Claude Opus 4.5 (Non-reasoning) vs o3: The Ultimate Performance & Pricing Comparison

Claude Opus 4.5 is the stronger general intelligence choice, while o3 is substantially cheaper and stronger on the available math evaluation. This comparison uses the supplied Artificial Analysis snapshot, which reports Claude Opus 4.5 at 34.7 on the Intelligence Index and 62.7 on the Math Index, compared with o3 at 30.4 and 88.3. Data provided by Artificial Analysis.

The practical decision is not simply a benchmark ranking. Claude Opus 4.5 costs $10 per 1M blended tokens, while o3 costs $3.5. o3 also has a reported median output speed of 128.056 tokens per second, while Claude Opus 4.5 has no reported output-speed value in the supplied data. Both models show 0.3 seconds of latency in the snapshot.

The evidence has an important limitation. Anthropic's current model overview does not list a complete, model-specific specification for Claude Opus 4.5, while OpenAI's current model directory does not list o3. The comparison therefore supports a model-selection judgment, but it does not establish current API availability, context limits, or production support for either model.

Claude Opus 4.5 offers the better broad intelligence score, but o3 offers the better value and the stronger math result. The supplied data gives Claude Opus 4.5 a 34.7 Intelligence Index score versus 30.4 for o3. That advantage is meaningful for applications that combine planning, interpretation, writing, and varied technical judgment.

O3 is the clearer choice for math-heavy workloads. Its Math Index score is 88.3, compared with 62.7 for Claude Opus 4.5. The available evidence does not show whether that math advantage transfers equally to software debugging, code generation, tool use, or long-form reasoning. Developers should treat the benchmark as directional rather than as a complete application scorecard.

The official documentation creates a second selection risk. Anthropic still shows Claude Opus 4.5 on its pricing page, but its model overview does not provide a complete dedicated entry. OpenAI's model directory and pricing page do not currently provide equivalent o3 details. That documentation gap is more important than a small benchmark difference for teams planning a new production integration.

O3 is the better documented speed choice in the supplied data, while Claude Opus 4.5 has the stronger broad intelligence score. The snapshot reports o3 at 128.056 median output tokens per second. Claude Opus 4.5 has no reported value, so the data cannot prove that Claude is slower. It can only show that o3 has a measurable speed result and Claude does not.

The equal 0.3-second latency figure changes how developers should interpret the speed comparison. Initial response delay appears tied in the snapshot, but generation speed can still shape the experience of long answers, code reviews, and multi-step outputs. A fast first token does not guarantee fast completion, and a high output rate does not guarantee better answers. The available data does not provide enough information to separate those effects.

Claude Opus 4.5's 34.7 Intelligence Index score gives it the stronger broad result. That advantage may matter in tasks where the model must balance several forms of judgment, such as interpreting ambiguous requirements, producing an implementation plan, and explaining tradeoffs. The benchmark does not identify which task families created that result. The supplied research also found no reliable community tests for Claude Opus 4.5 Non-reasoning, so claims about coding feel, stylistic preferences, or failure patterns remain unsupported.

O3's 88.3 Math Index score is its clearest performance advantage. Developers building symbolic reasoning, quantitative analysis, or math-oriented verification features should give o3 priority during evaluation. Developers should not assume that the same score predicts superior product behavior in ordinary application development. No verified source in the brief provides a direct comparison of code quality, tool calling, visual input, context handling, or error recovery.

The responsible conclusion is conditional: o3 has the measurable responsiveness advantage, Claude Opus 4.5 has the stronger general intelligence score, and the evidence is insufficient to declare a universal performance winner.

O3 is the cheaper model by a wide margin, but Claude Opus 4.5 can still be economically rational when answer quality reduces retries or review work. The blended price is $3.5 per 1M tokens for o3 and $10 for Claude Opus 4.5. O3 also costs $2 per 1M input tokens and $8 per 1M output tokens, versus $5 and $25 for Claude Opus 4.5.

The price gap matters most when the workload produces many output tokens. Output is priced at $25 per 1M tokens for Claude Opus 4.5, compared with $8 for o3. Long generated responses, large patches, and repeated agent actions can therefore make model choice a major operating-cost decision. The data does not include request volume, output distribution, retry rates, or review costs, so it cannot calculate a project-level bill.

Claude Opus 4.5's pricing model adds operational details that developers should validate before deployment. Anthropic documents prompt caching on its pricing page, including paid cache writes and lower-cost cache reads. The brief also reports that regional and multi-region endpoints carry an additional charge. Those options may change the economics of workloads with repeated system prompts, but the supplied data does not quantify their effect in the blended comparison.

O3 is the default cost choice for high-volume experimentation, batch-style processing, and workloads where the math result is already a strong fit. Claude Opus 4.5 deserves consideration when its broader intelligence result can reduce human correction, retries, or orchestration complexity. That claim is a decision hypothesis, not a measured finding. No source in the brief provides total-cost data linking either model to production quality or support effort.

Use the page's cost chart to compare listed rates, then test cost per accepted result. A cheaper token can become more expensive after failed calls, extra validation, or manual repair, but the brief does not reveal which model has those failure rates.

Claude Opus 4.5 is the better first candidate for broad, ambiguous engineering work, while o3 is the better first candidate for math-heavy and cost-sensitive workloads. Choose Claude Opus 4.5 when the application must interpret nuanced requirements, synthesize varied information, or produce responses where general intelligence matters more than raw token economics. Its Intelligence Index score is 34.7, higher than o3's 30.4.

Choose o3 when mathematical accuracy, lower token cost, or measurable generation speed dominates the requirements. Its Math Index score is 88.3, its blended price is $3.5 per 1M tokens, and its reported median output speed is 128.056 tokens per second. Those are concrete advantages in the supplied snapshot. They do not prove that o3 is the best model for every coding or agent workflow.

A two-stage evaluation is sensible for teams with enough capacity. Start with o3 for broad traffic if cost and math performance are central. Route ambiguous or high-consequence tasks to Claude Opus 4.5 only if application tests show fewer corrections or better accepted outputs. The brief does not provide routing data, reliability data, or verified production failure cases, so any mixture should be validated with the team's own workload.

Availability is the largest unresolved risk. Anthropic's model overview does not clearly expose a complete Claude Opus 4.5 model entry, although the model remains present on the pricing page. OpenAI's model directory and pricing documentation do not currently establish o3's direct availability or current price. Confirm endpoint access, model identifiers, limits, and retirement policy before committing architecture.

The final pick should follow workload evidence. Use Claude Opus 4.5 for general capability, o3 for math and economics, and neither model should be selected solely from undocumented assumptions about context size, multimodality, or community reputation.

O3 has the clearer measurable operating profile, but neither model has enough current official documentation in the supplied sources to remove integration risk. The questions below identify what the comparison can answer and what a developer still needs to verify.