Grok 4 vs Grok 4.1 Fast

Grok 4 (xAI, 256,000-token context) versus Grok 4.1 Fast (xAI, 2,000,000-token context). Grok 4.1 Fast is cheaper by 96% on a blended token mix. Grok 4.1 Fast uniquely supports vision input and audio input. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.

Bottom line — Grok 4 vs Grok 4.1 Fast

Grok 4 and Grok 4.1 Fast target overlapping workloads but differ sharply on economics. Grok 4.1 Fast runs roughly 96% cheaper on a blended input-plus-output token mix, which translates to approximately $17,100 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.

Grok 4.1 Fast ships a 2,000,000-token context window, 7.8x larger than Grok 4's 256,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 256,000 tokens, the extra context on Grok 4.1 Fast is insurance you may never use — and Grok 4 may win on other axes.

On capability surface area, the models diverge: Grok 4.1 Fast supports vision input where the other does not; Grok 4.1 Fast supports audio input where the other does not; Grok 4.1 Fast supports structured output (json schema) where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.

For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.

Side-by-side cost

Live workload comparison

Same workload run through both models. The cheaper one is highlighted.

3,000
02,000,000
400
0200,000
5,000
01,000,000
xAI
$2,283/mo
Input $3.00/M · Output $15.00/M
xAI
$122/mo
Input $0.200/M · Output $0.500/M
At this workload, Grok 4.1 Fast is 95% cheaper than Grok 4 — a savings of $2,161/month ($25,933/year).
Production recipe — Agent Command Center
strategy: cost-optimized
primary:
  model: grok-4-1-fast
  provider: xai
fallback:
  model: grok-4
  provider: xai
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live
Grok 4
xAI
Grok 4.1 Fast
xAI
Input price $3.00/M $0.200/M
Output price $15.00/M $0.500/M
Context window 256,000 2,000,000
Max output 256,000 2,000,000
Function calling
Vision
Audio input
Reasoning
Prompt caching
Structured output
Pricing verified Jun 2, 2026 Jun 2, 2026
Cheaper option
~96% cheaper than the priciest in this pair
Larger context
2,000,000 tokens
More capabilities
6 of 6 capability flags advertised

Benchmark comparison

Side-by-side public benchmark scores. Greener bar = winner.

Chatbot Arena ELOgeneral
Grok 4
1,459
Grok 4.1 Fast
MATH-500math
Grok 4
98.0%
Grok 4.1 Fast
AIME 2024math
Grok 4
93.3%
Grok 4.1 Fast
GPQA Diamondreasoning
Grok 4
87.5%
Grok 4.1 Fast
MMLU-Proreasoning
Grok 4
86.6%
Grok 4.1 Fast
BFCL v3agent
Grok 4
79.5%
Grok 4.1 Fast
LiveCodeBenchcode
Grok 4
79.4%
Grok 4.1 Fast
SWE-bench Verifiedagent
Grok 4
72.0%
Grok 4.1 Fast
Humanity's Last Examreasoning
Grok 4
25.4%
Grok 4.1 Fast
ARC-AGI-2reasoning
Grok 4
15.9%
Grok 4.1 Fast

Cost at scale: monthly spend at three usage volumes

Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.

Scale Grok 4 Grok 4.1 Fast Delta
Startup
10K requests/day
$1,800 /mo $90.00 /mo $1,710/mo
Mid-market
100K requests/day
$18,000 /mo $900 /mo $17,100/mo
Enterprise
1M requests/day
$180,000 /mo $9,000 /mo $171,000/mo

At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.

When to choose which

Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.

Choose Grok 4.1 Fast

You're cost-sensitive at scale — Grok 4.1 Fast runs ~96% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.

Choose Grok 4.1 Fast

Your workload needs long context — Grok 4.1 Fast fits 2,000,000 tokens versus the other model's 256,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.

Choose Grok 4.1 Fast

Your inputs include screenshots, diagrams, or product photos — Grok 4.1 Fast accepts image input natively, the other doesn't.

Choose Grok 4.1 Fast

Your agent listens to calls or voice notes — Grok 4.1 Fast accepts audio input directly, the other requires an ASR preprocessing hop.

Choose Grok 4.1 Fast

Your tasks involve multi-step planning or math-heavy reasoning — Grok 4.1 Fast ships a native reasoning mode that explicitly thinks before responding, the other doesn't.

Capability diff — what you gain and lose on the swap

A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Grok 4, switching to Grok 4.1 Fast means re-architecting that path (and vice versa).

Only on Grok 4
Nothing — everything Grok 4 ships is also on Grok 4.1 Fast.
Only on Grok 4.1 Fast
  • • Vision input
  • • Audio input
  • • Structured output (JSON schema)
  • • Native reasoning mode
Capabilities both share (3)
  • ✓ Function calling
  • ✓ Streaming
  • ✓ Prompt caching

Migration considerations

Concrete differences to wire through your stack before you flip traffic from one to the other.

  • Context window changes up 681% when moving from Grok 4 (256,000) to Grok 4.1 Fast (2,000,000). Re-check any prompt that relies on cramming long history or documents.
  • Max output tokens differ: 256,000 on Grok 4 vs 2,000,000 on Grok 4.1 Fast. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
  • Grok 4.1 Fast has capabilities Grok 4 lacks: Vision input, Audio input, Structured output (JSON schema), Native reasoning mode. Worth wiring through the agent design before commit.

How to A/B test Grok 4 vs Grok 4.1 Fast in production

If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:

  1. 1. Point your existing OpenAI SDK at https://gateway.futureagi.com/v1. No code change beyond base_url and a virtual key.
  2. 2. Mark Grok 4 primary, mirror 20% of traffic to Grok 4.1 Fast in shadow mode. Both responses are logged; only the primary is served to users.
  3. 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
  4. 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.

Full walkthrough on the Agent Command Center page.

FAQ — Grok 4 vs Grok 4.1 Fast

Which is cheaper, Grok 4 or Grok 4.1 Fast?

Grok 4.1 Fast is cheaper by roughly 96% on a blended input + output token mix. Input prices are $3.00/M for Grok 4 versus $0.200/M for Grok 4.1 Fast; output prices are $15.00/M versus $0.500/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.

What is the context window of Grok 4 versus Grok 4.1 Fast?

Grok 4 supports up to 256,000 tokens of context. Grok 4.1 Fast supports up to 2,000,000 tokens. Grok 4.1 Fast has the larger window by a factor of 7.8x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.

Do Grok 4 and Grok 4.1 Fast both support tool calling?

Yes — both Grok 4 and Grok 4.1 Fast support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.

Can Grok 4 and Grok 4.1 Fast process images?

Grok 4.1 Fast accepts native image input. Grok 4 does not — you would need to route image-heavy workloads through Grok 4.1 Fast or add a separate vision model in front of Grok 4.

Which model supports prompt caching for cost reduction?

Both Grok 4 and Grok 4.1 Fast support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.

When should I choose Grok 4 over Grok 4.1 Fast?

On the data this page surfaces, Grok 4 is the right pick when Grok 4.1 Fast's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.

When should I choose Grok 4.1 Fast over Grok 4?

You're cost-sensitive at scale — Grok 4.1 Fast runs ~96% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — Grok 4.1 Fast fits 2,000,000 tokens versus the other model's 256,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Grok 4.1 Fast accepts image input natively, the other doesn't. Your agent listens to calls or voice notes — Grok 4.1 Fast accepts audio input directly, the other requires an ASR preprocessing hop. Your tasks involve multi-step planning or math-heavy reasoning — Grok 4.1 Fast ships a native reasoning mode that explicitly thinks before responding, the other doesn't.

How do I A/B test Grok 4 against Grok 4.1 Fast in production?

Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.