Gemini 3.1 Pro preview vs Grok 4.20 Multi Agent beta 0309
Gemini 3.1 Pro preview (Google Vertex AI, 1,048,576-token context) versus Grok 4.20 Multi Agent beta 0309 (xAI, 2,000,000-token context). Grok 4.20 Multi Agent beta 0309 is cheaper by 43% on a blended token mix. Gemini 3.1 Pro preview uniquely supports audio input and pdf input. Across 1 public benchmark we tracked, Gemini 3.1 Pro preview wins 1 and Grok 4.20 Multi Agent beta 0309 wins 0. 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 — Gemini 3.1 Pro preview vs Grok 4.20 Multi Agent beta 0309
Gemini 3.1 Pro preview and Grok 4.20 Multi Agent beta 0309 target overlapping workloads but differ sharply on economics. Grok 4.20 Multi Agent beta 0309 runs roughly 43% cheaper on a blended input-plus-output token mix, which translates to approximately $3,600 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, 1.9x larger than Gemini 3.1 Pro preview's 1,048,576 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 1,048,576 tokens, the extra context on Grok 4.20 Multi Agent beta 0309 is insurance you may never use — and Gemini 3.1 Pro preview may win on other axes.
On capability surface area, the models diverge: Gemini 3.1 Pro preview supports audio input where the other does not; Gemini 3.1 Pro preview supports pdf input where the other does not; Gemini 3.1 Pro preview 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.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: grok-4-20-multi-agent-beta-0309
provider: xai
fallback:
model: gemini-3-1-pro-preview
provider: vertex-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Gemini 3.1 Pro preview | Grok 4.20 Multi Agent beta 0309 | |
|---|---|---|
| Input price | $2.00/M | $2.00/M |
| Output price | $12.00/M | $6.00/M |
| Context window | 1,048,576 | 2,000,000 |
| Max output | 65,536 | 2,000,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | ✓ | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Jun 2, 2026 | Jun 2, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
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 | Gemini 3.1 Pro preview | Grok 4.20 Multi Agent beta 0309 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,320 /mo | $960 /mo | $360/mo |
| Mid-market 100K requests/day | $13,200 /mo | $9,600 /mo | $3,600/mo |
| Enterprise 1M requests/day | $132,000 /mo | $96,000 /mo | $36,000/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.
You're cost-sensitive at scale — Grok 4.20 Multi Agent beta 0309 runs ~43% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your agent listens to calls or voice notes — Gemini 3.1 Pro preview accepts audio input directly, the other requires an ASR preprocessing hop.
On arena-elo, Gemini 3.1 Pro preview scores 19.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
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 Gemini 3.1 Pro preview, switching to Grok 4.20 Multi Agent beta 0309 means re-architecting that path (and vice versa).
- • Audio input
- • PDF input
- • Structured output (JSON schema)
Capabilities both share (5)
- ✓ Function calling
- ✓ Vision input
- ✓ Streaming
- ✓ Prompt caching
- ✓ Native reasoning mode
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | Gemini 3.1 Pro preview | Grok 4.20 Multi Agent beta 0309 | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1492.0 | 1473.0 | Gemini 3.1 Pro preview | +19.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 91% when moving from Gemini 3.1 Pro preview (1,048,576) 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: 65,536 on Gemini 3.1 Pro preview 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.
- Gemini 3.1 Pro preview has capabilities Grok 4.20 Multi Agent beta 0309 lacks: Audio input, PDF input, Structured output (JSON schema). Switching to Grok 4.20 Multi Agent beta 0309 means re-architecting any flow that depends on these.
- Provider changes from Google Vertex AI to xAI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Gemini 3.1 Pro preview 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. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Gemini 3.1 Pro preview 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. 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. 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 — Gemini 3.1 Pro preview vs Grok 4.20 Multi Agent beta 0309
Which is cheaper, Gemini 3.1 Pro preview or Grok 4.20 Multi Agent beta 0309? ▾
Grok 4.20 Multi Agent beta 0309 is cheaper by roughly 43% on a blended input + output token mix. Input prices are $2.00/M for Gemini 3.1 Pro preview versus $2.00/M for Grok 4.20 Multi Agent beta 0309; output prices are $12.00/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 Gemini 3.1 Pro preview versus Grok 4.20 Multi Agent beta 0309? ▾
Gemini 3.1 Pro preview supports up to 1,048,576 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 1.9x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Gemini 3.1 Pro preview and Grok 4.20 Multi Agent beta 0309 both support tool calling? ▾
Yes — both Gemini 3.1 Pro preview 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.
Which model supports prompt caching for cost reduction? ▾
Both Gemini 3.1 Pro preview 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 Gemini 3.1 Pro preview over Grok 4.20 Multi Agent beta 0309? ▾
Your agent listens to calls or voice notes — Gemini 3.1 Pro preview accepts audio input directly, the other requires an ASR preprocessing hop. On arena-elo, Gemini 3.1 Pro preview scores 19.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose Grok 4.20 Multi Agent beta 0309 over Gemini 3.1 Pro preview? ▾
You're cost-sensitive at scale — Grok 4.20 Multi Agent beta 0309 runs ~43% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
How do I A/B test Gemini 3.1 Pro preview 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.