Grok 4.3 Review: Benchmarks, Pricing & Verdict

"Grok 4.3" is xAI's reasoning model, released in late April 2026, with a 1,000,000-token context window and official list pricing of $1.25 / $2.50 per 1M input/output tokens. It is confirmed real via xAI's official developer docs, and DataLLM Lab serves it on our OpenAI-compatible gateway at the same list rate. This review is unusually hands-on: we ran Grok 4.3 through our own executed 9-task coding benchmark (it scored 8/9), and every other number below is either from xAI's own docs (official), from xAI's marketing (vendor-reported), or from an independent evaluator (Artificial Analysis, Vals AI) — and we label which is which. Where the vendor's claims and the independent data disagree, we say so rather than pick a side.

What Grok 4.3 is

Grok 4.3 is xAI's current reasoning model — a 1M-context, text-and-image model released in late April 2026, and one DataLLM Lab serves on its gateway. The essentials, straight from xAI's official developer docs:

• Officially named grok-4.3 and listed as a current model in xAI's developer documentation.
• 1,000,000-token context window — large enough to hold whole codebases, long document sets or extended agent transcripts in one request.
• Text and image inputs, text output. Note: xAI's official model page does not list video input. Secondary press mentions "native video," but we could not confirm that on a primary source, so we exclude it.
• Reasoning model. xAI's docs indicate reasoning is supported; secondary sources describe configurable reasoning levels (none/low/medium/high) that we could not verify on the official page.

On the release date: xAI's official docs do not state one. Independent trackers conflict — VentureBeat reports an April 17, 2026 early beta while Artificial Analysis lists April 30, 2026 — so "late April 2026" is the honest window. Everything in this review is dated as of July 2026.

One disclosure up front: we ran our own executed 9-task coding benchmark on Grok 4.3 — it scored 8/9 (details in the benchmarks section). Every other figure below is tagged official (xAI docs), vendor-reported (xAI marketing), or independent (a third-party evaluator).

Spec & positioning table

A single synthesized view of what Grok 4.3 is, where its numbers come from, and how confident we are in each. This is our own table, built from the sources cited throughout — not lifted from any one page:

"Video input" is intentionally absent: it is a press claim, not on xAI's official model page. Figures as of July 2026 and subject to change.

Pricing on xAI and the gateway

Grok 4.3 is a value-tier price for a 1M-context reasoning model — $1.25 in, $2.50 out per 1M tokens. xAI's official list pricing:

DataLLM Lab serves Grok 4.3 on our OpenAI-compatible API at approximately the same $1.25 / $2.50 rate, matching xAI's published list price. That $0.20 cached-input rate matters for agent and RAG workloads that resend the same system prompt or context repeatedly — the same lever we cover in the cheapest-LLM-API guide. For the current gateway number, the Grok 4.3 model page and pricing page are the source of truth.

Benchmarks: vendor vs independent

The independent data puts Grok 4.3 mid-pack, with one standout domain — which is a more useful signal than xAI's "tops the leaderboards" marketing. We separate vendor, independent, and our own first-party numbers deliberately:

Two honesty flags worth stating plainly:

• The "53" discrepancy. Some secondary press cited a 53 Intelligence Index figure. That does not match Artificial Analysis's own model page, which shows 38. We use the primary source's number.
• Vendor claims don't line up with independent data. xAI says Grok 4.3 is #1 in case law and corporate finance. Independent Vals AI shows it is strong in corporate finance (#3/121) but weak in legal research (#17/18) — so treat the "#1 case law" claim as marketing until an independent source confirms it.

Honest pros & cons

Bought on price and context, Grok 4.3 is compelling; bought on a single "best model" headline, it is oversold. Our synthesized read — now backed by our own executed coding test (8/9):

Net: a well-priced, huge-context reasoning model with a genuinely strong corporate-finance showing and a solid 8/9 on our own executed coding benchmark — but not a universal frontier leader, and its marketing runs ahead of the independent evidence.

Grok 4.3 vs rivals

Grok 4.3's edge is price-per-context; its rivals win on either raw intelligence or specific-domain depth. How to place it:

• Vs frontier flagships (Claude Opus, GPT-class). Grok 4.3 is far cheaper — $1.25/$2.50 against Claude Opus 4.8 at $5/$25 — and matches the largest 1M context windows, but sits below the top of independent intelligence indexes. If you want the flagship-tier comparison framework, see Claude vs ChatGPT.
• Vs coding specialists. xAI markets strong agentic tool calling, but that is vendor-reported. For coding specifically, weigh it against the dedicated leaders in our best coding LLM guide rather than trusting the leaderboard headline.
• Vs open-weight options. If cost floor and self-hosting matter more than a hosted API, compare against the field in best open-source LLMs.
• Vs other gateway models. On DataLLM Lab you can A/B Grok 4.3 against GLM 5.2, Mistral Medium 3.5 and others on one key — see the GLM 5.2 review or the Mistral Medium 3.5 review for those write-ups.

Who should use it

Reach for Grok 4.3 when you want a cheap, million-token reasoning model and can validate quality on your own task. Good fits and poor fits:

• Good fit: long-context work (large codebases, document sets, agent transcripts) where the 1M window and low price compound; corporate-finance analysis, where independent data actually backs it; cost-sensitive agent loops that benefit from $0.20 cached input.
• Poor fit: legal-research or tax workloads, where Vals AI ranks it near the bottom; any use where you need a top-of-index frontier score more than price; anything relying on unconfirmed "native video."
• Always: run your own eval. Our executed coding test (8/9) is one data point, not a verdict on your workload; neither it, the vendor headline, nor a single index should decide it for you. Route it through a gateway so you can compare it side-by-side against alternatives before committing — see best LLMs for AI agents for how to structure that.

Call Grok 4.3 on the gateway

Grok 4.3 is live on DataLLM Lab's OpenAI-compatible endpoint — same key, same SDK, just swap the model string. Point any OpenAI-compatible client at our base URL:

from openai import OpenAI

client = OpenAI(
    base_url="https://www.datallmlab.com/v1",
    api_key="YOUR_DATALLMLAB_KEY",
)

resp = client.chat.completions.create(
    model="x-ai/grok-4.3",
    messages=[{"role": "user", "content": "Summarize this 10-K in 5 bullets."}],
)
print(resp.choices[0].message.content)

If a call fails, the error-code reference maps each status to a fix, and routing & failover shows how to reroute to an equivalent model automatically. New to gateways? Start with what an LLM gateway is.

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