GPT-4o mini vs o4-mini
GPT-4o mini (OpenAI, 128,000-token context) versus o4-mini (OpenAI, 200,000-token context). GPT-4o mini is cheaper by 86% on a blended token mix. GPT-4o mini uniquely supports parallel tool calls. o4-mini uniquely supports native reasoning mode. 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-4o mini vs o4-mini
GPT-4o mini and o4-mini target overlapping workloads but differ sharply on economics. GPT-4o mini runs roughly 86% cheaper on a blended input-plus-output token mix, which translates to approximately $5,130 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.
o4-mini ships a 200,000-token context window, 1.6x larger than GPT-4o mini'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 o4-mini is insurance you may never use — and GPT-4o mini may win on other axes.
On capability surface area, the models diverge: GPT-4o mini supports parallel tool calls where the other does not; o4-mini 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-4o-mini
provider: openai
fallback:
model: o4-mini
provider: openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-4o mini | o4-mini | |
|---|---|---|
| Input price | $0.150/M | $1.10/M |
| Output price | $0.600/M | $4.40/M |
| Context window | 128,000 | 200,000 |
| Max output | 16,384 | 100,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | May 19, 2026 | May 19, 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-4o mini | o4-mini | Delta |
|---|---|---|---|
| Startup 10K requests/day | $81.00 /mo | $594 /mo | $513/mo |
| Mid-market 100K requests/day | $810 /mo | $5,940 /mo | $5,130/mo |
| Enterprise 1M requests/day | $8,100 /mo | $59,400 /mo | $51,300/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-4o mini runs ~86% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your tasks involve multi-step planning or math-heavy reasoning — o4-mini 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 GPT-4o mini, switching to o4-mini means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Native reasoning mode
Capabilities both share (6)
- ✓ Function calling
- ✓ Vision input
- ✓ PDF input
- ✓ 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.
- Context window changes up 56% when moving from GPT-4o mini (128,000) to o4-mini (200,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 16,384 on GPT-4o mini vs 100,000 on o4-mini. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT-4o mini has capabilities o4-mini lacks: Parallel tool calls. Switching to o4-mini means re-architecting any flow that depends on these.
- o4-mini has capabilities GPT-4o mini lacks: Native reasoning mode. Worth wiring through the agent design before commit.
How to A/B test GPT-4o mini vs o4-mini 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-4o mini primary, mirror 20% of traffic to o4-mini 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-4o mini vs o4-mini
Which is cheaper, GPT-4o mini or o4-mini? ▾
GPT-4o mini is cheaper by roughly 86% on a blended input + output token mix. Input prices are $0.150/M for GPT-4o mini versus $1.10/M for o4-mini; output prices are $0.600/M versus $4.40/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-4o mini versus o4-mini? ▾
GPT-4o mini supports up to 128,000 tokens of context. o4-mini supports up to 200,000 tokens. o4-mini 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 GPT-4o mini and o4-mini both support tool calling? ▾
Yes — both GPT-4o mini and o4-mini 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.
Which model supports prompt caching for cost reduction? ▾
Both GPT-4o mini and o4-mini 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-4o mini over o4-mini? ▾
You're cost-sensitive at scale — GPT-4o mini runs ~86% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
When should I choose o4-mini over GPT-4o mini? ▾
Your tasks involve multi-step planning or math-heavy reasoning — o4-mini ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
How do I A/B test GPT-4o mini against o4-mini 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.