GPT-5.6 Cyber vs o1-preview
GPT-5.6 Cyber (OpenAI, 400,000-token context) versus o1-preview (Azure OpenAI, 128,000-token context). o1-preview is cheaper by 14% on a blended token mix. GPT-5.6 Cyber uniquely supports vision input and pdf 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 — GPT-5.6 Cyber vs o1-preview
GPT-5.6 Cyber and o1-preview target overlapping workloads but differ sharply on economics. o1-preview runs roughly 14% cheaper on a blended input-plus-output token mix, which translates to approximately $1,500 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.
GPT-5.6 Cyber ships a 400,000-token context window, 3.1x 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 GPT-5.6 Cyber is insurance you may never use — and o1-preview may win on other axes.
On capability surface area, the models diverge: GPT-5.6 Cyber supports vision input where the other does not; GPT-5.6 Cyber supports pdf input where the other does not; GPT-5.6 Cyber supports structured output (json schema) 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: gpt-5-6-cyber
provider: openai
fallback:
model: o1-preview
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-5.6 Cyber | o1-preview | |
|---|---|---|
| Input price | $12.50/M | $15.00/M |
| Output price | $75.00/M | $60.00/M |
| Context window | 400,000 | 128,000 |
| Max output | 128,000 | 32,768 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Oct 22, 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 | GPT-5.6 Cyber | o1-preview | Delta |
|---|---|---|---|
| Startup 10K requests/day | $8,250 /mo | $8,100 /mo | $150/mo |
| Mid-market 100K requests/day | $82,500 /mo | $81,000 /mo | $1,500/mo |
| Enterprise 1M requests/day | $825,000 /mo | $810,000 /mo | $15,000/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 workload needs long context — GPT-5.6 Cyber fits 400,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — GPT-5.6 Cyber accepts image input natively, 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 GPT-5.6 Cyber, switching to o1-preview means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Structured output (JSON schema)
Capabilities both share (5)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Streaming
- ✓ Prompt caching
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 68% when moving from GPT-5.6 Cyber (400,000) to o1-preview (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 128,000 on GPT-5.6 Cyber vs 32,768 on o1-preview. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT-5.6 Cyber has capabilities o1-preview lacks: Vision input, PDF input, Structured output (JSON schema). Switching to o1-preview means re-architecting any flow that depends on these.
- Provider changes from OpenAI to Azure 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 GPT-5.6 Cyber vs o1-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 GPT-5.6 Cyber primary, mirror 20% of traffic to o1-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 — GPT-5.6 Cyber vs o1-preview
Which is cheaper, GPT-5.6 Cyber or o1-preview? ▾
o1-preview is cheaper by roughly 14% on a blended input + output token mix. Input prices are $12.50/M for GPT-5.6 Cyber versus $15.00/M for o1-preview; output prices are $75.00/M versus $60.00/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 GPT-5.6 Cyber versus o1-preview? ▾
GPT-5.6 Cyber supports up to 400,000 tokens of context. o1-preview supports up to 128,000 tokens. GPT-5.6 Cyber has the larger window by a factor of 3.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GPT-5.6 Cyber and o1-preview both support tool calling? ▾
Yes — both GPT-5.6 Cyber and o1-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.
Can GPT-5.6 Cyber and o1-preview process images? ▾
GPT-5.6 Cyber accepts native image input. o1-preview does not — you would need to route image-heavy workloads through GPT-5.6 Cyber or add a separate vision model in front of o1-preview.
Which model supports prompt caching for cost reduction? ▾
Both GPT-5.6 Cyber and o1-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 GPT-5.6 Cyber over o1-preview? ▾
Your workload needs long context — GPT-5.6 Cyber fits 400,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — GPT-5.6 Cyber accepts image input natively, the other doesn't.
When should I choose o1-preview over GPT-5.6 Cyber? ▾
On the data this page surfaces, o1-preview is the right pick when GPT-5.6 Cyber'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 GPT-5.6 Cyber against o1-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.