Ft o4 mini (2025-04-16) vs GPT-4o (2024-05-13)
Ft o4 mini (2025-04-16) (OpenAI, 200,000-token context) versus GPT-4o (2024-05-13) (Azure OpenAI, 128,000-token context). Ft o4 mini (2025-04-16) is cheaper by 0% on a blended token mix. Ft o4 mini (2025-04-16) uniquely supports structured output (json schema) and native reasoning mode. GPT-4o (2024-05-13) uniquely supports parallel tool calls and 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 — Ft o4 mini (2025-04-16) vs GPT-4o (2024-05-13)
Ft o4 mini (2025-04-16) and GPT-4o (2024-05-13) are priced within 0% 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.
Ft o4 mini (2025-04-16) ships a 200,000-token context window, 1.6x larger than GPT-4o (2024-05-13)'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 Ft o4 mini (2025-04-16) is insurance you may never use — and GPT-4o (2024-05-13) may win on other axes.
On capability surface area, the models diverge: Ft o4 mini (2025-04-16) supports structured output (json schema) where the other does not; Ft o4 mini (2025-04-16) supports native reasoning mode where the other does not; GPT-4o (2024-05-13) supports parallel tool calls 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: ft-o4-mini-2025-04-16
provider: openai
fallback:
model: gpt-4o-2024-05-13
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Ft o4 mini (2025-04-16) | GPT-4o (2024-05-13) | |
|---|---|---|
| Input price | $4.00/M | $5.00/M |
| Output price | $16.00/M | $15.00/M |
| Context window | 200,000 | 128,000 |
| Max output | 100,000 | 4,096 |
| 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 | Ft o4 mini (2025-04-16) | GPT-4o (2024-05-13) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $2,160 /mo | $2,400 /mo | $240/mo |
| Mid-market 100K requests/day | $21,600 /mo | $24,000 /mo | $2,400/mo |
| Enterprise 1M requests/day | $216,000 /mo | $240,000 /mo | $24,000/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-4o (2024-05-13) accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — Ft o4 mini (2025-04-16) 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 o4 mini (2025-04-16), switching to GPT-4o (2024-05-13) means re-architecting that path (and vice versa).
- • Structured output (JSON schema)
- • Native reasoning mode
- • Parallel tool calls
- • Vision input
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Prompt caching
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 36% when moving from Ft o4 mini (2025-04-16) (200,000) to GPT-4o (2024-05-13) (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 100,000 on Ft o4 mini (2025-04-16) vs 4,096 on GPT-4o (2024-05-13). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Ft o4 mini (2025-04-16) has capabilities GPT-4o (2024-05-13) lacks: Structured output (JSON schema), Native reasoning mode. Switching to GPT-4o (2024-05-13) means re-architecting any flow that depends on these.
- GPT-4o (2024-05-13) has capabilities Ft o4 mini (2025-04-16) lacks: Parallel tool calls, Vision input. Worth wiring through the agent design before commit.
- 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 Ft o4 mini (2025-04-16) vs GPT-4o (2024-05-13) 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 o4 mini (2025-04-16) primary, mirror 20% of traffic to GPT-4o (2024-05-13) 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 o4 mini (2025-04-16) vs GPT-4o (2024-05-13)
What is the context window of Ft o4 mini (2025-04-16) versus GPT-4o (2024-05-13)? ▾
Ft o4 mini (2025-04-16) supports up to 200,000 tokens of context. GPT-4o (2024-05-13) supports up to 128,000 tokens. Ft o4 mini (2025-04-16) 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 Ft o4 mini (2025-04-16) and GPT-4o (2024-05-13) both support tool calling? ▾
Yes — both Ft o4 mini (2025-04-16) and GPT-4o (2024-05-13) 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 o4 mini (2025-04-16) and GPT-4o (2024-05-13) process images? ▾
GPT-4o (2024-05-13) accepts native image input. Ft o4 mini (2025-04-16) does not — you would need to route image-heavy workloads through GPT-4o (2024-05-13) or add a separate vision model in front of Ft o4 mini (2025-04-16).
Which model supports prompt caching for cost reduction? ▾
Both Ft o4 mini (2025-04-16) and GPT-4o (2024-05-13) 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 o4 mini (2025-04-16) over GPT-4o (2024-05-13)? ▾
Your tasks involve multi-step planning or math-heavy reasoning — Ft o4 mini (2025-04-16) ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose GPT-4o (2024-05-13) over Ft o4 mini (2025-04-16)? ▾
Your inputs include screenshots, diagrams, or product photos — GPT-4o (2024-05-13) accepts image input natively, the other doesn't.
How do I A/B test Ft o4 mini (2025-04-16) against GPT-4o (2024-05-13) 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.