GPT-5 vs o1-preview (2024-09-12)
GPT-5 (Azure OpenAI, 272,000-token context) versus o1-preview (2024-09-12) (OpenAI, 128,000-token context). GPT-5 is cheaper by 85% on a blended token mix. GPT-5 uniquely supports function calling and 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 — GPT-5 vs o1-preview (2024-09-12)
GPT-5 and o1-preview (2024-09-12) target overlapping workloads but differ sharply on economics. GPT-5 runs roughly 85% cheaper on a blended input-plus-output token mix, which translates to approximately $71,250 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 ships a 272,000-token context window, 2.1x larger than o1-preview (2024-09-12)'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 is insurance you may never use — and o1-preview (2024-09-12) may win on other axes.
On capability surface area, the models diverge: GPT-5 supports function calling where the other does not; GPT-5 supports parallel tool calls where the other does not; GPT-5 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
provider: azure-openai
fallback:
model: o1-preview-2024-09-12
provider: openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-5 | o1-preview (2024-09-12) | |
|---|---|---|
| Input price | $1.25/M | $15.00/M |
| Output price | $10.00/M | $60.00/M |
| Context window | 272,000 | 128,000 |
| Max output | 128,000 | 32,768 |
| Function calling | ✓ | — |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Jun 2, 2026 | May 7, 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 | o1-preview (2024-09-12) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $975 /mo | $8,100 /mo | $7,125/mo |
| Mid-market 100K requests/day | $9,750 /mo | $81,000 /mo | $71,250/mo |
| Enterprise 1M requests/day | $97,500 /mo | $810,000 /mo | $712,500/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.
You're cost-sensitive at scale — GPT-5 runs ~85% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — GPT-5 fits 272,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your agent calls tools or APIs — GPT-5 supports function calling natively, the other model needs a parser shim.
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, switching to o1-preview (2024-09-12) means re-architecting that path (and vice versa).
- • Function calling
- • Parallel tool calls
- • Structured output (JSON schema)
Capabilities both share (5)
- ✓ Vision input
- ✓ PDF input
- ✓ 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 53% when moving from GPT-5 (272,000) to o1-preview (2024-09-12) (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 128,000 on GPT-5 vs 32,768 on o1-preview (2024-09-12). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT-5 has capabilities o1-preview (2024-09-12) lacks: Function calling, Parallel tool calls, Structured output (JSON schema). Switching to o1-preview (2024-09-12) means re-architecting any flow that depends on these.
- Provider changes from Azure OpenAI 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 GPT-5 vs o1-preview (2024-09-12) 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 primary, mirror 20% of traffic to o1-preview (2024-09-12) 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 vs o1-preview (2024-09-12)
Which is cheaper, GPT-5 or o1-preview (2024-09-12)? ▾
GPT-5 is cheaper by roughly 85% on a blended input + output token mix. Input prices are $1.25/M for GPT-5 versus $15.00/M for o1-preview (2024-09-12); output prices are $10.00/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 versus o1-preview (2024-09-12)? ▾
GPT-5 supports up to 272,000 tokens of context. o1-preview (2024-09-12) supports up to 128,000 tokens. GPT-5 has the larger window by a factor of 2.1x, 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 and o1-preview (2024-09-12) both support tool calling? ▾
Only GPT-5 supports native function calling. The other model can still be made to call tools through a structured-output workaround, but the reliability of that pattern is lower than native support.
Which model supports prompt caching for cost reduction? ▾
Both GPT-5 and o1-preview (2024-09-12) 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 over o1-preview (2024-09-12)? ▾
You're cost-sensitive at scale — GPT-5 runs ~85% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — GPT-5 fits 272,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your agent calls tools or APIs — GPT-5 supports function calling natively, the other model needs a parser shim.
When should I choose o1-preview (2024-09-12) over GPT-5? ▾
On the data this page surfaces, o1-preview (2024-09-12) is the right pick when GPT-5'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 against o1-preview (2024-09-12) 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.