Claude Opus 4.7 (Adaptive Reasoning, Max Effort) vs o3: The Ultimate Performance & Pricing Comparison

Claude Opus 4.7 is the stronger documented choice for demanding developer workflows, while o3 is the cheaper option with a reported output speed of 128.056 median output tokens per second. The available evidence does not support a complete capability comparison because the supplied OpenAI materials do not document o3’s current context window, output limit, API configuration, official benchmark results, or community failure modes.\n\nThe measurable comparison still has a clear shape. Claude Opus 4.7 reaches 53.5 on the Artificial Analysis Intelligence Index, compared with 30.4 for o3. o3 has the available math score at 88.3, while Claude Opus 4.7 has the available coding score at 73.6. Those scores do not form a matched benchmark pair, so they should not be treated as direct evidence that one model is better at every technical task.\n\nAnthropic positions Claude Opus 4.7 for complex, long-running software engineering and agent tasks, with adaptive reasoning and configurable effort. The release announcement describes instruction following, sustained execution, and output verification as central goals (Introducing Claude Opus 4.7). Data provided by https://artificialanalysis.ai/.

Claude Opus 4.7 offers the stronger documented intelligence signal, but o3 offers substantially lower token prices and the only supplied output-speed measurement.\n\n| Decision factor | Claude Opus 4.7 | o3 | What it means for selection | |---|---:|---:|---| | Artificial Analysis Intelligence Index | 53.5 | 30.4 | Claude has the stronger supplied general intelligence signal | | Artificial Analysis Coding Index | 73.6 | Not provided | Coding evidence is available for Claude only | | Artificial Analysis Math Index | Not provided | 88.3 | Math evidence is available for o3 only | | Blended price per 1M tokens | $10 | $3.5 | o3 is cheaper for broad usage | | Input price per 1M tokens | $5 | $2 | o3 lowers prompt-heavy costs | | Output price per 1M tokens | $25 | $8 | o3 lowers answer-heavy costs | | Median output speed | Not provided | 128.056 tokens per second | o3 has the available throughput measurement | | Reported latency | 0.3 seconds | 0.3 seconds | The supplied latency figures are tied | \nClaude Opus 4.7 is easier to evaluate as a production system because Anthropic documents its model identity, reasoning controls, multimodal support, pricing, and migration constraints. The official model overview identifies the API model as claude-opus-4-7 and documents a 1M token context window and a synchronous maximum output of 128k tokens (Models overview).\n\no3 has a weaker evidence position in the supplied material. OpenAI’s current model directory does not list o3, and the provided source does not establish whether it remains directly callable, whether it has a stable alias, or which version formally replaces it (OpenAI Models). That documentation gap is itself a deployment risk, even though it does not prove that o3 is unavailable.

Claude Opus 4.7 has the stronger documented general intelligence score, but the available benchmark coverage cannot establish a universal coding or reasoning winner.\n\nThe Artificial Analysis Intelligence Index is 53.5 for Claude Opus 4.7 and 30.4 for o3. That gap supports choosing Claude for workflows that combine planning, tool use, ambiguous requirements, and multi-step verification. It does not prove that every individual task will improve by the same amount. Benchmark composition and workload fit still matter.\n\nThe coding comparison is incomplete. Claude Opus 4.7 has an Artificial Analysis Coding Index of 73.6, but no corresponding o3 coding value appears in the supplied data. o3 has a math index of 88.3, but Claude has no corresponding math value. A team choosing between software engineering and mathematical reasoning should run a matched internal test rather than infer cross-index superiority.\n\no3 has the only supplied output-speed result, at 128.056 median output tokens per second. That may matter for interactive interfaces, streaming assistants, and workloads where users wait for long answers. Claude’s missing speed value prevents a direct throughput comparison. Reported latency is 0.3 seconds for each model, so the available data does not show a latency advantage.\n\nAnthropic’s own evidence emphasizes complex engineering and agent tasks, including CursorBench at 70%, while its release material also documents adaptive thinking and effort controls (Introducing Claude Opus 4.7). Those controls can improve difficult-task persistence, but higher effort can increase reasoning and output consumption. Anthropic documents that max_tokens covers both thinking and the final response, and that Opus 4.7 uses adaptive thinking rather than traditional manual extended-thinking configuration (Migration guide).

o3 is the clear price winner on nominal rates, but Claude Opus 4.7 can justify its higher cost when fewer failed runs and less developer intervention matter.\n\nThe blended price is $3.5 per 1M tokens for o3 and $10 for Claude Opus 4.7. o3 also costs $2 per 1M input tokens and $8 per 1M output tokens, compared with Claude’s $5 input and $25 output rates. This price structure favors o3 for high-volume classification, short transformations, repeated extraction, and workloads where a fast response is more valuable than extended reasoning.\n\nNominal price is not the whole bill. Claude Opus 4.7 uses a newer tokenizer, and Anthropic says the same text usually produces about 30% more tokens. The actual increase depends on content and workload shape (Pricing). Long source files, repository context, and generated code can therefore widen the effective cost gap beyond the displayed rates.\n\nClaude’s economics improve when prompt caching or batch processing matches the workload. Anthropic lists cache-hit and refresh pricing at $0.50 per MTok, while Batch API pricing is $2.50 per MTok for input and $12.50 per MTok for output (Pricing). These options require a workload with reusable prompts or asynchronous execution. They do not automatically make Claude cheaper for ordinary interactive calls.\n\nThe cheaper model can also become more expensive operationally if it needs retries, larger prompts, manual review, or downstream correction. The supplied materials do not provide matched error rates, task-success rates, or total cost per completed developer task. Teams should measure successful task cost, not token cost alone.

Claude Opus 4.7 is the better default for high-stakes engineering agents, while o3 is the better default for cost-sensitive and speed-sensitive workloads.\n\nChoose Claude Opus 4.7 when the task requires sustained execution across multiple steps, careful interpretation of requirements, document-heavy context, visual input, or explicit effort control. Anthropic documents text and image input, multilingual capability, adaptive thinking, client-side tools, server-side tools, PDF support, Files API, Prompt Caching, and Batch API support (Models overview, Migration guide). Its documented 1M token context capacity is useful for large repositories and document sets, but community discussion warns that retrieval quality can decline as context grows. The discussion cites 59.2% for 128k–256k context tests and 32.2% for 524k–1024k tests, based on interpretation of Anthropic’s model-card data rather than an independent reproduction (Hacker News discussion).\n\nChoose o3 when marginal token cost, streaming throughput, or mathematical reasoning is the primary constraint. Its $3.5 blended price and 128.056 reported median output tokens per second make it attractive for high-volume services and interactive applications. The supplied material does not establish its current API availability, context limit, coding profile, or production restrictions, so procurement should verify access through the current OpenAI documentation and an authenticated test. OpenAI’s pricing page does not list o3 in the supplied evidence (OpenAI API Pricing).\n\nDo not select Claude solely because its intelligence score is higher, or o3 solely because its price is lower. Run matched tests for repository changes, tool-call reliability, long-document retrieval, math-heavy tasks, refusal behavior, and total successful task cost. Community reports about Claude Opus 4.7 conflict: some describe verbose planning and unstable collaboration, while others report stable coding and document work (Reddit discussion). Neither discussion provides controlled, reproducible measurements.

Claude Opus 4.7 is the safer documented choice for complex engineering agents, but o3 may be preferable when cost and measured streaming speed dominate the decision. The evidence does not support a definitive answer for every developer workload.\n\nThe most important unresolved issue is o3’s current production status. The supplied OpenAI model directory does not list o3, while the supplied pricing page does not provide o3 pricing. The data brief still contains Artificial Analysis pricing and speed values, so those figures are useful for comparison, but teams should verify live access and billing before committing.