o4-mini

o4-mini is OpenAI's cost-optimized reasoning model in the o-series lineup. It uses the same extended chain-of-thought architecture as o3 but with a smaller parameter count, delivering strong reasoning capability at significantly lower cost and latency. Despite its "mini" designation, o4-mini delivers stronger results than GPT-4o on most reasoning-heavy tasks — it is not a downgrade from GPT-4o but a different capability profile optimized for reasoning tasks at efficient pricing. It supports vision inputs, function calling, streaming, and a 200K-token context window, and is the recommended starting point for teams evaluating OpenAI's reasoning models.

o4-mini is priced at $1.10 per million input tokens and $4.40 per million output tokens — significantly cheaper than o3. Prompt caching reduces repeated input to $0.275/1M. At 1,000 input + 500 output tokens per call, it costs approximately $0.0033 per 1,000 API calls — more expensive than GPT-4o ($0.0075) on a per-call basis only when output is short; for reasoning tasks that generate longer outputs, o4-mini's lower per-token rate often makes it cheaper in practice. The cost-to-capability ratio makes o4-mini the default choice for most reasoning workloads — use o3 only when o4-mini's quality is measurably insufficient on your specific task.

o4-mini's 200K-token context window matches o3, enabling large-context reasoning tasks. Latency is lower than o3 — median TTFT ranges from 2 to 20 seconds depending on reasoning effort, making it more suitable for semi-interactive applications. Throughput is approximately 60–80 tokens per second for visible output. Maximum output is 100,000 tokens. The effort parameter (low/medium/high) is available as with o3. At low effort, o4-mini approaches GPT-4o latency for simpler reasoning tasks while still applying some chain-of-thought processing. At high effort, it approaches o3 quality on many benchmarks at roughly one-quarter the cost.

o4-mini is best suited to coding tasks (it scores near o3 on competitive programming benchmarks), mathematical reasoning, structured analysis, and any workload where GPT-4o produces errors that extended reasoning would catch. It is a strong choice for code review, debugging, algorithm design, and technical writing where accuracy matters more than raw speed. Vision support makes it useful for analyzing code screenshots, mathematical diagrams, and technical figures. For customer-facing chat or high-volume simple tasks, GPT-4o or GPT-4o mini remain more appropriate — o4-mini's reasoning overhead adds latency that is unnecessary for straightforward conversational tasks.

o4-mini sits between GPT-4o and o3 in OpenAI's lineup — more capable than GPT-4o on reasoning tasks, cheaper and faster than o3. Its main competitors are extended-thinking models from Anthropic and Google at similar price points. On coding benchmarks, o4-mini and its closest competitors are closely matched; on mathematics, o4-mini has a slight edge. For teams already on OpenAI's API, o4-mini is the natural upgrade path from GPT-4o for reasoning-heavy workloads. Its intelligence score of approximately 88/100 reflects near-frontier reasoning capability — among the highest available at its price point.