Grok 3 mini Fast latest vs Grok 4.20 Multi Agent beta 0309

Grok 3 mini Fast latest (xAI, 131,072-token context) versus Grok 4.20 Multi Agent beta 0309 (xAI, 2,000,000-token context). Grok 3 mini Fast latest is cheaper by 43% on a blended token mix. Grok 4.20 Multi Agent beta 0309 uniquely supports vision 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 3 mini Fast latest vs Grok 4.20 Multi Agent beta 0309

Grok 3 mini Fast latest and Grok 4.20 Multi Agent beta 0309 target overlapping workloads but differ sharply on economics. Grok 3 mini Fast latest runs roughly 43% cheaper on a blended input-plus-output token mix, which translates to approximately $5,400 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 4.20 Multi Agent beta 0309 ships a 2,000,000-token context window, 15.3x larger than Grok 3 mini Fast latest's 131,072 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 131,072 tokens, the extra context on Grok 4.20 Multi Agent beta 0309 is insurance you may never use — and Grok 3 mini Fast latest may win on other axes.

On capability surface area, the models diverge: Grok 4.20 Multi Agent beta 0309 supports vision input 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
$517/mo
Input $0.600/M · Output $4.00/M
xAI
$1,278/mo
Input $2.00/M · Output $6.00/M
At this workload, Grok 3 mini Fast latest is 60% cheaper than Grok 4.20 Multi Agent beta 0309 — a savings of $761/month ($9,131/year).
Production recipe — Agent Command Center
strategy: cost-optimized
primary:
  model: grok-3-mini-fast-latest
  provider: xai
fallback:
  model: grok-4-20-multi-agent-beta-0309
  provider: xai
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live
Grok 3 mini Fast latest
xAI
Grok 4.20 Multi Agent beta 0309
xAI
Input price $0.600/M $2.00/M
Output price $4.00/M $6.00/M
Context window 131,072 2,000,000
Max output 131,072 2,000,000
Function calling ✓ ✓
Vision — ✓
Audio input — —
Reasoning ✓ ✓
Prompt caching ✓ ✓
Structured output — —
Pricing verified Aug 6, 2026 Aug 6, 2026
Cheaper option
~43% cheaper than the priciest in this pair
Larger context
2,000,000 tokens
More capabilities
4 of 6 capability flags advertised

Benchmark comparison

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

Chatbot Arena ELOgeneral
Grok 3 mini Fast latest
—
Grok 4.20 Multi Agent beta 0309
1,473

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 3 mini Fast latest Grok 4.20 Multi Agent beta 0309 Delta
Startup
10K requests/day
$420 /mo $960 /mo $540/mo
Mid-market
100K requests/day
$4,200 /mo $9,600 /mo $5,400/mo
Enterprise
1M requests/day
$42,000 /mo $96,000 /mo $54,000/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 3 mini Fast latest

You're cost-sensitive at scale — Grok 3 mini Fast latest runs ~43% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.

Choose Grok 4.20 Multi Agent beta 0309

Your workload needs long context — Grok 4.20 Multi Agent beta 0309 fits 2,000,000 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot.

Choose Grok 4.20 Multi Agent beta 0309

Your inputs include screenshots, diagrams, or product photos — Grok 4.20 Multi Agent beta 0309 accepts image input natively, 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 3 mini Fast latest, switching to Grok 4.20 Multi Agent beta 0309 means re-architecting that path (and vice versa).

Only on Grok 3 mini Fast latest
Nothing — everything Grok 3 mini Fast latest ships is also on Grok 4.20 Multi Agent beta 0309.
Only on Grok 4.20 Multi Agent beta 0309
  • • Vision input
Capabilities both share (4)
  • ✓ Function calling
  • ✓ Streaming
  • ✓ Prompt caching
  • ✓ Native reasoning mode

Migration considerations

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

  • Context window changes up 1426% when moving from Grok 3 mini Fast latest (131,072) to Grok 4.20 Multi Agent beta 0309 (2,000,000). Re-check any prompt that relies on cramming long history or documents.
  • Max output tokens differ: 131,072 on Grok 3 mini Fast latest vs 2,000,000 on Grok 4.20 Multi Agent beta 0309. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
  • Grok 4.20 Multi Agent beta 0309 has capabilities Grok 3 mini Fast latest lacks: Vision input. Worth wiring through the agent design before commit.

How to A/B test Grok 3 mini Fast latest vs Grok 4.20 Multi Agent beta 0309 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 3 mini Fast latest primary, mirror 20% of traffic to Grok 4.20 Multi Agent beta 0309 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 3 mini Fast latest vs Grok 4.20 Multi Agent beta 0309

Which is cheaper, Grok 3 mini Fast latest or Grok 4.20 Multi Agent beta 0309? ▾

Grok 3 mini Fast latest is cheaper by roughly 43% on a blended input + output token mix. Input prices are $0.600/M for Grok 3 mini Fast latest versus $2.00/M for Grok 4.20 Multi Agent beta 0309; output prices are $4.00/M versus $6.00/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 3 mini Fast latest versus Grok 4.20 Multi Agent beta 0309? ▾

Grok 3 mini Fast latest supports up to 131,072 tokens of context. Grok 4.20 Multi Agent beta 0309 supports up to 2,000,000 tokens. Grok 4.20 Multi Agent beta 0309 has the larger window by a factor of 15.3x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.

Do Grok 3 mini Fast latest and Grok 4.20 Multi Agent beta 0309 both support tool calling? ▾

Yes — both Grok 3 mini Fast latest and Grok 4.20 Multi Agent beta 0309 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 3 mini Fast latest and Grok 4.20 Multi Agent beta 0309 process images? ▾

Grok 4.20 Multi Agent beta 0309 accepts native image input. Grok 3 mini Fast latest does not — you would need to route image-heavy workloads through Grok 4.20 Multi Agent beta 0309 or add a separate vision model in front of Grok 3 mini Fast latest.

Which model supports prompt caching for cost reduction? ▾

Both Grok 3 mini Fast latest and Grok 4.20 Multi Agent beta 0309 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 3 mini Fast latest over Grok 4.20 Multi Agent beta 0309? ▾

You're cost-sensitive at scale — Grok 3 mini Fast latest runs ~43% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.

When should I choose Grok 4.20 Multi Agent beta 0309 over Grok 3 mini Fast latest? ▾

Your workload needs long context — Grok 4.20 Multi Agent beta 0309 fits 2,000,000 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Grok 4.20 Multi Agent beta 0309 accepts image input natively, the other doesn't.

How do I A/B test Grok 3 mini Fast latest against Grok 4.20 Multi Agent beta 0309 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.