- Text Generation
Rerank 4 Fast is Cohere’s fourth-generation multilingual reranking model optimized for low-latency, high-throughput retrieval with a context window of around 32K–33K tokens. It is designed to…
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Qwen3 Max Thinking
- Text Generation
Qwen3 Max Thinking is a large language model from Qwen optimized for extended, step-by-step reasoning. It is designed to handle complex analytical tasks while maintaining strong general-purpose chat and coding capabilities.
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 Max Thinking?
Qwen3 Max Thinking is a reasoning-focused large language model developed by Qwen. It is mainly used for tasks that benefit from long, deliberate chains of thought, such as complex problem solving, code generation and review, and multi-step data or text analysis. It is also applied to research assistance, planning, and other scenarios where transparent intermediate reasoning is valuable. It belongs to the Qwen3 model family, an evolution of earlier Qwen series models from the same provider.
Model capabilities
5 Core Capabilities
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Deep Reasoning
Excels at complex multi-step reasoning for math, coding, and science tasks using extended internal thinking traces before answering.
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Advanced Chat
Handles multi-turn conversations, follows nuanced instructions, and maintains context over long dialogues for assistant-style interactions.
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Multimodal Generation
Generates rich text responses and can produce images or video content based on user prompts via the Qwen3-Max family.
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Multilingual Support
Understands and generates content in many languages, enabling cross-lingual question answering and content creation scenarios.
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Text Extraction
Processes and extracts structured information from documents or screenshots, supporting search, analysis, and downstream workflows.
Use cases
6 Most Valuable Use Cases
- Complex Code Generation
- Stepwise Reasoning Assistant
- Legal Case Analysis
- Regulation Change Monitoring
- Financial Report Summaries
- Business Strategy Ideation
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Qwen3 Max Thinking–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.20 | $0.60 | 256K |
| Qwen | Global | ~220ms | ~70 tps | ~99.9% | ~$0.40 | ~$1.20 | ~128K |
| Alibaba Cloud | APAC | ~260ms | ~60 tps | ~99.9% | ~$0.45 | ~$1.30 | ~128K |
| OpenAI | Global | ~180ms | ~80 tps | ~99.9% | ~$0.50 | ~$1.50 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3 Max Thinking | GPT-4.1 Thinking | Claude 3.7 Sonnet Thinking |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~240ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $2.00 | $5.00 | $3.00 |
| Output Price ($/1M) | $6.00 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 8K | 8K |
| Throughput | 40 tps | 35 tps | 30 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 22.5B
- Prompt tokens processed (30 days)
- 9.8M
- API requests served (30 days)
- 18.9B
- Completion tokens generated (30 days)
- 99.96%
- Avg uptime over 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
Automatically route each request to the optimal model across providers based on quality, latency, and cost, without changing your integration or redeploying code.
One endpoint, any model -
Cost-Aware Execution
Control spend with per-call pricing visibility, smart model selection, and guardrails that keep your workloads on budget while preserving response quality.
Max performance, minimal spend -
Resilient Fallback Logic
Define provider and model failover chains so requests transparently retry on alternate backends, eliminating single-provider outages and improving reliability SLAs.
Never ship a 500 -
Full-Stack Observability
Trace every call across models, providers, and regions with unified logs, metrics, and latency breakdowns, so you can debug issues and tune performance quickly.
See every token -
Task-Aware Orchestration
Describe tasks at a high level and let the platform pick the right tools, models, and prompts, standardizing patterns like RAG, agents, and workflows.
Tasks, not plumbing -
High-Throughput Batch
Run large-scale inference jobs with parallelized batching, retry semantics, and progress tracking, dramatically reducing wall-clock time for bulk workloads.
Millions of calls, one job
Decision guide
When to Use — When NOT to Use
Use it if...
- You need strong multi-step reasoning with deliberate thinking traces for complex problem-solving tasks.
- You need higher accuracy on math, coding, or logic puzzles than typical fast chat models.
- Your use case involves agents or tools that benefit from explicit chain-of-thought planning.
- You need an open-weight or ecosystem-friendly model compatible with Qwen-style reasoning workflows.
- Your use case involves low-concurrency, high-stakes queries where correctness outweighs raw latency.
- You need to debug or audit model decisions using transparent intermediate reasoning steps.
- Your use case involves generating or critiquing algorithms, proofs, or step-by-step technical derivations.
Avoid if...
- You need ultra-low latency responses for interactive chatbots or high-frequency user interfaces.
- Your workload requires serving millions of short requests where throughput cost dominates accuracy needs.
- You need strict suppression of chain-of-thought outputs for sensitive or regulated applications.
- Your workload requires lightweight on-device inference on very constrained edge or mobile hardware.
- You need deterministic, verifiable outputs for safety-critical domains beyond what LLMs generally ensure.
- Your workload requires multi-modal capabilities like image understanding that this text-focused model lacks.
- You need fully proprietary, enterprise-certified support from major US cloud providers only.
FAQ
Frequently Asked Questions
-
What is Qwen3 Max Thinking?
Qwen3 Max Thinking is a large language model by Qwen focused on high-quality reasoning and complex problem-solving via the LLM.API gateway.
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What is Qwen3 Max Thinking best suited for?
It is best for multi-step reasoning, code generation, data analysis explanations, and complex instruction-following where deliberate thought and intermediate reasoning are valuable.
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How is Qwen3 Max Thinking priced on LLM.API?
LLM.API charges per-token for input and output; check the Qwen3 Max Thinking pricing table in LLM.API for current rates.
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What context window does Qwen3 Max Thinking support?
Qwen3 Max Thinking supports a large context window suitable for long conversations and multi-file prompts; check LLM.API docs for the exact current token limit.
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How fast is Qwen3 Max Thinking in terms of latency?
Latency depends on load and token lengths, but as a deliberate reasoning model it is typically slower than lighter chat-optimized models.
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What modalities does Qwen3 Max Thinking support via LLM.API?
Through LLM.API, Qwen3 Max Thinking currently supports text input and text output; check the docs to confirm any image or other modality support.
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How do I call Qwen3 Max Thinking through LLM.API?
Use the LLM.API chat or completion endpoint with the model identifier for Qwen3 Max Thinking, passing your prompt and usual configuration parameters.
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How does Qwen3 Max Thinking compare to similar reasoning-focused models?
Compared to general chat models, it emphasizes deeper chain-of-thought reasoning, often trading higher latency and cost for stronger performance on complex tasks.
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What are the main limitations of Qwen3 Max Thinking?
It can hallucinate, may produce incorrect or outdated information, and is slower and potentially more expensive than smaller or non-thinking models.
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Can I fine-tune Qwen3 Max Thinking via LLM.API?
Direct fine-tuning is not guaranteed; LLM.API typically supports prompt-engineering and system prompts instead, so check docs for any available tuning options.
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