Amazon Nova 2 Pro preview 20251202 v1.0 vs Ft GPT 4o (2024-08-06)
Amazon Nova 2 Pro preview 20251202 v1.0 (Amazon Bedrock, 1,000,000-token context) versus Ft GPT 4o (2024-08-06) (OpenAI, 128,000-token context). Ft GPT 4o (2024-08-06) is cheaper by 5% on a blended token mix. Amazon Nova 2 Pro preview 20251202 v1.0 uniquely supports native reasoning mode. Ft GPT 4o (2024-08-06) uniquely supports parallel tool calls. 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 2 Pro preview 20251202 v1.0 vs Ft GPT 4o (2024-08-06)
Amazon Nova 2 Pro preview 20251202 v1.0 and Ft GPT 4o (2024-08-06) are priced within 5% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
Amazon Nova 2 Pro preview 20251202 v1.0 ships a 1,000,000-token context window, 7.8x larger than Ft GPT 4o (2024-08-06)'s 128,000 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 128,000 tokens, the extra context on Amazon Nova 2 Pro preview 20251202 v1.0 is insurance you may never use — and Ft GPT 4o (2024-08-06) may win on other axes.
On capability surface area, the models diverge: Amazon Nova 2 Pro preview 20251202 v1.0 supports native reasoning mode where the other does not; Ft GPT 4o (2024-08-06) supports parallel tool calls 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: amazon-nova-2-pro-preview-20251202-v1-0
provider: bedrock
fallback:
model: ft-gpt-4o-2024-08-06
provider: openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Amazon Nova 2 Pro preview 20251202 v1.0 | Ft GPT 4o (2024-08-06) | |
|---|---|---|
| Input price | $2.19/M | $3.75/M |
| Output price | $17.50/M | $15.00/M |
| Context window | 1,000,000 | 128,000 |
| Max output | 64,000 | 16,384 |
| 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 2 Pro preview 20251202 v1.0 | Ft GPT 4o (2024-08-06) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,706 /mo | $2,025 /mo | $319/mo |
| Mid-market 100K requests/day | $17,063 /mo | $20,250 /mo | $3,188/mo |
| Enterprise 1M requests/day | $170,625 /mo | $202,500 /mo | $31,875/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 — Amazon Nova 2 Pro preview 20251202 v1.0 fits 1,000,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your tasks involve multi-step planning or math-heavy reasoning — Amazon Nova 2 Pro preview 20251202 v1.0 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 2 Pro preview 20251202 v1.0, switching to Ft GPT 4o (2024-08-06) means re-architecting that path (and vice versa).
- • Native reasoning mode
- • Parallel tool calls
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 down 87% when moving from Amazon Nova 2 Pro preview 20251202 v1.0 (1,000,000) to Ft GPT 4o (2024-08-06) (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 64,000 on Amazon Nova 2 Pro preview 20251202 v1.0 vs 16,384 on Ft GPT 4o (2024-08-06). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Amazon Nova 2 Pro preview 20251202 v1.0 has capabilities Ft GPT 4o (2024-08-06) lacks: Native reasoning mode. Switching to Ft GPT 4o (2024-08-06) means re-architecting any flow that depends on these.
- Ft GPT 4o (2024-08-06) has capabilities Amazon Nova 2 Pro preview 20251202 v1.0 lacks: Parallel tool calls. Worth wiring through the agent design before commit.
- Provider changes from Amazon Bedrock to OpenAI. 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 2 Pro preview 20251202 v1.0 vs Ft GPT 4o (2024-08-06) 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 2 Pro preview 20251202 v1.0 primary, mirror 20% of traffic to Ft GPT 4o (2024-08-06) 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 2 Pro preview 20251202 v1.0 vs Ft GPT 4o (2024-08-06)
Which is cheaper, Amazon Nova 2 Pro preview 20251202 v1.0 or Ft GPT 4o (2024-08-06)? ▾
Ft GPT 4o (2024-08-06) is cheaper by roughly 5% on a blended input + output token mix. Input prices are $2.19/M for Amazon Nova 2 Pro preview 20251202 v1.0 versus $3.75/M for Ft GPT 4o (2024-08-06); output prices are $17.50/M versus $15.00/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 2 Pro preview 20251202 v1.0 versus Ft GPT 4o (2024-08-06)? ▾
Amazon Nova 2 Pro preview 20251202 v1.0 supports up to 1,000,000 tokens of context. Ft GPT 4o (2024-08-06) supports up to 128,000 tokens. Amazon Nova 2 Pro preview 20251202 v1.0 has the larger window by a factor of 7.8x, 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 2 Pro preview 20251202 v1.0 and Ft GPT 4o (2024-08-06) both support tool calling? ▾
Yes — both Amazon Nova 2 Pro preview 20251202 v1.0 and Ft GPT 4o (2024-08-06) 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 2 Pro preview 20251202 v1.0 and Ft GPT 4o (2024-08-06) 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 2 Pro preview 20251202 v1.0 over Ft GPT 4o (2024-08-06)? ▾
Your workload needs long context — Amazon Nova 2 Pro preview 20251202 v1.0 fits 1,000,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your tasks involve multi-step planning or math-heavy reasoning — Amazon Nova 2 Pro preview 20251202 v1.0 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose Ft GPT 4o (2024-08-06) over Amazon Nova 2 Pro preview 20251202 v1.0? ▾
On the data this page surfaces, Ft GPT 4o (2024-08-06) is the right pick when Amazon Nova 2 Pro preview 20251202 v1.0'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.
How do I A/B test Amazon Nova 2 Pro preview 20251202 v1.0 against Ft GPT 4o (2024-08-06) 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.