- Instruction Following
GPT-5.2 Pro is an OpenAI frontier large language model optimized for strong general reasoning, coding, and multimodal assistant use in demanding, real-world applications.
Powered by xAI
Grok 4.3
- Instruction Following
Grok 4.3 is a large language model from xAI designed to provide fast, conversational reasoning and question-answering, particularly around real‑time and technical topics. It is part of xAI’s Grok series focused on practical, web‑aware AI assistants.
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 Grok 4.3?
Grok 4.3 is an xAI large language model optimized for conversational assistance and reasoning. It is used for answering questions, drafting and refining text, and providing general-purpose coding and technical help. It is also applied to data interpretation and explanations across domains such as science, engineering, and everyday problem-solving. Grok 4.3 continues the Grok model line from xAI, following earlier Grok versions in the same family.
Model capabilities
5 Core Capabilities
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Advanced Text Chat
Provides general-purpose conversational assistance with strong reasoning, instruction following, and minimal hallucinations for complex multi-step queries.
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Image Understanding
Accepts image inputs alongside text, analyzing visual content to answer questions and describe scenes within multimodal prompts.
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Document Handling
Processes large text contexts up to one million tokens, enabling work with long documents, reports, and multi-document workflows.
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Structured Function Calling
Supports structured outputs and function calling to connect with external tools and return data in well-defined formats.
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Multilingual Responses
Handles prompts and generates responses in multiple languages, suitable for global users interacting across diverse linguistic contexts.
Use cases
6 Most Valuable Use Cases
- Code Generation Support
- Business Data Analysis
- Customer Support Automation
- Legal Case Research
- Regulatory Change Monitoring
- Financial Document Review
Transparent pricing
Cost Comparison
Save up to ~70% vs major Grok-class APIs with LLM API’s optimized pricing.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.40 | $0.80 | 256K |
| xAI | US West | ~220ms | ~60 tps | ~99.9% | ~$0.90 | ~$1.80 | ~128K |
| Groq | US East | ~180ms | ~80 tps | ~99.9% | ~$0.70 | ~$1.40 | ~128K |
| Fireworks.ai | Global | ~210ms | ~55 tps | ~99.9% | ~$0.80 | ~$1.60 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | Grok 4.3 (xAI) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~230ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $2.00 | $5.00 | $3.00 |
| Output Price ($/1M) | $5.00 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | ≥100 tps | ≥60 tps | ≥50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (last 30 days)
- 9.5B
- Completion tokens generated (last 30 days)
- 21M
- 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
Intelligently route each request to the optimal model across providers based on latency, cost, and quality. Ship faster without hard‑coding provider logic.
One endpoint, any model -
Cost-Aware Orchestration
Define budgets and price caps, then let LLM.API choose the most economical model per call. Avoid bill shocks while still meeting quality SLAs.
Control spend by design -
Resilient Fallback Flows
Automatically fail over to backup models or providers on timeouts, rate limits, and errors. Keep your AI features up, even when vendors are not.
Fail soft, not hard -
End-to-End Observability
Get full visibility into prompts, latencies, costs, and provider performance in one place. Debug faster and tune routing with real production data.
See every token spent -
Task-Level Abstractions
Describe intent like ‘summarize’, ‘extract’, or ‘classify’ and let LLM.API pick the right model and settings. Standardize behaviors across vendors and versions.
Code to intent, not models -
High-Throughput Batch Jobs
Run massive prompt batches through any provider with automatic chunking, retries, and aggregation. Maximize throughput while staying within rate and cost limits.
Ship bulk AI workloads
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a frontier general-purpose model from xAI with strong reasoning capabilities.
- You need tight integration with the xAI ecosystem and Grok-specific tools or APIs.
- Your use case involves chat-style assistants that must handle open-domain questions reliably.
- Your use case involves exploratory coding help, debugging, and code explanation across languages.
- You need a model optimized for conversational search, summarization, and explanation over web content.
- Your use case involves creative writing, ideation, and content drafting with human-like style.
- You need an xAI-hosted model where data residency and vendor consolidation favor the xAI stack.
Avoid if...
- You need strict, battle-tested enterprise compliance certifications beyond what xAI currently advertises.
- Your workload requires highly specialized industry-tuned models, like dedicated medical or legal experts.
- You need fully on-premise deployment with air-gapped infrastructure and no external API dependence.
- Your workload requires guaranteed compatibility with existing OpenAI-specific extensions or tool formats.
- You need ultra-low-latency edge inference on small devices rather than hosted cloud models.
- Your workload requires mature third-party ecosystem integrations that only long-established providers currently offer.
- You need extensive region-specific data centers and SLAs across many regulated global jurisdictions.
FAQ
Frequently Asked Questions
-
What is Grok 4.3?
Grok 4.3 is an advanced large language model from xAI accessible through LLM.API for code, reasoning, and general assistant use cases.
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What is Grok 4.3 best suited for?
Grok 4.3 is best for complex reasoning, multi-step code tasks, and chat-style assistants that require high-quality, steerable responses.
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How is Grok 4.3 priced on LLM.API?
Grok 4.3 pricing on LLM.API is usage-based per input and output token; check your LLM.API dashboard or pricing docs for current rates.
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What is the context window of Grok 4.3?
Grok 4.3 supports a large-context window suitable for long conversations and multi-file code tasks; see LLM.API documentation for the latest token limit.
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How fast is Grok 4.3 in terms of latency?
Grok 4.3 typically returns first tokens in a few seconds, with total latency depending on prompt size, output length, and LLM.API routing.
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What modalities does Grok 4.3 support via LLM.API?
Through LLM.API, Grok 4.3 currently supports text input and text output; check docs for any enabled image or other modality extensions.
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How do I call Grok 4.3 using LLM.API?
Specify the model name "grok-4.3" (or the exact identifier in docs) in your LLM.API completion or chat endpoint request.
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How does Grok 4.3 compare to similar frontier models?
Grok 4.3 targets competitive quality and reasoning versus other flagship models while being accessible through a unified LLM.API interface.
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What are the main limitations of Grok 4.3?
Grok 4.3 can hallucinate, may contain outdated knowledge, and should not be used without human review for safety-critical or compliance-sensitive decisions.
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Can I fine-tune or customize Grok 4.3 through LLM.API?
Fine-tuning support depends on LLM.API’s capabilities for xAI models; consult the customization section of the platform documentation.
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