GPT-5.4 mini (xhigh) vs o3: Which OpenAI Model Should Developers Choose?

GPT-5.4 mini (xhigh) is the safer default for cost-sensitive general development, but o3 remains relevant for workloads that value its measured mathematics result and reported generation speed. The available evidence does not support a universal capability winner.

Artificial Analysis reports an Intelligence Index of 40 for GPT-5.4 mini (xhigh), compared with 30.4 for o3. The same data set reports o3 at 88.3 on its Mathematics Index, while GPT-5.4 mini (xhigh) has no corresponding mathematics value. GPT-5.4 mini (xhigh) has a Coding Index of 56.1, but no matching o3 coding value is provided.

The commercial picture is clearer. GPT-5.4 mini (xhigh) has a blended price of $1.6875 per 1M tokens, compared with $3.5 for o3. Both models show 0.3 seconds of latency in the supplied data, while only o3 has a reported median output speed, at 128.056 tokens per second.

Data provided by https://artificialanalysis.ai/

GPT-5.4 mini (xhigh) offers the stronger documented default because its measured general intelligence score is higher and its listed token prices are lower. That conclusion is narrow, not a claim that it dominates every engineering task.

Decision factor	GPT-5.4 mini (xhigh)	o3	What the evidence supports
Release date	2026-03-17	2025-04-16	GPT-5.4 mini (xhigh) is the newer entry in the supplied data
Intelligence Index	40	30.4	GPT-5.4 mini (xhigh) leads this reported measure
Coding Index	56.1	Not provided	No direct coding comparison is possible
Mathematics Index	Not provided	88.3	No direct mathematics comparison is possible
Latency	0.3 seconds	0.3 seconds	The supplied latency measure is tied
Median output speed	Not provided	128.056 tokens per second	o3 has the only reported speed value
Blended price	$1.6875	$3.5	GPT-5.4 mini (xhigh) is cheaper on this measure

OpenAI’s current model documentation lists GPT-5.4 mini as a model available through the Responses API and official Client SDKs, while the same model page does not provide the supplied material with specific context, output, parameter, or benchmark details for this model. OpenAI’s model documentation also shows a GPT-5.6 product line, but the supplied research found no explicit deprecation or shutdown notice for GPT-5.4 mini.

o3 has a weaker current-availability signal. The research did not find o3 in the current model directory, a stable alias, a confirmed endpoint, or a current listed price. The current OpenAI model directory and OpenAI pricing page therefore make o3 harder to select for a new production integration, even though its benchmark profile may fit a specialized workload.

Community evidence does not resolve the gap. Reliable Reddit, Hacker News, or X test threads were not found for either model, so coding feel, behavioral quirks, and stable real-world failure patterns remain unverified.

GPT-5.4 mini (xhigh) leads the available general intelligence measurement, while o3 has the only reported generation-speed result and the only reported mathematics result. The performance evidence is therefore split by task category.

The Intelligence Index is 40 for GPT-5.4 mini (xhigh) and 30.4 for o3. That gap supports choosing GPT-5.4 mini (xhigh) for broad assistant behavior, mixed reasoning, and general developer workflows, provided the workload resembles the index’s coverage. The data brief does not identify every task included in the index, so the score cannot establish superiority for a specific codebase, agent loop, or production prompt.

o3’s Mathematics Index is 88.3, while GPT-5.4 mini (xhigh) has no reported value. Developers building symbolic reasoning, mathematical analysis, or verification workflows should treat o3 as a serious candidate for a bake-off. The available evidence does not show whether that mathematics advantage survives on the user’s exact prompts, tool calls, response formats, or error tolerance.

The coding comparison is incomplete in the opposite direction. GPT-5.4 mini (xhigh) has a Coding Index of 56.1, but o3 has no supplied coding value. That result supports testing GPT-5.4 mini (xhigh), not declaring it the coding winner. The research also found no reliable community tests that could fill the missing comparison.

Latency is tied at 0.3 seconds in the supplied data. o3 additionally reports 128.056 median output tokens per second, but GPT-5.4 mini (xhigh) has no corresponding speed value. A developer cannot infer that o3 will always feel faster, because user-perceived completion time also depends on output length, streaming behavior, network conditions, retries, and application orchestration. Those factors were not supplied here.

GPT-5.4 mini (xhigh) is the clear price leader, but o3 can still be economically rational when its specialized result reduces retries, review time, or downstream computation. The supplied prices establish token cost, not total workload cost.

GPT-5.4 mini (xhigh) is listed at $0.75 per 1M input tokens and $4.5 per 1M output tokens. o3 is listed at $2 per 1M input tokens and $8 per 1M output tokens. The blended comparison is $1.6875 versus $3.5 per 1M tokens. These values make GPT-5.4 mini (xhigh) the better starting point for high-volume classification, routine code assistance, extraction, and mixed prompts where quality is acceptable without a specialized mathematics advantage.

Output-heavy applications deserve extra attention. GPT-5.4 mini (xhigh) has the lower output price, so verbose agent traces, generated patches, and long explanations increase o3’s cost exposure. The difference matters less if o3’s answers prevent repeated calls or human correction, but the research contains no verified failure-rate or retry-rate data for either model.

OpenAI’s pricing page lists GPT-5.4 mini with standard, Batch, Flex, and Fast mode prices, including $0.375 per 1M input tokens and $2.25 per 1M output tokens for Batch and Flex. The page does not list a current o3 price in the supplied research. OpenAI pricing documentation therefore supports GPT-5.4 mini (xhigh) for predictable procurement, while o3 requires availability and billing confirmation before deployment.

Context economics remain unresolved. The research found no context window for either model, and GPT-5.4 mini’s long-context price was not found. A large-document workflow could change the cost ranking if limits, caching, or endpoint-specific rules differ.

GPT-5.4 mini (xhigh) should be the first production candidate for most new developer integrations, while o3 should enter the shortlist for mathematics-heavy or speed-sensitive experiments. The recommendation depends on availability, task fit, and measured application outcomes.

Choose GPT-5.4 mini (xhigh) when the application needs a current documented model entry, lower token prices, and broad capability coverage. Its reported Intelligence Index is 40, its Coding Index is 56.1, and its blended price is $1.6875 per 1M tokens. Those facts form a strong default case for coding assistants, repository questions, structured generation, support automation, and general-purpose agents.

Choose o3 when mathematical reasoning is central and the application can validate access before committing to it. Its Mathematics Index is 88.3, and its reported median output speed is 128.056 tokens per second. Those signals justify a controlled evaluation for theorem-like tasks, quantitative analysis, and workflows where response speed is important. They do not prove better end-to-end performance for every reasoning task.

Run a task-specific bake-off before choosing either model for high-risk code generation. Use representative prompts, fixed tools, expected output schemas, and human review criteria. Compare correction rate, valid tool calls, test-passing patches, latency under streaming, and cost per successful task. The supplied research does not provide these application-level measurements.

The largest operational risk is o3’s unclear current status. The current official pages reviewed in the research do not confirm a stable alias, endpoint, or price for o3. OpenAI’s model page and pricing page also do not document the exact context window or limits needed for either model. Confirm those details directly in the target account before implementation.

A practical decision rule is simple: start with GPT-5.4 mini (xhigh), then add o3 only if a representative mathematics or speed test produces a material improvement that offsets its higher listed benchmark price or availability risk.

GPT-5.4 mini (xhigh) and o3 cannot be ranked completely because the supplied evidence leaves several production-critical fields unknown. The missing evidence is itself important for model selection.

The research does not establish either model’s context window, maximum output length, complete parameter set, or reliable failure modes. It also does not provide a directly comparable coding result for o3 or a directly comparable mathematics result for GPT-5.4 mini (xhigh). Community reports were not reliable enough to fill those gaps.

OpenAI’s official pages create an availability asymmetry. The model directory includes GPT-5.4 mini in the supplied research but does not list o3 in the current directory. The pricing directory lists GPT-5.4 mini prices but no current o3 price. That difference should influence procurement risk, but it is not proof that o3 is unavailable in every account or endpoint.

Data provided by https://artificialanalysis.ai/