Ft GPT-4.1 (2025-04-14) vs GPT-5.4
Ft GPT-4.1 (2025-04-14) (OpenAI, 1,047,576-token context) versus GPT-5.4 (OpenAI, 1,050,000-token context). Ft GPT-4.1 (2025-04-14) is cheaper by 14% on a blended token mix. GPT-5.4 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 — Ft GPT-4.1 (2025-04-14) vs GPT-5.4
Ft GPT-4.1 (2025-04-14) and GPT-5.4 target overlapping workloads but differ sharply on economics. Ft GPT-4.1 (2025-04-14) runs roughly 14% cheaper on a blended input-plus-output token mix, which translates to approximately $300 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.
On capability surface area, the models diverge: GPT-5.4 supports vision input where the other does not; GPT-5.4 supports pdf input where the other does not; GPT-5.4 supports native reasoning mode 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: ft-gpt-4-1-2025-04-14
provider: openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Ft GPT-4.1 (2025-04-14) | GPT-5.4 | |
|---|---|---|
| Input price | $3.00/M | $2.50/M |
| Output price | $12.00/M | $15.00/M |
| Context window | 1,047,576 | 1,050,000 |
| Max output | 32,768 | 128,000 |
| Function calling | ✓ | ✓ |
| Vision | — | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 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 | Ft GPT-4.1 (2025-04-14) | GPT-5.4 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,620 /mo | $1,650 /mo | $30.00/mo |
| Mid-market 100K requests/day | $16,200 /mo | $16,500 /mo | $300/mo |
| Enterprise 1M requests/day | $162,000 /mo | $165,000 /mo | $3,000/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 — GPT-5.4 accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — GPT-5.4 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 Ft GPT-4.1 (2025-04-14), switching to GPT-5.4 means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Native reasoning mode
Capabilities both share (5)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ 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.
- Max output tokens differ: 32,768 on Ft GPT-4.1 (2025-04-14) vs 128,000 on GPT-5.4. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT-5.4 has capabilities Ft GPT-4.1 (2025-04-14) lacks: Vision input, PDF input, Native reasoning mode. Worth wiring through the agent design before commit.
How to A/B test Ft GPT-4.1 (2025-04-14) vs GPT-5.4 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 Ft GPT-4.1 (2025-04-14) primary, mirror 20% of traffic to GPT-5.4 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 — Ft GPT-4.1 (2025-04-14) vs GPT-5.4
Which is cheaper, Ft GPT-4.1 (2025-04-14) or GPT-5.4? ▾
Ft GPT-4.1 (2025-04-14) is cheaper by roughly 14% on a blended input + output token mix. Input prices are $3.00/M for Ft GPT-4.1 (2025-04-14) versus $2.50/M for GPT-5.4; output prices are $12.00/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 Ft GPT-4.1 (2025-04-14) versus GPT-5.4? ▾
Ft GPT-4.1 (2025-04-14) supports up to 1,047,576 tokens of context. GPT-5.4 supports up to 1,050,000 tokens. GPT-5.4 has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Ft GPT-4.1 (2025-04-14) and GPT-5.4 both support tool calling? ▾
Yes — both Ft GPT-4.1 (2025-04-14) and GPT-5.4 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 Ft GPT-4.1 (2025-04-14) and GPT-5.4 process images? ▾
GPT-5.4 accepts native image input. Ft GPT-4.1 (2025-04-14) does not — you would need to route image-heavy workloads through GPT-5.4 or add a separate vision model in front of Ft GPT-4.1 (2025-04-14).
Which model supports prompt caching for cost reduction? ▾
Both Ft GPT-4.1 (2025-04-14) and GPT-5.4 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 Ft GPT-4.1 (2025-04-14) over GPT-5.4? ▾
On the data this page surfaces, Ft GPT-4.1 (2025-04-14) 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.
When should I choose GPT-5.4 over Ft GPT-4.1 (2025-04-14)? ▾
Your inputs include screenshots, diagrams, or product photos — GPT-5.4 accepts image input natively, the other doesn't. Your tasks involve multi-step planning or math-heavy reasoning — GPT-5.4 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
How do I A/B test Ft GPT-4.1 (2025-04-14) against GPT-5.4 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.