- Instruction Following
GLM 4.7 Flash is a 30B-class Mixture-of-Experts language model from Z.ai, optimized for speed and efficiency while maintaining strong performance on coding and agentic reasoning tasks.
Powered by OpenAI
GPT-5.1
- Instruction Following
GPT-5.1 is an OpenAI language model; as of mid-2026, OpenAI has not publicly released technical details or documentation about it.
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?
GPT-5.1 is an OpenAI model name for which no official public specification, capabilities overview, or documentation has been released as of mid-2026. Because of this, there is no reliable, verifiable information about its intended primary use cases beyond general large-language-model tasks like text generation, coding assistance, and reasoning that OpenAI models typically target. Any more specific claims about its performance, architecture, or domain specialization would be speculative and are not supported by public sources. It is presumably related in name to the GPT model family that includes earlier generations such as GPT-3.5, GPT-4, and GPT-4.1, but its exact position or role in that family has not been formally described.
Model capabilities
5 Core Capabilities
-
Advanced Chat
Engages in multi-turn conversations, following complex instructions and maintaining context across long interactions for varied assistant-style tasks.
-
Image Understanding
Interprets and reasons about images, supporting tasks like description, comparison, and extraction of visual details from user-provided pictures.
-
Text Translation
Translates between many languages while preserving meaning and tone, supporting instructions to constrain or adapt style as needed.
-
Document OCR
Extracts text and structure from images or scans of documents, enabling downstream search, summarization, and analysis workflows.
-
Usage Monitoring
Supports integration into applications where developers can observe, evaluate, and iterate on prompts and outputs for quality control.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice And Receipt Extraction
- Legal Case Research
- Regulatory Case Monitoring
- E-commerce Product Recommendations
- Code Generation And Review
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for GPT-5.1–class models, up to ~40–60% cheaper than major providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.60 | $2.40 | 256K |
| OpenAI | Global | ~220ms | ~40 tps | 99.9% | ~$1.00 | ~$4.00 | ~256K |
| Azure OpenAI | US East | ~250ms | ~35 tps | 99.9% | ~$1.10 | ~$4.40 | ~256K |
| Google Cloud (Gemini Ultra-equivalent) | US Central | ~260ms | ~30 tps | 99.9% | ~$1.20 | ~$4.80 | ~256K |
| Anthropic (Claude 3.5-equivalent) | US West | ~240ms | ~32 tps | 99.9% | ~$1.30 | ~$5.20 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.1 (OpenAI) | Claude 3.7 (Anthropic) | Gemini 2.0 Pro (Google) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~240ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M) | $2.50 | $3.00 | $2.20 |
| Output Price ($/1M) | $7.50 | $15.00 | $7.00 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 120 tps | 90 tps | 100 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 3.8T
- Prompt tokens processed (last 30 days)
- 2.1T
- Completion tokens generated (last 30 days)
- 640M
- API requests served (last 30 days)
- 99.97%
- 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.
-
Intelligent Model Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Orchestration
Enforce budgets, cap spend per app or tenant, and downshift to cheaper models automatically—so you control cost without manually tuning every call.
Predictable AI spend -
Resilient Fallback Flows
Define fallback chains so if a model, region, or provider fails, calls transparently fail over without breaking your application or SLAs.
No single point of failure -
End-to-End Observability
Get unified logs, metrics, traces, and per-provider analytics so you can debug issues, tune routing, and track performance from a single pane.
See every token, everywhere -
Task-Level Abstractions
Use high-level task APIs—chat, generation, tools, embeddings—instead of provider-specific formats, so you can swap models without rewriting business logic.
Code to tasks, not vendors -
High-Throughput Batch Jobs
Run large-scale batch inference across models and providers with automatic sharding, retries, and progress tracking to keep pipelines fast and reliable.
Scale inference on autopilot
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose model that balances strong reasoning, coding, and language skills.
- You need high-quality natural language understanding and generation for chatbots or virtual assistants.
- Your use case involves building chat assistants that must handle diverse, unpredictable queries.
- You need high-quality code generation, refactoring, and debugging across multiple programming languages.
- Your use case involves complex natural language understanding, such as contract or policy review.
- You need a single model that performs well across text, tools, and structured outputs.
Avoid if...
- You need the absolute lowest inference cost and can accept noticeably weaker model quality.
- Your workload requires ultra-low latency responses for tight real-time or on-device interactions.
- You need guaranteed offline or fully self-hosted deployment without relying on cloud services.
- Your workload requires strict, custom fine-tuning beyond what OpenAI’s tooling currently supports.
- You need a model optimized solely for simple classification where smaller models suffice.
- Your workload requires full transparency into weights and training data, including complete open weights.
FAQ
Frequently Asked Questions
-
What is GPT-5.1?
GPT-5.1 is a frontier OpenAI model accessible via LLM.API, optimized for high-quality reasoning, coding, and multimodal interactions.
-
What modalities does GPT-5.1 support through LLM.API?
GPT-5.1 supports text input and output via LLM.API; check the LLM.API docs for current support of image, audio, or other modalities.
-
How is GPT-5.1 priced when used via LLM.API?
GPT-5.1 pricing is usage-based per input and output token, with exact rates defined in the LLM.API pricing documentation.
-
What is the context window of GPT-5.1?
GPT-5.1 supports a large token context window suitable for long conversations and documents; consult LLM.API docs for the current token limit.
-
How fast is GPT-5.1 in terms of latency?
GPT-5.1 typically returns first tokens within a few seconds, with total latency depending on prompt size, response length, and LLM.API routing.
-
What is GPT-5.1 best suited for?
GPT-5.1 is best for complex reasoning, advanced coding assistance, multi-step tool use, and high-quality natural language generation across domains.
-
How do I call GPT-5.1 through LLM.API?
Specify the model name "GPT-5.1" in your LLM.API request payload and authenticate with your LLM.API key as described in the API docs.
-
How does GPT-5.1 compare to earlier OpenAI models like GPT-4.1?
GPT-5.1 generally improves on reasoning depth, coding reliability, and instruction following compared with GPT-4.1, while remaining API compatible via LLM.API.
-
What are the main limitations of GPT-5.1?
GPT-5.1 can still hallucinate facts, misunderstand ambiguous instructions, and lacks real-time access to proprietary or constantly changing external data by default.
-
Can I fine-tune or customize GPT-5.1 via LLM.API?
Fine-tuning or configuration options for GPT-5.1 depend on LLM.API’s current feature set; check the fine-tuning section of the documentation.
Compare
Competitive Models
- Instruction Following
Gemma 4 26B A4B (free) is a 26-billion-parameter variant in Google’s Gemma 4 family, offered with an A4B quantization profile for more efficient inference. It is…
- Instruction Following
GLM 5 is Z.ai’s fifth-generation large language model, a large open-source Mixture-of-Experts foundation model focused on advanced reasoning and long-horizon agent workflows. It is notable for…