xAI's reasoning-first flagship with a 1M-token context, configurable reasoning effort, and leading agentic tool-calling on independent benchmarks.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
Where Grok 4.3 Earns Its Place: Agentic Tool Use
Benchmarks
Grok 4.3 on the Artificial Analysis Intelligence Index
Output Speed
Intelligence Index
MMLU
GPQA
HLE
LiveCodeBench
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "x-ai/grok-4.3",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer anyapi_api_key",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "x-ai/grok-4.3",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer anyapi_api_key",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer anyapi_api_key' \
--header 'Content-Type: application/json' \
--data '
{
"model": "x-ai/grok-4.3",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
'
Comparison
Grok 4.3 vs Grok 4.20: What Actually Changes?
Grok 4.3 is the direct architectural successor to Grok 4.20, matching its pretraining scale with an improved architecture and a December 2025 knowledge cutoff. Both target agentic and long-context workloads, but Grok 4.20 carries a larger 2M-token context window, while Grok 4.3 uses 1M. The realistic decision is between raw context capacity and updated behavior: Grok 4.3 delivers materially better agentic task performance and cost-per-intelligence, whereas Grok 4.20 leads on non-hallucination rate and offers more context headroom for very large document sets.
Choose Grok 4.3 when agentic tool use, instruction following, and cost-per-intelligence matter most — it posts a large GDPval-AA gain over 4.20 and lower cost to run the benchmark suite. Choose Grok 4.20 when you need the 2M-token context window for extremely large inputs, or when non-hallucination rate is the priority, since Grok 4.20 still leads Grok 4.3 on that metric. For most new agent builds, Grok 4.3 is the stronger default.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Multi-step agentic automation
Grok 4.3's strongest evidence is agentic: a GDPval-AA Elo of 1500 (up 321 points over Grok 4.20) and 98% on 𝜏²-Bench Telecom. Always-on, configurable reasoning helps it stay on-task across long tool-calling loops. This fits customer-support automation, workflow agents, and research agents that chain many sequential API calls, where dropped instructions or skipped reasoning steps break the loop.
Long-document and multi-document analysis
The 1M-token context window lets Grok 4.3 ingest extensive reports, policy manuals, research packets, or code fragments in a single request. Paired with medium or high reasoning effort, it supports deep synthesis across many sources. This suits legal, financial, and technical review pipelines — but note tiered billing above 200K tokens and confirm the model reasons accurately over selected material rather than being distracted by low-value content.
Enterprise accuracy-critical Q&A
Grok 4.3 ranks well in enterprise domains such as case-law and corporate-finance analysis, and independent testing shows strong instruction following (81% IFBench). It fits contract review, credit-agreement analysis, and financial document Q&A where structured, schema-constrained outputs matter. Because its non-hallucination rate trails Grok 4.20, pair it with retrieval grounding and verification for outputs that carry compliance or financial risk.
Tool-driven data extraction with structured outputs
With native function calling and JSON-schema structured outputs, Grok 4.3 can drive pipelines that must return machine-parseable results from unstructured input. Combined with image input, it handles document-plus-screenshot extraction tasks. Keep reasoning effort low for high-throughput extraction to control latency and token overhead, reserving medium or high for records requiring judgment.