Gemini 3.1 Pro preview vs Gemma 4 31B IT
Gemini 3.1 Pro preview (Google Vertex AI, 1,048,576-token context) versus Gemma 4 31B IT (SambaNova, 131,072-token context). Gemma 4 31B IT is cheaper by 89% on a blended token mix. Gemini 3.1 Pro preview uniquely supports function calling and audio input. Across 4 public benchmarks we tracked, Gemini 3.1 Pro preview wins 4 and Gemma 4 31B IT 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 Gemma 4 31B IT
Gemini 3.1 Pro preview and Gemma 4 31B IT target overlapping workloads but differ sharply on economics. Gemma 4 31B IT runs roughly 89% cheaper on a blended input-plus-output token mix, which translates to approximately $11,370 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.
Gemini 3.1 Pro preview ships a 1,048,576-token context window, 8.0x larger than Gemma 4 31B IT'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 Gemini 3.1 Pro preview is insurance you may never use — and Gemma 4 31B IT may win on other axes.
On capability surface area, the models diverge: Gemini 3.1 Pro preview supports function calling where the other does not; 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. 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.
Across 4 public benchmarks, Gemini 3.1 Pro preview leads on 4 and Gemma 4 31B IT leads on 0. The widest gap is on arena-webdev, where Gemini 3.1 Pro preview scores 80.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
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: gemma-4-31b-it
provider: sambanova
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 | Gemma 4 31B IT | |
|---|---|---|
| Input price | $2.00/M | $0.380/M |
| Output price | $12.00/M | $1.15/M |
| Context window | 1,048,576 | 131,072 |
| Max output | 65,536 | 131,072 |
| Function calling | ✓ | — |
| Vision | ✓ | ✓ |
| Audio input | ✓ | — |
| Reasoning | ✓ | — |
| Prompt caching | ✓ | — |
| Structured output | ✓ | — |
| Pricing verified | Oct 22, 2026 | Oct 22, 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 | Gemma 4 31B IT | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,320 /mo | $183 /mo | $1,137/mo |
| Mid-market 100K requests/day | $13,200 /mo | $1,830 /mo | $11,370/mo |
| Enterprise 1M requests/day | $132,000 /mo | $18,300 /mo | $113,700/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 — Gemma 4 31B IT runs ~89% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Gemini 3.1 Pro preview fits 1,048,576 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your agent listens to calls or voice notes — Gemini 3.1 Pro preview accepts audio input directly, the other requires an ASR preprocessing hop.
Your tasks involve multi-step planning or math-heavy reasoning — Gemini 3.1 Pro preview ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
You re-send the same large system prompt across requests — Gemini 3.1 Pro preview supports prompt caching, cutting input cost on repeat hits.
Your agent calls tools or APIs — Gemini 3.1 Pro preview supports function calling natively, the other model needs a parser shim.
On arena-webdev, Gemini 3.1 Pro preview scores 80.0 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 Gemma 4 31B IT means re-architecting that path (and vice versa).
- • Function calling
- • Audio input
- • PDF input
- • Structured output (JSON schema)
- • Prompt caching
- • Native reasoning mode
Capabilities both share (2)
- ✓ Vision input
- ✓ Streaming
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 | Gemma 4 31B IT | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1487.0 | 1452.0 | Gemini 3.1 Pro preview | +35.0 |
| arena-elo-coding | 1521.0 | 1502.0 | Gemini 3.1 Pro preview | +19.0 |
| arena-vision | 1280.0 | 1262.0 | Gemini 3.1 Pro preview | +18.0 |
| arena-webdev | 1447.0 | 1367.0 | Gemini 3.1 Pro preview | +80.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 88% when moving from Gemini 3.1 Pro preview (1,048,576) to Gemma 4 31B IT (131,072). 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 131,072 on Gemma 4 31B IT. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Gemini 3.1 Pro preview has capabilities Gemma 4 31B IT lacks: Function calling, Audio input, PDF input, Structured output (JSON schema), Prompt caching, Native reasoning mode. Switching to Gemma 4 31B IT means re-architecting any flow that depends on these.
- Provider changes from Google Vertex AI to SambaNova. 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 Gemma 4 31B IT 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 Gemma 4 31B IT 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 Gemma 4 31B IT
Which is cheaper, Gemini 3.1 Pro preview or Gemma 4 31B IT? ▾
Gemma 4 31B IT is cheaper by roughly 89% on a blended input + output token mix. Input prices are $2.00/M for Gemini 3.1 Pro preview versus $0.380/M for Gemma 4 31B IT; output prices are $12.00/M versus $1.15/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 Gemma 4 31B IT? ▾
Gemini 3.1 Pro preview supports up to 1,048,576 tokens of context. Gemma 4 31B IT supports up to 131,072 tokens. Gemini 3.1 Pro preview has the larger window by a factor of 8.0x, 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 Gemma 4 31B IT both support tool calling? ▾
Only Gemini 3.1 Pro preview supports native function calling. The other model can still be made to call tools through a structured-output workaround, but the reliability of that pattern is lower than native support.
Which model supports prompt caching for cost reduction? ▾
Gemini 3.1 Pro preview supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Gemini 3.1 Pro preview gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Gemini 3.1 Pro preview over Gemma 4 31B IT? ▾
Your workload needs long context — Gemini 3.1 Pro preview fits 1,048,576 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your agent listens to calls or voice notes — Gemini 3.1 Pro preview accepts audio input directly, the other requires an ASR preprocessing hop. Your tasks involve multi-step planning or math-heavy reasoning — Gemini 3.1 Pro preview ships a native reasoning mode that explicitly thinks before responding, the other doesn't. You re-send the same large system prompt across requests — Gemini 3.1 Pro preview supports prompt caching, cutting input cost on repeat hits. Your agent calls tools or APIs — Gemini 3.1 Pro preview supports function calling natively, the other model needs a parser shim. On arena-webdev, Gemini 3.1 Pro preview scores 80.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose Gemma 4 31B IT over Gemini 3.1 Pro preview? ▾
You're cost-sensitive at scale — Gemma 4 31B IT runs ~89% 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 Gemma 4 31B IT 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.