- Instruction Following
Qwen3.5-Flash is a hosted, production-oriented large language model from Qwen, optimized for fast, efficient text and vision-language generation. It corresponds to the Qwen3.5-35B-A3B model and offers…
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GPT-5.1 Chat
- Instruction Following
GPT-5.1 Chat is an OpenAI conversational AI model designed for high-quality dialogue, reasoning, and assistance across many domains. It is notable for improved reliability, instruction-following, and versatility compared to earlier GPT models.
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 GPT-5.1 Chat?
GPT-5.1 Chat is an OpenAI language model optimized for interactive, multi-turn conversation. It is typically used for tasks such as answering questions, drafting and editing text, and providing coding or analytical help. It is also applied in building chatbots, virtual assistants, and productivity tools that require natural language understanding and generation. GPT-5.1 Chat follows earlier GPT-series models from OpenAI, improving on their capabilities while remaining part of the same generative transformer family.
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 across extended interactions.
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Image Understanding
Interprets images, describing content, layout, and relationships between visual elements to support reasoning and question answering.
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Visual Text OCR
Extracts readable text from images, screenshots, and documents, enabling downstream search, analysis, and transformation of visual content.
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Multilingual Translation
Translates between many languages while preserving meaning, tone, and style, suitable for both casual and formal content.
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Tool Integration
Coordinates with external tools and systems, interpreting outputs to help with monitoring, analysis, and automation workflows.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice And Receipt Parsing
- Legal Case Law Search
- Compliance Case Monitoring
- E-commerce Product Assistance
- Code Generation And Review
Transparent pricing
Cost Comparison
LLM API offers the lowest GPT-5.1 Chat-equivalent prices with the largest context window.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.40 | $1.60 | 1M tokens |
| OpenAI | Global | ~220ms | ~80 tps | 99.9% | ~$0.60 | ~$2.40 | 128K tokens |
| Azure OpenAI | US East | ~240ms | ~70 tps | 99.9% | ~$0.65 | ~$2.60 | 128K tokens |
| Anthropic (Claude Sonnet-equivalent) | US West | ~260ms | ~60 tps | 99.9% | ~$0.70 | ~$2.80 | 200K tokens |
| Google (Gemini 1.5 Pro-equivalent) | Global | ~250ms | ~65 tps | 99.9% | ~$0.55 | ~$2.20 | 1M tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.1 Chat (OpenAI) | Claude 3.7 Sonnet (Anthropic) | Gemini 2.0 Pro (Google) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~230ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M tokens) | $0.80 | $1.00 | $0.90 |
| Output Price ($/1M tokens) | $2.40 | $3.00 | $2.70 |
| Max Output Tokens | 8K | 8K | 8K |
| Throughput | ~70 tps | ~55 tps | ~50 tps |
| Uptime | 99.9% | 99.5% | 99.5% |
30-day usage via LLM API
- 980B
- Prompt tokens processed (last 30 days)
- 210M
- API requests served (last 30 days)
- 1.4T
- Completion tokens generated (last 30 days)
- 3.1M
- Unique developer accounts (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-fitting model across providers, based on latency, cost, or quality—without changing your integration.
One endpoint, every model. -
Cost-Aware Orchestration
Optimize spend automatically by mixing premium and budget models, enforcing per-request and per-project cost controls directly in your AI gateway.
Ship fast, spend less. -
Resilient Fallback Flows
Design multi-model fallback chains so failed or degraded providers are retried on alternates, keeping production apps stable under real-world outages.
No single point of failure. -
Full-Stack Observability
Trace every call across providers with metrics, logs, and structured events so you can debug prompts, track usage, and tune performance in one place.
See every token, everywhere. -
Task-Level Abstractions
Define reusable tasks—like summarize, classify, or extract—that map to different models and prompts, decoupling your app logic from provider details.
Code tasks, not providers. -
High-Throughput Batch Jobs
Run massive batch inference jobs with automatic chunking, concurrency control, and retries, turning one API call into millions of safely processed items.
Scale from one to millions.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose assistant for chat, coding, analysis, and content drafting.
- You need strong reasoning for debugging, refactoring, and explaining complex software systems.
- You need high-quality, context-aware writing for emails, reports, or product documentation.
- Your use case involves multi-step data analysis, planning, and summarizing long technical materials.
- Your use case involves building a conversational agent that must follow nuanced instructions reliably.
- You need a model that balances quality, latency, and cost without heavy fine-tuning.
- Your use case involves generating or reviewing code across multiple languages and frameworks.
Avoid if...
- You need strict on-device inference with no external API calls or connectivity.
- Your workload requires guaranteed fixed-cost inference where per-token API pricing is unacceptable.
- You need domain-specific performance that only a heavily fine-tuned proprietary model can provide.
- Your workload requires ultra-low latency responses for high-frequency trading or hard real-time control.
- You need processing of extremely sensitive data that must never leave a closed environment.
- Your workload requires deterministic, bit-for-bit reproducible outputs across runs and environments.
- You need specialized multimodal capabilities beyond text and images, like real-time audio or video.
FAQ
Frequently Asked Questions
-
What is GPT-5.1 Chat?
GPT-5.1 Chat is a general-purpose conversational large language model by OpenAI, accessible through the unified LLM.API gateway.
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What is GPT-5.1 Chat best suited for?
GPT-5.1 Chat is best for multi-turn assistants, complex reasoning, code generation, and knowledge work requiring reliable instruction-following.
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What modalities does GPT-5.1 Chat support via LLM.API?
GPT-5.1 Chat supports text input and output, with optional image input when enabled by your LLM.API configuration.
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What is the context window of GPT-5.1 Chat?
GPT-5.1 Chat supports long-context interactions; check your LLM.API plan for the exact maximum token window available.
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How does GPT-5.1 Chat pricing work on LLM.API?
LLM.API bills GPT-5.1 Chat usage per token for input and output, with rates defined in your LLM.API pricing page.
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How fast is GPT-5.1 Chat in terms of latency?
GPT-5.1 Chat generally responds in seconds, with latency depending on prompt size, response length, and your LLM.API region.
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How do I call GPT-5.1 Chat through LLM.API?
Specify the model name "gpt-5.1-chat" in your LLM.API request and send standard chat-style messages with role and content fields.
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How does GPT-5.1 Chat compare to other OpenAI chat models?
GPT-5.1 Chat typically offers stronger reasoning, better instruction-following, and improved safety compared to earlier GPT-4-class chat models.
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Does GPT-5.1 Chat have any important limitations?
GPT-5.1 Chat can still hallucinate, reflect training data biases, and should not be solely relied on for high-stakes decisions without human review.
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Can I fine-tune GPT-5.1 Chat through LLM.API?
Fine-tuning availability for GPT-5.1 Chat depends on LLM.API support; if unavailable, you can still perform lightweight prompt-based adaptation.
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