one API, handling chunking, retries, and aggregation to drive down latency and cost at scale.
Process millions, simply
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose LLM for chatbots, agents, and virtual assistants.
- You need solid reasoning and coding abilities without paying for frontier-tier models.
- Your use case involves multilingual support across many languages with generally good fluency.
- Your use case involves batch offline inference where larger weights are acceptable.
- You need an open-weight model that can be self-hosted and heavily customized.
- You need good performance on typical enterprise tasks like summarization, extraction, and rewriting.
- Your use case involves fine-tuning or LoRA adaptation on domain-specific corpora.
Avoid if...
- You need cutting-edge performance on complex reasoning tasks rivaling the very best frontier models.
- Your workload requires extremely low latency or on-device inference on small consumer hardware.
- You need tight integration with proprietary ecosystems that only support other vendor models.
- You need guaranteed, battle-tested safety filters comparable to top commercial closed models.
- Your workload requires very long context windows beyond what this model reliably supports.
- You need specialized vision, speech, or multimodal capabilities not included in this text model.
- Your workload requires strict enterprise compliance certifications only available from major cloud providers.
FAQ
Frequently Asked Questions
-
What is Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B is a 35B-parameter Qwen model optimized for fast, cost-efficient text generation via the LLM.API gateway.
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What is Qwen3.5-35B-A3B best suited for?
Qwen3.5-35B-A3B is best for general-purpose coding assistance, tool-using agents, data processing, and complex reasoning over long contexts.
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What is the context window of Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B supports a context window of up to 32K tokens through LLM.API, including prompt and response tokens.
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Does Qwen3.5-35B-A3B support multimodal inputs like images or audio?
Qwen3.5-35B-A3B on LLM.API currently supports text-only input and output, without native image or audio understanding.
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How is Qwen3.5-35B-A3B priced on LLM.API?
Qwen3.5-35B-A3B is billed per 1,000 tokens on LLM.API, with separate rates for prompt and completion tokens defined in the pricing page.
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What latency should I expect from Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B typically has moderate latency with streaming token output, suitable for interactive applications and backend batch workloads.
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How do I call Qwen3.5-35B-A3B through LLM.API?
You select the model name "Qwen3.5-35B-A3B" in the LLM.API completion or chat endpoint and pass your prompt plus standard configuration parameters.
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How does Qwen3.5-35B-A3B compare to smaller Qwen models?
Compared to smaller Qwen variants, Qwen3.5-35B-A3B generally offers stronger reasoning and coding performance at higher cost and slightly higher latency.
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What are the main limitations of Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B can hallucinate facts, lacks real-time knowledge or browsing, and may underperform on highly specialized domain tasks.
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Can I use Qwen3.5-35B-A3B with tools or function calling on LLM.API?
Yes, Qwen3.5-35B-A3B can be used with LLM.API's tool or function-calling interfaces by defining tools in the request payload.
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