- Text Generation
MiMo-V2-Flash is an open-source Mixture-of-Experts language model from Xiaomi optimized for fast, long-context reasoning and coding. It combines a 309B-parameter MoE architecture with only 15B active…
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Qwen3.5 Plus 2026-02-15
- Text Generation
Qwen3.5 Plus 2026-02-15 is a conversational AI model from Qwen, released on February 15, 2026, designed for general-purpose reasoning and assistance. It is positioned as a stronger, more capable variant within the Qwen3.5 series for everyday and professional workloads.
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 Plus 2026-02-15?
Qwen3.5 Plus 2026-02-15 is a Qwen-developed large language model snapshot from February 15, 2026, aimed at broad, general-purpose use. It is intended for tasks such as drafting and editing text, answering questions, coding help, and other interactive assistant scenarios. It is also suited for integrating into applications that require multi-turn dialogue, tool use, or workflow automation. It belongs to the Qwen3.5 family of models, which iteratively improve on earlier Qwen and Qwen2 generations in capability and reliability.
Model capabilities
5 Core Capabilities
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Advanced Chat
Engages in multi-turn, context-aware conversations, following complex instructions and maintaining coherent dialogue over long interactions.
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Code Reasoning
Understands and generates code snippets, explains programming concepts, and assists with debugging across common languages and frameworks.
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Image Understanding
Interprets images at a high level, supporting tasks like object identification, scene description, and answering questions about visual content.
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Text Translation
Translates text between major languages while preserving meaning and tone, useful for comprehension and cross-language communication.
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Document OCR
Extracts readable text from images or scanned documents, enabling downstream processing, search, or summarization of visual text content.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice Data Extraction
- Legal Document Search
- Regulatory Case Monitoring
- E-commerce Product Assistance
- Code Generation and Review
Transparent pricing
Cost Comparison
LLM API offers the lowest Qwen3.5 Plus–class pricing with faster latency and larger context than major providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 90ms | 120 tps | 99.99% | $0.05 | $0.10 | 256K |
| Qwen | Global | ~160ms | ~70 tps | ~99.9% | ~$0.08 | ~$0.16 | ~128K |
| OpenAI | Global | ~200ms | ~60 tps | ~99.9% | ~$0.10 | ~$0.20 | ~128K |
| Azure AI | US East | ~190ms | ~55 tps | ~99.9% | ~$0.11 | ~$0.22 | ~128K |
| AWS Bedrock | US West | ~210ms | ~50 tps | ~99.9% | ~$0.12 | ~$0.24 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5 Plus 2026-02-15 | GPT-4.1 Mini | Claude 3.5 Haiku |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~230ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.20 | $0.15 | $0.18 |
| Output Price ($/1M) | $0.60 | $0.60 | $0.72 |
| Max Output Tokens | 8K | 8K | 8K |
| Throughput | 45 tps | 40 tps | 38 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 620M
- Completion tokens generated (last 30 days)
- 36.8M
- API requests served (last 30 days)
- 99.8%
- Average API 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 optimal model across providers based on latency, cost, and quality—without changing your application code or wiring.
One API, all models -
Cost-Aware Execution
Enforce per-request and per-project budgets, compare provider pricing in real time, and automatically choose cheaper equivalents without sacrificing required quality.
Control spend by default -
Intelligent Fallbacks
Automatically fail over to backup models or regions on timeouts, rate limits, and provider outages so your AI features stay online and resilient.
No more broken calls -
Deep Observability
Get per-request traces, latency and error metrics, and model-level usage breakdowns across all providers from one dashboard and API.
See every token -
Task-Level Orchestration
Describe tasks, constraints, and tools once; let LLM.API orchestrate the right models, prompts, and steps for consistent, reusable workflows.
From prompts to tasks -
High-Throughput Batching
Submit large batches across models and providers with built-in concurrency control, retries, and aggregation to maximize throughput and minimize infrastructure overhead.
Ship at batch scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose assistant for coding help, writing, and everyday reasoning tasks.
- You need strong support for English plus decent performance on several other languages.
- Your use case involves building chat-style applications that need instruction-following and tool use.
- Your use case involves moderately complex data analysis or summarizing medium-length technical documents.
- You need a capable model from Qwen’s ecosystem, integrated with their tooling and SDKs.
Avoid if...
- You need cutting-edge state-of-the-art reasoning performance on the hardest benchmark-style problems.
- Your workload requires extremely long context handling, such as millions of tokens per request.
- You need strict, independently audited guarantees around safety, compliance, and data governance.
- You need ultra-low-latency real-time interactions for high-frequency trading or similar time-critical systems.
- Your workload requires specialized domain models, such as top-tier medical or legal reasoning.
FAQ
Frequently Asked Questions
-
What is Qwen3.5 Plus 2026-02-15?
Qwen3.5 Plus 2026-02-15 is a general-purpose large language model from Qwen focused on strong reasoning and coding capabilities.
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What is the context window of Qwen3.5 Plus 2026-02-15?
Qwen3.5 Plus 2026-02-15 supports up to a 32,000 token context window for combined input and output.
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What is Qwen3.5 Plus 2026-02-15 best suited for?
It is best suited for complex reasoning, multi-step coding tasks, data analysis assistance, and high-quality general chatbots.
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How is Qwen3.5 Plus 2026-02-15 priced on LLM.API?
LLM.API exposes Qwen3.5 Plus 2026-02-15 with per-token metered pricing; check the LLM.API pricing page for current input and output rates.
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How fast is Qwen3.5 Plus 2026-02-15 on LLM.API?
Typical responses stream within a few hundred milliseconds for small prompts, with longer prompts adding latency proportional to token length.
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What modalities does Qwen3.5 Plus 2026-02-15 support via LLM.API?
Through LLM.API, Qwen3.5 Plus 2026-02-15 currently supports text input and text output only.
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How do I call Qwen3.5 Plus 2026-02-15 through LLM.API?
Use the LLM.API chat or completions endpoint and set the model parameter to "Qwen3.5 Plus 2026-02-15" with your API key.
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How does Qwen3.5 Plus 2026-02-15 compare to other Qwen3.5 models?
Compared to lighter Qwen3.5 variants, Plus generally offers better reasoning quality and coding performance at higher cost and latency.
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What are the main limitations of Qwen3.5 Plus 2026-02-15?
It can hallucinate incorrect facts, lacks real-time internet access, and should not be used as the sole source for critical decisions.
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Can I use Qwen3.5 Plus 2026-02-15 for long documents or multi-turn conversations?
Yes, as long as the total tokens of conversation history and response remain within the 32,000 token context limit.
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