Claude 4.5 Sonnet (Reasoning) vs o3: Which Model Should Developers Choose?

Claude 4.5 Sonnet (Reasoning) is the stronger measured general choice, while o3 is the clearer price and speed choice for developers who can accept greater availability uncertainty.

The available evidence does not support a universal winner. Claude 4.5 Sonnet (Reasoning) records an Artificial Analysis Intelligence Index of 36.4, compared with 30.4 for o3. The mathematics results are effectively close, with Claude at 88 and o3 at 88.3. Coding evidence is incomplete because the brief reports 52.1 for Claude and no comparable o3 value.

The operational picture is less settled. o3 has a reported median output speed of 128.056 tokens per second, while Claude has no reported value. Both models have a measured latency of 0.3 seconds. Claude costs $6 per 1M blended tokens, while o3 costs $3.5. Those numbers make o3 attractive for volume workloads, but the official OpenAI model directory and pricing page do not currently establish whether o3 remains directly callable.

Data provided by https://artificialanalysis.ai/

Claude 4.5 Sonnet (Reasoning) offers the stronger measured intelligence result, but o3 offers lower listed cost and reported output speed.

Decision factor	Claude 4.5 Sonnet (Reasoning)	o3
Intelligence Index	36.4	30.4
Mathematics Index	88	88.3
Coding Index	52.1	Not reported
Blended price per 1M tokens	$6	$3.5
Input price per 1M tokens	$3	$2
Output price per 1M tokens	$15	$8
Latency	0.3 seconds	0.3 seconds
Median output speed	Not reported	128.056 tokens per second

Claude has a clearer current commercial signal. Anthropic’s pricing page still lists Claude Sonnet 4.5 and does not mark it as retired. The same page also lists Claude Sonnet 4.6 and Claude Sonnet 5, so buyers should treat Claude 4.5 as an available model with a visible successor path, not necessarily the strategic endpoint. See Anthropic’s pricing documentation.

o3 has a weaker current-status signal. The supplied OpenAI model directory highlights newer GPT-5.6 models and does not list o3. The supplied OpenAI pricing documentation also does not list o3 pricing. That absence does not prove that o3 is unavailable, but it creates a deployment risk that the benchmark and price figures cannot resolve.

The most important unanswered question is whether o3’s lower cost and measured speed remain actionable in the target API environment. The research brief does not provide a confirmed stable alias, endpoint, or replacement statement for o3.

Claude 4.5 Sonnet (Reasoning) has the stronger measured intelligence score, but o3 has the only reported output-speed result.

Claude leads the Intelligence Index at 36.4 versus 30.4 for o3. For developers, that gap suggests Claude may be the safer first candidate for broad tasks that combine planning, interpretation, and multi-step reasoning. The result is still an aggregate index, so it cannot establish superiority for every prompt, language, tool workflow, or production domain.

o3 narrowly leads mathematics at 88.3 versus Claude at 88. The difference is small enough that the mathematics result should not decide a migration by itself. It indicates parity in the supplied data, not a meaningful practical separation. A team choosing primarily for mathematical reasoning should test its own representative problems because neither official brief provides a model-specific failure analysis.

Coding is harder to judge than the table may suggest. Claude has a Coding Index of 52.1, but the comparison has no o3 Coding Index. Therefore, the evidence supports a measured Claude coding result, not a Claude-versus-o3 coding victory. Developers should avoid treating the missing o3 value as zero or as evidence of weakness.

o3 reports 128.056 median output tokens per second, while Claude has no reported output-speed value. This makes o3 the only defensible choice when streaming throughput is a primary requirement. It does not prove that o3 will feel faster in an application, because user-perceived responsiveness also depends on prompt size, reasoning behavior, network conditions, and output length. The supplied data reports equal latency of 0.3 seconds, so the available latency measure does not separate them.

The official documentation leaves important capability questions open. Anthropic’s model overview describes current Claude models as supporting text and image input, text output, multilingual ability, and vision, but it does not provide a complete Claude Sonnet 4.5-specific capability entry. The OpenAI model directory does not provide the supplied o3 context, output, parameter, or multimodal details. Context limits and tool behavior therefore remain evidence gaps for both selection and architecture planning.

o3 is the cheaper listed model, yet Claude’s caching economics and o3’s uncertain current listing can change the practical cost decision.

At the supplied blended rate, o3 costs $3.5 per 1M tokens compared with $6 for Claude 4.5 Sonnet (Reasoning). o3 is also cheaper on both direct components, at $2 input and $8 output, compared with Claude’s $3 input and $15 output. This matters most for workloads with high request volume, long generated answers, or frequent retries.

The price advantage can narrow when output volume is modest and quality affects downstream work. A cheaper response that needs more validation, another model call, or manual correction can consume more of the application’s total budget. The supplied research does not measure retry rates, task success, human review time, or production error costs, so it cannot establish the cheaper model’s total cost of ownership.

Claude’s prompt caching adds another decision variable. Anthropic lists 5-minute cache writes at $3.75 per 1M tokens, 1-hour cache writes at $6 per 1M tokens, and cache hits and refreshes at $0.30 per 1M tokens. Caching can make repeated large prompts more economical, but it is not a free reuse mechanism because writes and reads have separate charges. See Anthropic’s pricing documentation.

Endpoint choice can also alter Claude’s bill. Anthropic states that regional and multi-region endpoints carry a 10% premium for Claude Sonnet 4.5 and later models, while the global endpoint is the default for the Claude API. The brief gives no equivalent confirmed o3 endpoint pricing. Teams with residency or routing requirements should therefore compare the actual deployment configuration, rather than selecting from base prices alone.

The largest cost uncertainty is availability. OpenAI’s pricing page does not list o3 in the supplied material. The $3.5 figure is useful for model comparison, but developers should verify that the price applies to the endpoint and access path they intend to ship.

Claude 4.5 Sonnet (Reasoning) is the better default for evidence-led general reasoning, while o3 is the better candidate for cost-sensitive streaming workloads with verified access.

Choose Claude when the application needs a stronger measured general intelligence result and the team values a currently visible Anthropic pricing entry. Claude’s 36.4 Intelligence Index leads o3 at 30.4, and its reported Coding Index of 52.1 provides at least some coding evidence. That recommendation remains conditional because the research does not provide comparable o3 coding data or a Claude-specific list of failure modes.

Choose o3 when output throughput and unit economics dominate the decision. Its reported 128.056 median output tokens per second is the only supplied speed result, and its $3.5 blended price is lower than Claude’s $6. This combination may suit streaming assistants, high-volume generation, or systems where response cost is more important than the measured general intelligence score. Confirm API access, model identity, and pricing before committing production architecture.

Use a staged evaluation for teams that cannot tolerate uncertainty. Start with the task set that matters most, then measure answer acceptance, retry frequency, tool-call correctness, output length, and end-to-end latency. The supplied research does not contain those application-level measurements, so a benchmark-only decision would leave major risks untested.

Do not select either model solely from the mathematics result. o3 reaches 88.3 and Claude reaches 88, which is a near tie in the supplied comparison. Do not describe Claude as the coding winner either, because the o3 coding value is missing. The evidence supports Claude for the measured intelligence result and o3 for the measured price and speed results, with no reliable conclusion for several production capabilities.

Version governance should be part of the choice. Anthropic documents dated model identifiers and convenience aliases for models before 4.6, but the supplied page does not show Claude Sonnet 4.5’s complete alias. OpenAI’s supplied directory does not list o3 or clarify its replacement status. Both teams should pin and monitor the exact model identifier used in deployment.

Claude 4.5 Sonnet (Reasoning) is easier to justify from the supplied current pricing evidence, but neither model has complete production documentation in the brief.

The unanswered questions are practical rather than cosmetic. Developers still need to verify context limits, output limits, endpoint access, stable aliases, tool behavior, and failure patterns. The official sources establish some current product visibility and pricing facts, but they do not establish every implementation detail for the compared model versions. Read the Anthropic model overview, the OpenAI model directory, and the OpenAI pricing page before finalizing an integration.