o1-preview vs o1 (2024-12-17)
o1-preview (Azure OpenAI, 128,000-token context) versus o1 (2024-12-17) (Azure OpenAI, 200,000-token context). o1-preview is cheaper by 9% on a blended token mix. o1-preview uniquely supports native reasoning mode. o1 (2024-12-17) uniquely supports vision input. 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 — o1-preview vs o1 (2024-12-17)
o1-preview and o1 (2024-12-17) are priced within 9% 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.
o1 (2024-12-17) ships a 200,000-token context window, 1.6x larger than o1-preview'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 o1 (2024-12-17) is insurance you may never use — and o1-preview may win on other axes.
On capability surface area, the models diverge: o1-preview supports native reasoning mode where the other does not; o1 (2024-12-17) supports vision 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.
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: o1-preview
provider: azure-openai
fallback:
model: us-o1-2024-12-17
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| o1-preview | o1 (2024-12-17) | |
|---|---|---|
| Input price | $15.00/M | $16.50/M |
| Output price | $60.00/M | $66.00/M |
| Context window | 128,000 | 200,000 |
| Max output | 32,768 | 100,000 |
| Function calling | ✓ | ✓ |
| Vision | — | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | — |
| Prompt caching | ✓ | ✓ |
| Structured output | — | — |
| Pricing verified | Aug 6, 2026 | Aug 6, 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 | o1-preview | o1 (2024-12-17) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $8,100 /mo | $8,910 /mo | $810/mo |
| Mid-market 100K requests/day | $81,000 /mo | $89,100 /mo | $8,100/mo |
| Enterprise 1M requests/day | $810,000 /mo | $891,000 /mo | $81,000/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.
Your inputs include screenshots, diagrams, or product photos — o1 (2024-12-17) accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — o1-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 o1-preview, switching to o1 (2024-12-17) means re-architecting that path (and vice versa).
- • Native reasoning mode
- • Vision input
Capabilities both share (4)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Streaming
- ✓ Prompt caching
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 56% when moving from o1-preview (128,000) to o1 (2024-12-17) (200,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 32,768 on o1-preview vs 100,000 on o1 (2024-12-17). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- o1-preview has capabilities o1 (2024-12-17) lacks: Native reasoning mode. Switching to o1 (2024-12-17) means re-architecting any flow that depends on these.
- o1 (2024-12-17) has capabilities o1-preview lacks: Vision input. Worth wiring through the agent design before commit.
How to A/B test o1-preview vs o1 (2024-12-17) 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 o1-preview primary, mirror 20% of traffic to o1 (2024-12-17) 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 — o1-preview vs o1 (2024-12-17)
Which is cheaper, o1-preview or o1 (2024-12-17)? ▾
o1-preview is cheaper by roughly 9% on a blended input + output token mix. Input prices are $15.00/M for o1-preview versus $16.50/M for o1 (2024-12-17); output prices are $60.00/M versus $66.00/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 o1-preview versus o1 (2024-12-17)? ▾
o1-preview supports up to 128,000 tokens of context. o1 (2024-12-17) supports up to 200,000 tokens. o1 (2024-12-17) has the larger window by a factor of 1.6x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do o1-preview and o1 (2024-12-17) both support tool calling? ▾
Yes — both o1-preview and o1 (2024-12-17) 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.
Can o1-preview and o1 (2024-12-17) process images? ▾
o1 (2024-12-17) accepts native image input. o1-preview does not — you would need to route image-heavy workloads through o1 (2024-12-17) or add a separate vision model in front of o1-preview.
Which model supports prompt caching for cost reduction? ▾
Both o1-preview and o1 (2024-12-17) 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 o1-preview over o1 (2024-12-17)? ▾
Your tasks involve multi-step planning or math-heavy reasoning — o1-preview ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose o1 (2024-12-17) over o1-preview? ▾
Your inputs include screenshots, diagrams, or product photos — o1 (2024-12-17) accepts image input natively, the other doesn't.
How do I A/B test o1-preview against o1 (2024-12-17) 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.