Grok 4 vs Grok 3 in 2026: Benchmarks, Context Window, Pricing, and What Replaced Grok 3

TL;DR: Grok 3 vs Grok 4 vs Grok 4.1 Fast vs Grok 4.3 at a glance

Verdict for new builds in 2026: start with Grok 4.1 Fast for cost-sensitive agents that need a 2M window or tool calling, and reach for Grok 4.3 when reasoning depth matters more than per-token cost. Keep Grok 3 only if you have a frozen production contract pinned to it.

What Grok 3 Originally Claimed and Which Claims Held Up

Grok 3’s February 2025 launch leaned on five claims. Here is how each one looks in 2026:

The original Grok 3 architecture and benchmarks are documented at x.ai/news/grok-3. The 1M-token context, Think Mode, and DeepSearch features are still present in the Grok 3 API for customers on legacy contracts.

Grok 4 Benchmarks vs Grok 3, GPT-5, Claude, and Gemini

Grok 4’s reported benchmark results at launch (July 9 2025), per Artificial Analysis and xAI:

Source: artificialanalysis.ai/models/grok-4, llm-stats.com/models/grok-4, and the original x.ai/news/grok-3 page for the Grok 3 baseline.

Important caveats. AIME 100% is reported with code-execution tools enabled. Public benchmark leaderboards shift week to week, so the only reliable comparison for your workload is a side-by-side replay in your own eval harness. We cover how to set that up in the FAGI evaluation section below.

Grok 4.1 Fast: 2M Context, $0.20 per Million Tokens, Best Tool-Calling

Grok 4.1 Fast (November 17 2025) is the model most production teams should consider first in 2026. Three reasons:

1. 2M token context window. Largest production context window from any frontier vendor as of May 2026. Lets you put a full codebase, a 5,000-page legal corpus, or a multi-day agent trajectory into a single prompt without aggressive chunking.
2. $0.20 input / $0.50 output per 1M tokens. Roughly 10x cheaper than GPT-5 reasoning and 5x cheaper than Claude Opus 4.x on like-for-like reasoning tasks.
3. Tool-call pricing capped at $5 per 1,000 successful calls. This is unique to xAI in late 2025 and makes Grok 4.1 Fast attractive for agentic workflows where tool calls dominate cost.

Halved hallucination rates vs the earlier Grok 4 Fast variant are documented on the model card. Source: openrouter.ai/x-ai/grok-4.1-fast. For pricing context across the full xAI lineup, see mem0.ai/blog/xai-grok-api-pricing.

Grok 4.3 (May 2026): Aggressive Pricing for the Reasoning Tier

Grok 4.3 launched on May 1 2026 at $1.25 per 1M input and $2.50 per 1M output. It is positioned as the higher-reasoning sibling to Grok 4.1 Fast, with a fast voice cloning suite bundled at launch. The pricing undercuts GPT-5 (full reasoning) and Claude Opus 4.x on the reasoning tier, which makes Grok 4.3 the budget option when you cannot live with Grok 4.1 Fast’s reasoning ceiling. Source: openrouter.ai/x-ai/grok-4.3.

Grok 4.x Architecture and Training Setup

xAI has not published a full technical report for Grok 4 the way it did for Grok 3, but the public statements at launch were:

• Trained on the expanded Colossus supercluster, an order-of-magnitude larger than the cluster that trained Grok 3.
• 100x more training compute than Grok 2.
• 10x more reinforcement-learning compute than Grok 3, with the RL stack focused on tool-use and multi-step reasoning.
• Native multimodality (text, image, document) at the API level, with Grok Imagine handling video and image generation as a companion model.

The reinforcement-learning emphasis is the architectural lever that drove the GPQA, AIME, and HLE gains. The same lever is what made Grok 4 better at agentic workflows than Grok 3 from day one, which is why Grok 4.1 Fast is now the default xAI choice for agents.

When Grok 3 Is Still the Right Answer

Grok 3 is not deprecated. xAI continues to serve it for customers with frozen integrations. Three scenarios where staying on Grok 3 makes sense:

• You have a regulatory model-pinning contract that forbids upgrading without re-certification.
• You depend on a specific Think Mode output format that Grok 4 has not preserved verbatim.
• You are paying a discounted legacy rate that the Grok 4.x SKUs do not match.

For everyone else, the migration to Grok 4.1 Fast or Grok 4.3 is straightforward (same API surface, same tool-calling format) and the cost reduction is material.

How to Evaluate Grok 3, Grok 4, and Grok 4.1 Fast for Your Workload

Public benchmarks tell you which model wins on AIME or GPQA. They do not tell you which model wins on your prompts. The evaluation loop that does:

1. Capture a representative sample. 200-500 real prompts from your production traffic, with annotated ground truth where you have it. Synthesize the rest.
2. Wire every call through a trace layer. Use traceAI (Apache 2.0, OTel-native) so every model swap is captured as a span tree with inputs, outputs, latency, and tool calls. Source on the license: github.com/future-agi/traceAI/blob/main/LICENSE.
3. Score every turn with eval templates. Future AGI ships 50+ templates including task completion, factuality, tool-selection accuracy, latency, cost, and PII leakage. Run them against every model variant in parallel.
4. Replay the same prompts across Grok 3, Grok 4.1 Fast, Grok 4.3, GPT-5, and Claude. Use FAGI’s prototype harness to do this side-by-side; the dashboard surfaces the winner per metric.
5. Guardrail in production. Once you pick a winner, wrap the deployed call in Agent Command Center to apply PII redaction, prompt-injection screening, and toxicity filters before the response reaches the user.

A minimal Future AGI eval setup for Grok models looks like this:

from fi.evals import evaluate
from fi.evals.metrics import CustomLLMJudge
from fi.evals.llm import LiteLLMProvider
import os

os.environ["FI_API_KEY"] = "..."
os.environ["FI_SECRET_KEY"] = "..."

# Replace these strings with the actual completions returned by each Grok model.
prompt = "Summarize the attached 200-page contract."
grok41_response = "Grok 4.1 Fast response goes here."
grok43_response = "Grok 4.3 response goes here."

judge = CustomLLMJudge(
    name="grok_task_completion",
    provider=LiteLLMProvider(model="gpt-5-2025-08-07"),
    prompt="Did the assistant fully resolve the user's request? Reply YES or NO.",
)

# Score each candidate against the same prompt with the built-in faithfulness
# and task-completion templates plus the custom judge.
for label, output in [("grok-4.1-fast", grok41_response), ("grok-4.3", grok43_response)]:
    print(label, "faithfulness:", evaluate("faithfulness", output=output, context=prompt))
    print(label, "task_completion:", evaluate("task_completion", input=prompt, output=output))
    print(label, "custom_judge:", judge(output=output))

For a deeper walkthrough on multi-model eval, see LLM Benchmarking Compared: 2026 and How to Build an LLM Evaluation Framework in 2026.

Bottom Line: Grok in May 2026

Grok 3 was a strong model in early 2025. It is no longer the right default in 2026. The right defaults are:

• Grok 4.1 Fast for cost-sensitive, agentic, or long-context workloads.
• Grok 4.3 for higher-reasoning workloads where you want to stay inside the xAI stack.
• Grok 3 only for frozen contracts or regulatory pinning.

Whatever you pick, run a real eval against your traffic. The model with the best public benchmark is rarely the model with the best p95 on your workload, and the difference between “looks fine in dev” and “ships in prod” is the trace plus eval loop above.

Future AGI is the evaluation, simulation, and Agent Command Center stack for that loop. Spin up a free workspace at app.futureagi.com, or read Best LLM Monitoring Tools in 2026 and Best AI Agent Observability Tools in 2026 for the comparison set.