GPT 5.4 vs o1-preview
GPT 5.4 (OpenAI, 1,050,000-token context) versus o1-preview (OpenAI, 128,000-token context). GPT 5.4 is cheaper by 77% on a blended token mix. GPT 5.4 uniquely supports function calling and parallel tool calls. Across 1 public benchmark we tracked, GPT 5.4 wins 1 and o1-preview wins 0. 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 5.4 vs o1-preview
GPT 5.4 and o1-preview target overlapping workloads but differ sharply on economics. GPT 5.4 runs roughly 77% cheaper on a blended input-plus-output token mix, which translates to approximately $64,500 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 5.4 ships a 1,050,000-token context window, 8.2x 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 5.4 is insurance you may never use — and o1-preview may win on other axes.
On capability surface area, the models diverge: GPT 5.4 supports function calling where the other does not; GPT 5.4 supports parallel tool calls where the other does not; GPT 5.4 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-5-4
provider: openai
fallback:
model: o1-preview
provider: openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT 5.4 | o1-preview | |
|---|---|---|
| Input price | $2.50/M | $15.00/M |
| Output price | $15.00/M | $60.00/M |
| Context window | 1,050,000 | 128,000 |
| Max output | 128,000 | 32,768 |
| Function calling | ✓ | — |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Aug 6, 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 5.4 | o1-preview | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,650 /mo | $8,100 /mo | $6,450/mo |
| Mid-market 100K requests/day | $16,500 /mo | $81,000 /mo | $64,500/mo |
| Enterprise 1M requests/day | $165,000 /mo | $810,000 /mo | $645,000/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.
You're cost-sensitive at scale — GPT 5.4 runs ~77% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — GPT 5.4 fits 1,050,000 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 5.4 supports function calling natively, the other model needs a parser shim.
On arena-elo, GPT 5.4 scores 88.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
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 5.4, switching to o1-preview 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
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | GPT 5.4 | o1-preview | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1477.0 | 1389.0 | GPT 5.4 | +88.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 88% when moving from GPT 5.4 (1,050,000) 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 5.4 vs 32,768 on o1-preview. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT 5.4 has capabilities o1-preview lacks: Function calling, Parallel tool calls, Structured output (JSON schema). Switching to o1-preview means re-architecting any flow that depends on these.
- Pricing on o1-preview was last verified 139 days ago — confirm against the provider's published rate card before committing to a multi-month migration.
How to A/B test GPT 5.4 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 5.4 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 5.4 vs o1-preview
Which is cheaper, GPT 5.4 or o1-preview? ▾
GPT 5.4 is cheaper by roughly 77% on a blended input + output token mix. Input prices are $2.50/M for GPT 5.4 versus $15.00/M for o1-preview; output prices are $15.00/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 5.4 versus o1-preview? ▾
GPT 5.4 supports up to 1,050,000 tokens of context. o1-preview supports up to 128,000 tokens. GPT 5.4 has the larger window by a factor of 8.2x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GPT 5.4 and o1-preview both support tool calling? ▾
Only GPT 5.4 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 5.4 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 5.4 over o1-preview? ▾
You're cost-sensitive at scale — GPT 5.4 runs ~77% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — GPT 5.4 fits 1,050,000 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 5.4 supports function calling natively, the other model needs a parser shim. On arena-elo, GPT 5.4 scores 88.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose o1-preview over GPT 5.4? ▾
On the data this page surfaces, o1-preview is the right pick when GPT 5.4'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 5.4 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.