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xAI: Grok 4.3
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xAI: Grok 4.3

Speed at Frontier Intelligence — a Rare Combination

Context window: 
1
M tokens
Output: 
1000000
 tokens
Modality:
Text
Image
Video
PDF
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The Fastest Frontier Reasoner in Production

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Most reasoning models pay a latency tax. Longer internal thinking chains produce better answers but slower responses, which creates a real friction point for any product that needs real-time AI applications: agents that loop over multiple steps, customer-facing interfaces, and pipelines where time-to-output affects user experience directly.

Grok 4.3 breaks this pattern in measured benchmarks. At 209 output tokens per second, it ranks first of 154 models evaluated by Artificial Analysis, while simultaneously placing tenth on their Intelligence Index - a composite of ten evaluations covering agentic task performance, instruction following, factual accuracy, scientific reasoning, and coding. Ranking in the top 7% for speed and top 7% for intelligence on the same model is not typical at this price tier.

The largest single benchmark gain versus its predecessor (Grok 4.20) came on GDPval-AA, a real-world agentic task evaluation where Grok 4.3 scored an ELO of 1500 - 321 points higher than Grok 4.20's 1179. This is the most significant agentic performance jump observed in the Grok family across any single release. For developers building multi-step agent pipelines, that improvement translates directly to task completion rates, not abstract benchmark differences.

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On cost efficiency: Grok 4.3 cost $395 to run the full Artificial Analysis Intelligence Index benchmark suite — approximately 20% less than Grok 4.20, despite generating more output tokens. This positions it on the Pareto frontier for intelligence versus cost among proprietary reasoning models.

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Where Grok 4.3 Falls Short

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Always-on reasoning is not always the right tool

Grok 4.3's reasoning cannot be disabled. For latency-sensitive tasks that don't benefit from extended thinking - simple retrieval, structured data extraction, short classification tasks - the forced reasoning overhead adds cost and time without quality benefit. Developers who need a fast non-reasoning path should consider Grok 4.1 Fast (non-reasoning variant) or route simple queries to a lower-tier model. Grok 4.3 is not the right hammer for every nail in a mixed-complexity pipeline.

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No persistent memory between sessions

One limitation that stands out given the model's price point: Grok 4.3 has no persistent memory across sessions. Users and applications must explicitly manage conversation state and context injection. Competitors including Claude and ChatGPT have offered session memory for over a year. For products where continuity of context matters  ongoing research assistants, long-running customer relationships, personalized tutoring - this requires an additional memory layer in the application stack.

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Hallucination rate regression versus Grok 4.20

On the AA-Omniscience Non-Hallucination Rate benchmark, Grok 4.3 scores 8 points lower than Grok 4.20. This is a meaningful difference for applications where factual precision in open-domain knowledge queries is critical. The accuracy gains (Grok 4.3 scores 8 points higher on AA-Omniscience Accuracy) offset this partially, but the trade-off exists and should factor into deployment decisions for high-stakes knowledge retrieval.

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Verbosity at scale

Grok 4.3 is significantly more verbose than comparable models: it generated 88 million output tokens running the Artificial Analysis Intelligence Index, against an average of 35 million for comparable models. In benchmarks, verbosity correlates with thoroughness. In production, it means higher output token costs if responses are not constrained by prompt engineering. Teams running high-volume workloads should account for this when estimating costs.

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Where This Model Earns Its Place

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Agentic pipelines requiring speed and accuracy together

The GDPval-AA performance gain makes Grok 4.3 the strongest xAI option for multi-step agentic workflows. Tool-calling loops, research agents, and autonomous task execution benefit from the combination of fast output generation and improved task completion accuracy. The always-on reasoning also reduces the need for prompt engineering to trigger thoughtful responses - the model defaults to structured analysis.

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Long-document analysis within 1M tokens

A 1 million token context window covers the vast majority of real-world document processing workloads: legal contract review, research paper synthesis, large codebase analysis, financial report processing. For documents or sessions that stay within this range, Grok 4.3 handles long-context coherence well, and the $2.50/M output pricing makes it economical for high-volume document throughput.

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Instruction-following and structured output

Grok 4.3 maintains an 81% IFBench score and reaches 98% on τ²-Bench Telecom, a benchmark focused on customer support agentic tasks. For LLM integration into workflows that require precise adherence to output schemas, extraction templates, or multi-step instructions, the instruction-following performance is among the strongest at this price tier.

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Scientific and technical reasoning

The model scores competitively on SciCode, GPQA Diamond, and Humanity's Last Exam within the Intelligence Index. Developer teams building research-adjacent tools - scientific literature synthesis, technical troubleshooting systems, engineering analysis - have a well-performing option here without paying o3-level pricing.

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Sample code for 

xAI: Grok 4.3

Python
JavaScript
Curl
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)
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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)
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Code is copied
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"
    }
  ]
}
'
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" } ] } '
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Frequently
Asked
Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

reasoning_effort controls how much deliberative processing Grok 4.3 applies before generating output. It accepts "none", "low" (the default), "medium", and "high". Higher settings increase reasoning depth and latency. Reasoning tokens are billed as output tokens but are excluded from the max_completion_tokens cap, so visible response length can be set independently.

Requests with fewer than 200k prompt tokens are billed at 7.5 anytokens output. Once a single request's prompt crosses 200,000 tokens, xAI reprices the entire request at 15 anytokens output — the higher rate applies to all tokens, not just the overflow. Budget calculations for long-document workflows need to account for this threshold.

Grok 4.5 scores substantially higher on independent benchmarks — 56 versus 38 on the Artificial Analysis Intelligence Index, ranking #21 versus #75 of 186 models. For tasks requiring difficult reasoning, coding, or scientific analysis, Grok 4.5 likely delivers better results. Grok 4.3's advantage is its 1M context window (2× larger) and significantly lower cost per token.

Yes. Per official xAI documentation, Grok 4.3 accepts image input (JPG/JPEG/PNG, up to 20 MiB per image, no stated image count limit) alongside text via the /v1/chat/completions endpoint. Output is text only — Grok 4.3 does not generate images.

Not ideal. Independent benchmarks (Artificial Analysis) measure time to first token at approximately 17.95 seconds — roughly 6× the peer median. This latency makes Grok 4.3 a poor fit for interactive use cases where users expect immediate response. It is better suited to background processing, batch pipelines, and asynchronous document workflows.