Gemini 3.1 Flash Lite preview vs Gemini 3.5 Flash Lite

Gemini 3.1 Flash Lite preview (Google Vertex AI, 1,048,576-token context) versus Gemini 3.5 Flash Lite (Google Vertex AI, 1,048,576-token context). Gemini 3.1 Flash Lite preview is cheaper by 37% on a blended token mix. Across 4 public benchmarks we tracked, Gemini 3.1 Flash Lite preview wins 0 and Gemini 3.5 Flash Lite wins 4. 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 Flash Lite preview vs Gemini 3.5 Flash Lite

Gemini 3.1 Flash Lite preview and Gemini 3.5 Flash Lite target overlapping workloads but differ sharply on economics. Gemini 3.1 Flash Lite preview runs roughly 37% cheaper on a blended input-plus-output token mix, which translates to approximately $750 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.

Across 4 public benchmarks, Gemini 3.1 Flash Lite preview leads on 0 and Gemini 3.5 Flash Lite leads on 4. The widest gap is on arena-webdev, where Gemini 3.5 Flash Lite scores 178.0 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.

Side-by-side cost

Live workload comparison

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

3,000
01,048,576
400
065,536
5,000
01,000,000
Google Vertex AI
$205/mo
Input $0.250/M · Output $1.50/M
Google Vertex AI
$289/mo
Input $0.300/M · Output $2.50/M
At this workload, Gemini 3.1 Flash Lite preview is 29% cheaper than Gemini 3.5 Flash Lite — a savings of $83.70/month ($1,004/year).
Production recipe — Agent Command Center
strategy: cost-optimized
primary:
  model: gemini-3-1-flash-lite-preview
  provider: vertex-ai
fallback:
  model: gemini-3-5-flash-lite
  provider: vertex-ai
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live
Gemini 3.1 Flash Lite preview Gemini 3.5 Flash Lite
Input price $0.250/M $0.300/M
Output price $1.50/M $2.50/M
Context window 1,048,576 1,048,576
Max output 65,536 65,536
Function calling ✓ ✓
Vision ✓ ✓
Audio input ✓ ✓
Reasoning ✓ ✓
Prompt caching ✓ ✓
Structured output ✓ ✓
Pricing verified Oct 22, 2026 Oct 22, 2026
Cheaper option
~37% cheaper than the priciest in this pair
Larger context
1,048,576 tokens
More capabilities
6 of 6 capability flags advertised

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 Flash Lite preview Gemini 3.5 Flash Lite Delta
Startup
10K requests/day
$165 /mo $240 /mo $75.00/mo
Mid-market
100K requests/day
$1,650 /mo $2,400 /mo $750/mo
Enterprise
1M requests/day
$16,500 /mo $24,000 /mo $7,500/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 Gemini 3.1 Flash Lite preview

You're cost-sensitive at scale — Gemini 3.1 Flash Lite preview runs ~37% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.

Choose Gemini 3.5 Flash Lite

On arena-webdev, Gemini 3.5 Flash Lite scores 178.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.

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 Flash Lite preview Gemini 3.5 Flash Lite Winner Δ
arena-elo 1433.0 1455.0 Gemini 3.5 Flash Lite +22.0
arena-elo-coding 1458.0 1502.0 Gemini 3.5 Flash Lite +44.0
arena-vision 1235.0 1267.0 Gemini 3.5 Flash Lite +32.0
arena-webdev 1257.0 1435.0 Gemini 3.5 Flash Lite +178.0

How to A/B test Gemini 3.1 Flash Lite preview vs Gemini 3.5 Flash Lite 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 Gemini 3.1 Flash Lite preview primary, mirror 20% of traffic to Gemini 3.5 Flash Lite 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 — Gemini 3.1 Flash Lite preview vs Gemini 3.5 Flash Lite

Which is cheaper, Gemini 3.1 Flash Lite preview or Gemini 3.5 Flash Lite? ▾

Gemini 3.1 Flash Lite preview is cheaper by roughly 37% on a blended input + output token mix. Input prices are $0.250/M for Gemini 3.1 Flash Lite preview versus $0.300/M for Gemini 3.5 Flash Lite; output prices are $1.50/M versus $2.50/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 Flash Lite preview versus Gemini 3.5 Flash Lite? ▾

Gemini 3.1 Flash Lite preview supports up to 1,048,576 tokens of context. Gemini 3.5 Flash Lite supports up to 1,048,576 tokens. Gemini 3.5 Flash Lite has the larger window by a factor of 1.0x, 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 Flash Lite preview and Gemini 3.5 Flash Lite both support tool calling? ▾

Yes — both Gemini 3.1 Flash Lite preview and Gemini 3.5 Flash Lite 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 Flash Lite preview and Gemini 3.5 Flash Lite 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 Flash Lite preview over Gemini 3.5 Flash Lite? ▾

You're cost-sensitive at scale — Gemini 3.1 Flash Lite preview runs ~37% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.

When should I choose Gemini 3.5 Flash Lite over Gemini 3.1 Flash Lite preview? ▾

On arena-webdev, Gemini 3.5 Flash Lite scores 178.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.

How do I A/B test Gemini 3.1 Flash Lite preview against Gemini 3.5 Flash Lite 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.