o1 vs o3-mini (2025-01-31)
o1 (Azure OpenAI, 200,000-token context) versus o3-mini (2025-01-31) (OpenAI, 200,000-token context). o3-mini (2025-01-31) is cheaper by 93% on a blended token mix. o1 uniquely supports parallel tool calls and vision input. o3-mini (2025-01-31) uniquely supports structured output (json schema). 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.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: o3-mini-2025-01-31
provider: openai
fallback:
model: o1
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| o1 | o3-mini (2025-01-31) | |
|---|---|---|
| Input price | $15.00/M | $1.10/M |
| Output price | $60.00/M | $4.40/M |
| Context window | 200,000 | 200,000 |
| Max output | 100,000 | 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 | o1 | o3-mini (2025-01-31) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $8,100 /mo | $594 /mo | $7,506/mo |
| Mid-market 100K requests/day | $81,000 /mo | $5,940 /mo | $75,060/mo |
| Enterprise 1M requests/day | $810,000 /mo | $59,400 /mo | $750,600/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 — o3-mini (2025-01-31) runs ~93% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your inputs include screenshots, diagrams, or product photos — o1 accepts image input natively, 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 o1, switching to o3-mini (2025-01-31) means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Vision input
- • Structured output (JSON schema)
Capabilities both share (4)
- ✓ Function calling
- ✓ Streaming
- ✓ Prompt caching
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- o1 has capabilities o3-mini (2025-01-31) lacks: Parallel tool calls, Vision input. Switching to o3-mini (2025-01-31) means re-architecting any flow that depends on these.
- o3-mini (2025-01-31) has capabilities o1 lacks: Structured output (JSON schema). Worth wiring through the agent design before commit.
- 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 o1 vs o3-mini (2025-01-31) 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 o1 primary, mirror 20% of traffic to o3-mini (2025-01-31) 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 — o1 vs o3-mini (2025-01-31)
Which is cheaper, o1 or o3-mini (2025-01-31)? ▾
o3-mini (2025-01-31) is cheaper by roughly 93% on a blended input + output token mix. Input prices are $15.00/M for o1 versus $1.10/M for o3-mini (2025-01-31); output prices are $60.00/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 o1 versus o3-mini (2025-01-31)? ▾
o1 supports up to 200,000 tokens of context. o3-mini (2025-01-31) supports up to 200,000 tokens. o3-mini (2025-01-31) 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 o1 and o3-mini (2025-01-31) both support tool calling? ▾
Yes — both o1 and o3-mini (2025-01-31) 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 o1 and o3-mini (2025-01-31) process images? ▾
o1 accepts native image input. o3-mini (2025-01-31) does not — you would need to route image-heavy workloads through o1 or add a separate vision model in front of o3-mini (2025-01-31).
Which model supports prompt caching for cost reduction? ▾
Both o1 and o3-mini (2025-01-31) 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 o1 over o3-mini (2025-01-31)? ▾
Your inputs include screenshots, diagrams, or product photos — o1 accepts image input natively, the other doesn't.
When should I choose o3-mini (2025-01-31) over o1? ▾
You're cost-sensitive at scale — o3-mini (2025-01-31) runs ~93% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
How do I A/B test o1 against o3-mini (2025-01-31) 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.