Amazon Nova Pro v1.0 vs Google Gemini 3 Flash preview
Amazon Nova Pro v1.0 (Amazon Bedrock, 300,000-token context) versus Google Gemini 3 Flash preview (OpenRouter, 1,048,576-token context). Google Gemini 3 Flash preview is cheaper by 12% on a blended token mix. Google Gemini 3 Flash preview uniquely supports parallel tool calls and native reasoning mode. 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 — Amazon Nova Pro v1.0 vs Google Gemini 3 Flash preview
Amazon Nova Pro v1.0 and Google Gemini 3 Flash preview target overlapping workloads but differ sharply on economics. Google Gemini 3 Flash preview runs roughly 12% cheaper on a blended input-plus-output token mix, which translates to approximately $1,020 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.
Google Gemini 3 Flash preview ships a 1,048,576-token context window, 3.5x larger than Amazon Nova Pro v1.0's 300,000 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 300,000 tokens, the extra context on Google Gemini 3 Flash preview is insurance you may never use — and Amazon Nova Pro v1.0 may win on other axes.
On capability surface area, the models diverge: Google Gemini 3 Flash preview supports parallel tool calls where the other does not; Google Gemini 3 Flash preview supports native reasoning mode 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: google-gemini-3-flash-preview
provider: openrouter
fallback:
model: amazon-nova-pro-v1-0
provider: bedrock
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Amazon Nova Pro v1.0 | Google Gemini 3 Flash preview | |
|---|---|---|
| Input price | $0.800/M | $0.500/M |
| Output price | $3.20/M | $3.00/M |
| Context window | 300,000 | 1,048,576 |
| Max output | 10,000 | 65,535 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
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 | Amazon Nova Pro v1.0 | Google Gemini 3 Flash preview | Delta |
|---|---|---|---|
| Startup 10K requests/day | $432 /mo | $330 /mo | $102/mo |
| Mid-market 100K requests/day | $4,320 /mo | $3,300 /mo | $1,020/mo |
| Enterprise 1M requests/day | $43,200 /mo | $33,000 /mo | $10,200/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.
Your workload needs long context — Google Gemini 3 Flash preview fits 1,048,576 tokens versus the other model's 300,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your tasks involve multi-step planning or math-heavy reasoning — Google Gemini 3 Flash preview 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 Amazon Nova Pro v1.0, switching to Google Gemini 3 Flash preview means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Native reasoning mode
Capabilities both share (6)
- ✓ Function calling
- ✓ Vision input
- ✓ PDF input
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Prompt caching
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 250% when moving from Amazon Nova Pro v1.0 (300,000) to Google Gemini 3 Flash preview (1,048,576). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 10,000 on Amazon Nova Pro v1.0 vs 65,535 on Google Gemini 3 Flash preview. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Google Gemini 3 Flash preview has capabilities Amazon Nova Pro v1.0 lacks: Parallel tool calls, Native reasoning mode. Worth wiring through the agent design before commit.
- Provider changes from Amazon Bedrock to OpenRouter. 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 Amazon Nova Pro v1.0 vs Google Gemini 3 Flash preview 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 Amazon Nova Pro v1.0 primary, mirror 20% of traffic to Google Gemini 3 Flash preview 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 — Amazon Nova Pro v1.0 vs Google Gemini 3 Flash preview
Which is cheaper, Amazon Nova Pro v1.0 or Google Gemini 3 Flash preview? ▾
Google Gemini 3 Flash preview is cheaper by roughly 12% on a blended input + output token mix. Input prices are $0.800/M for Amazon Nova Pro v1.0 versus $0.500/M for Google Gemini 3 Flash preview; output prices are $3.20/M versus $3.00/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 Amazon Nova Pro v1.0 versus Google Gemini 3 Flash preview? ▾
Amazon Nova Pro v1.0 supports up to 300,000 tokens of context. Google Gemini 3 Flash preview supports up to 1,048,576 tokens. Google Gemini 3 Flash preview has the larger window by a factor of 3.5x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Amazon Nova Pro v1.0 and Google Gemini 3 Flash preview both support tool calling? ▾
Yes — both Amazon Nova Pro v1.0 and Google Gemini 3 Flash preview 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 Amazon Nova Pro v1.0 and Google Gemini 3 Flash preview 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 Amazon Nova Pro v1.0 over Google Gemini 3 Flash preview? ▾
On the data this page surfaces, Amazon Nova Pro v1.0 is the right pick when Google Gemini 3 Flash preview'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 Google Gemini 3 Flash preview over Amazon Nova Pro v1.0? ▾
Your workload needs long context — Google Gemini 3 Flash preview fits 1,048,576 tokens versus the other model's 300,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your tasks involve multi-step planning or math-heavy reasoning — Google Gemini 3 Flash preview ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
How do I A/B test Amazon Nova Pro v1.0 against Google Gemini 3 Flash preview 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.